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Excel for Data Analytics - Full Course for Beginners — Transcript

by Luke Barousse · 127,250 words · 17,114 segments · language en · Watch on YouTube

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0:00dat nerds welcome to this full course
0:01tutorial on Excel for data analytics
0:04this is the course I wish I would have
0:06had when I first started as a data
0:08analyst you're going to be working right
0:09alongside me as we Master how to use a
0:11spreadsheet starting with the basics of
0:13functions charts and tables working our
0:15way up to our first portfolio project
0:17we'll then shift gears into advanced
0:19features like pivot tables power query
0:21and power pivot ultimately building our
0:22second and final project analyzing real
0:25world data now to master this tool we're
0:27not going to go straight for 11 hours
0:29instead we're going to break it down
0:30into 10 to 20 minute lessons during this
0:33we'll have exercises for you to learn
0:35while doing not just watching followed
0:37by practice problems to reinforce your
0:39newly learned skills now Excel is the
0:41most popular spreadsheet tool in the
0:43world it's estimated to have over 1
0:45billion users that's one in eight people
0:48in the world and for data nerds it's one
0:50of the most popular skills for data
0:52analysts coming only behind SQL oh and
0:55the same can be said for business
0:56analysts in this Tool's popularity truth
0:58be told Excel was one of the only skills
1:01that I knew when I landed my first role
1:03in data analytics but it was able to
1:06handle everything thrown at me and so
1:08I've been cataloging over the years all
1:11of the most important features to
1:13perform data analytics and I compiled it
1:15in this course and this video is for
1:17absolute beginners you don't need any
1:19analytic or spreadsheet experience we'll
1:22be starting with the first half on the
1:23basic chapters which will build up your
1:25knowledge on the fundamentals with
1:27covering which versions of excel you can
1:29use for the course along with installing
1:31it then we'll get you familiar with
1:32working around how to manipulate a
1:34spreadsheet from there we'll shift into
1:36practical exercises analyzing data using
1:38formulas and functions and then
1:40visualizing it using common charts and
1:41statistical analysis at the end of the
1:43basics chapters we'll put your skills to
1:45the test to build an interactive
1:47dashboard to predict one salary based on
1:49job and location for the second half of
1:52the course we're going to ramp up our
1:53learnings diving into Advanced
1:54Analytical features focusing on using
1:56pivot tables and add-ins to dive quickly
1:59into Data in sites we'll learn power
2:01query to connect to a variety of data
2:03sets and perform ETL or extract
2:05transform and load finally we'll learn
2:07data modeling with power pivot and
2:09perform Advanced calculations with the
2:10Dax Language by the end of the advanced
2:13chapters we'll have built a full data
2:15analytics project analyzing the data
2:17science job market which you'll be able
2:19to share this and the previous project
2:21in order to Showcase your experience
2:23with analyzing data in Excel now I'm a
2:25big believer in open- sourcing education
2:28so this course and all the content
2:30required to complete the course is
2:31completely free I not only get you set
2:33up with Excel but I also provide all the
2:36different Excel workbooks and sheets
2:38needed to complete this course with this
2:40you'll get access to the data sets
2:41needed to make those final projects and
2:43even how to share them now unfortunately
2:46the AdSense Revenue alone from this
2:48course isn't enough in order to support
2:51all the different costs associated with
2:53building this so I have an option for
2:55those that want to support and help out
2:57for those that purchase my supporter
2:59resources you're you're going to get
3:00access to a lot of features that are
3:02going to help speed up your learning all
3:03provided through this custom dashboard
3:05to track your progress you'll get guided
3:07practice problems to perform after each
3:08lesson that will not only provide the
3:10solution but also walk you through how
3:12to get it if you get stuck along the way
3:14you'll have access to a community of
3:16others in order to jump in and comment
3:18and ask for help additionally you'll be
3:20getting my step-by-step instructions
3:22that walk through each of the lessons as
3:24I perform it and finally when you
3:25complete the course I'll email you a
3:27certificate of completion that you can
3:28upload to LinkedIn now one quick shout
3:31out before we jump in and that's to
3:32Kelly Adams she helped me plan out a lot
3:34of the different lessons for this course
3:36along with being the brains behind a lot
3:39of the different practice problems and
3:41frankly if I didn't have help I probably
3:42couldn't have completed this course so
3:44before we go any further with what we
3:46need to and actually diving into this
3:48course we need to First understand what
3:50is Excel and where the heck it came from
3:54so in order to understand this we need
3:56to go back oh a little too far back
4:00ah just right ancient Babylon when we
4:03used to trade livestock like it was
4:04crypto now it's during this time that we
4:06started recordkeeping and we didn't have
4:09paper so we used Stone and we partition
4:11it into rows and columns during the time
4:13of the Romans they began to perfect this
4:15even further with accounting eventually
4:17we get some advancements in technology
4:18we start getting this on paper this is
4:20when the term spreadsheets gets the
4:23introduction this maintained that
4:24familiar row and column format in order
4:27to catalog different things spread AC
4:29across different sheets spread sheet
4:33fast forward to the 1900s and we pack
4:36rooms full of underpaid people in order
4:38to maintain and keep track of all the
4:40different transactions on paper
4:42spreadsheets with the Advent of
4:43computers in the late '70s we started to
4:45see our first spreadsheet softwares vial
4:48and Lotus 123 then our boy here decided
4:51to revolutionize the world little
4:54bit okay not with that but with this I'm
4:58Bill Gates chairman of my
5:00Microsoft in this video you're going to
5:03see the future since its launch in 1985
5:06it's been wreaking havoc in the
5:08spreadsheet software Community
5:09dominating market share and to continue
5:12to dominate over the years Microsoft has
5:13added more and more features it
5:16initially started out to where you'd
5:17only be using it for the cells of
5:19entering different formulas and forming
5:20quick calculations along with getting
5:22different charts and Analysis shortly
5:24thereafter it was upgraded with pivot
5:26tables and that's my secret weapon to
5:28quickly analyzing data as I no longer
5:30have to remember which comes first and
5:32index and match now VBA or Visual Basic
5:35for applications was included in the
5:37mid90s and it's a programming language
5:39in order for you to automate task in
5:40Microsoft applications now we're not
5:42going to waste any time in this course
5:44learning VBA frankly I feel it's
5:46outdated you should learn python instead
5:48and there's newer tools that actually
5:50automate the process of data analysis
5:53like powerquery this was first
5:54introduced as 2010 and then rebranded to
5:57get and transform and then rebranded
5:59again to power query sort of similar to
6:02what Google does with renaming products
6:03anyway this bad boy is like washing down
6:06a couple caffeine pills with a shot of
6:08espresso it can ingest and clean so much
6:10data in the blink of an eye hardcore
6:12data nerds call this ETL or extract
6:15transform and load power pivot was also
6:18introduced during this time of power
6:20query and it's like putting your
6:22spreadsheets on steroids this allows us
6:24to perform data modeling on data sets
6:27greater than a million rows greater than
6:29what Excel actually holding the
6:30spreadsheets and combined with the power
6:32of Dax or data analysis Expressions we
6:34can supercharge our calculations fast
6:37forward to today and there's been two
6:38other major features added to excel
6:41co-pilot which is basically chat GPT
6:43inside of Microsoft Excel and python
6:46Excel which is basically python inside
6:48of excel anyway co-pilot is great wait
6:51that's a lie so I do believe AI chat
6:53rots are great at helping us out when we
6:55get stuck but I don't want you rely on
6:57that to actually learn this technology
6:59of Excel and for Python and Excel you
7:01need to know well python if you don't
7:04know this yet it's completely useless
7:06now with all these features it can make
7:08it seem like Excel is overwhelming which
7:10I completely get that but when you focus
7:13on the basics and work from there I
7:15think it makes a lot easier to learn it
7:17it's also why this course is almost 11
7:19hours long all right enough with the
7:21history lesson let's actually get into
7:22the course material and what you're
7:24going to need for this also we're going
7:25to be going over what data set or what
7:27data we're going to be analyzing for the
7:29project for this with the link provided
7:30below you can navigate to this which is
7:33the GitHub repo that has all the
7:35different folders and files needed to
7:37take the course now don't understand if
7:38you're not familiar with GitHub we're
7:39going to walk through this this pane
7:41here basically outlines all the
7:42different folders that you have access
7:44to and if I navigate in something like
7:45resources I can see I have a data sets
7:47folder images folder and even a problems
7:49folder so for those that purchase the
7:51course practice problems you have access
7:53to the problems inside of here and
7:55they're broken down by chapter along
7:56with the lesson in addition to that
7:58resources folder you can see numbered
7:59here we have each of those eight
8:01chapters and if we navigate into
8:03something like spreadsheets intro we
8:04have a workbook for each one of the
8:06lessons so you want to download this
8:07file you just navigate to it click the
8:09three dots and click download but have
8:11an alternate method coming up in a bit
8:13inside the workbooks I provide a blank
8:15template for you to go through and
8:16actually fill in and we'll be getting to
8:18what's in this final sheet of actually
8:20being filled in now as we move into the
8:22advanced chapters they're going to have
8:24something like the data sheet or you're
8:25going to use the data from the data
8:27sheets in order to do different
8:28operations and we'll put those in
8:30different sheets as well so how do we
8:32get these files well the easiest way is
8:33to come up here to this code and go to
8:35download zip with the file downloaded
8:37all you need is to unzip it and then
8:39from there it has all the different
8:41folders with the appropriate workbooks
8:43inside of them now after going through a
8:44lesson I then have practice problems for
8:47those that purchase the course perks to
8:48go through here's the course dashboard
8:50that you'll get access to that breaks it
8:52all down for the problems based on the
8:54chapter itself and then by the lesson
8:57and inside of each of these lessons is
8:59multip multiple different problems for
9:00you go through and work the other perk
9:02that you'll receive with those practice
9:03problems are the course notes these
9:05break down the concepts in a similar
9:07format of all the different chapters and
9:09lesson here's the one on Excel install
9:12which is going to be what we're covering
9:13next but it provides all the different
9:15background on all the different material
9:16that be covering this and it's in the
9:18same format that I'm covering it in the
9:21video so you can follow right along just
9:23as a reminder there's no requirement to
9:25purchase these practice problems or
9:26course notes just helps support me
9:28anyway what are we actually going to be
9:30covering in this data analysis that
9:32we're going to be doing inside of excel
9:33well you're going to be taking the role
9:35of a job Seeker in exploring what are
9:37some of the top paying roles along with
9:39skills of data nerds for this we're
9:42going to use the data from my app dat
9:44nerd. Tech that is collected to this
9:46point up to 3 million jobs it tells
9:49based on a job title and also on a
9:52location what are the top skills and it
9:54not only tells us the salary of these
9:55skills for a particular job but also the
9:57salaries of the jobs themselves now the
9:59main data set we're going to be using
10:01for the majority of this course is this
10:03one here inside the data sets folder of
10:05data job salary all this data set
10:07includes over 30,000 job postings from
10:102023 and it includes a wealth of
10:12information such as company name salary
10:14and location as we go through these
10:16examples I'm going to be doing it from
10:17the perspective of a data analyst which
10:19is their top job in the data set but as
10:22shown here there's a lot of different
10:23other job titles that you can check out
10:25and use as well so feel free to deviate
10:27additionally I'll be primarily focusing
10:29on the United States but there's a lot
10:31of different countries in there as well
10:33so feel free to plug in your home
10:35country and analyze this instead now
10:37with any course you're probably going to
10:38get stuck along the way and so how do
10:40you get help for this well I don't
10:42recommend just jumping into the comment
10:44section and waiting for somebody to help
10:45you out instead I recommend using a chat
10:48bot like chat GPT in it you can provide
10:50whatever era you're seeing and it will
10:52help you out and guide you along the way
10:54on what to do and there's other great
10:55options as well such as gemini or even
10:57Claude so feel free to use whichever one
10:59you're most comfortable with all right
11:00if you haven't done so already it's your
11:02turn now to go in and download that
11:04GitHub repo with all the different
11:05workbooks needed for this course in the
11:08next lesson we're going to be getting
11:10into installing Excel and mainly
11:12understanding what are the different
11:13versions that you can actually get with
11:15Excel and which one you need for the
11:17course with it I'll see you
11:21there let's now actually get into
11:23working with Excel so in this lesson
11:27we're going to be going through how to
11:28actually inst install Excel onto your
11:30computer assuming you don't have it but
11:33before we get to that for those that
11:35maybe have Excel or an older version of
11:37Excel or have different computers we're
11:40going to actually go through what are
11:41the preliminary requirements you need to
11:43have or set up in order to be able to
11:45have the Excel you need for this
11:49course now here's a breakdown of the
11:52different chapters within this course
11:55that is the rows here and then for the
11:57columns are the different micro Micosoft
11:59products that you can get in order to
12:02have Excel now if you're running Excel
12:05on a Windows machine either through
12:07Microsoft 365 Microsoft Office at home
12:09and student or even an older version of
12:13excel up to about
12:152010 you're going to be fine with
12:17completing all the different course
12:19content however if you have the Mac
12:22version or Mac operating system and
12:24Excel is installed directly on that
12:26operating system you're not going to be
12:27able to complete the Advanced chapter
12:30specifically on power query and on power
12:32pivot along with the project and it's
12:35similar as well for Microsoft 365 online
12:39as you won't also be able to complete
12:40the Advanced Data analysis section now
12:43if you have any of these first three
12:45versions of excel installed on your
12:47computer you can skip to the next lesson
12:50if you want I'm just be going through
12:52before the install process of breaking
12:55down each of these different versions so
12:57you understand your options what you can
13:02get so let's get into breaking down all
13:05these different versions available first
13:07up is Microsoft
13:09365 now with Microsoft 365 you're going
13:12to get a host of different Microsoft
13:14applications not only Excel but also
13:16things like word PowerPoint and even
13:18Outlook and there's two major plans I'm
13:20going to recommend for this either the
13:22family plan which allows you to give out
13:25these keys for these different services
13:26to up to six people or a personal plan
13:29which allows you to give it to well
13:30yourself now I do want to call out that
13:32if you're a college student or maybe you
13:34work for a big Corporation you may have
13:37access to a free Microsoft 365 plan so
13:41if you're in college check with your
13:42college and if you're working for a
13:43business check for your business if you
13:44have access to this so you don't have to
13:46pay money for it but regardless of that
13:48if money is an issue Microsoft 365
13:51family offers this free one-month trial
13:54which I think you can complete this
13:56course within a month so technically you
13:58could do this for free if you don't want
14:00to get charged you will need to actually
14:02cancel before the end of that 30 days
14:04and at that point you'll still have
14:06Microsoft Excel installed on your
14:08computer just everything will be in view
14:10only mode you won't actually be able to
14:12edit any of the different spreadsheets
14:14that we've operated on during this
14:16course let's now move into Microsoft
14:18Office home and
14:22student now this bad boy is the
14:24alternate recommendation I'm going to
14:26give you if you don't want to pay for a
14:28Microsoft 3 365 subscription this is
14:31only a onetime purchase and it gives you
14:34keys to Microsoft Office so you can
14:36install all the different Microsoft
14:38products of excel word and PowerPoint
14:41onto your computer for the low low price
14:43of $150 similar to Microsoft 365
14:47subscription this will not only work on
14:49a Windows machine but it will also work
14:51on a Mac machine Let's now move to this
14:54last option because it's sort of in the
14:55bundle of it of Microsoft 365 online
15:01now this version of Microsoft 365 is
15:04completely free but sort of a catch to
15:07this here I am on my web browser logged
15:10into Microsoft 365 online and I have
15:13access to all the different apps within
15:16the browser including something like
15:18Excel so we can go to it now this
15:20version looks very similar to the
15:22version that you can actually install
15:23the applications on your Windows or Mac
15:25machine there are limitations like a
15:28disuss before about power query and
15:30power pivot so you're going to be
15:32limited if you're trying to follow along
15:34in this course when we get to those
15:35Advanced chapters also the layout on the
15:38web browser version of this app is much
15:40different from that that's installing
15:42your computer so I'm not going to be
15:43providing any support on this course on
15:45actually actually how to navigate this
15:47so you're going to have to figure that
15:48out yourself so we've discussed
15:49everything except for these Mac versions
15:52of Microsoft 365 and office so here's a
15:55quick recap of all the different
15:58features and cost of the three major
16:01versions of Microsoft that you can get
16:03in order to get Excel on your computer
16:05for this personally I'm using the
16:08Microsoft 365 family plan because it
16:10includes all the different features that
16:13I need and it also I save cost because
16:15I'm splitting with my brother who now
16:17that I think of it is actually paying
16:18for it but it provides everything that I
16:21need and so it's the one I'm
16:22recommending for this
16:26course now before we get into the
16:27install I want to briefly show what are
16:30the differences between using Mac with
16:33Excel installed Vice windows and Excel
16:35installed on it anyway here's Excel
16:37installed on my Windows operating system
16:41and Excel on this operating system is in
16:43my opinion the flagship product from
16:47Microsoft so they're investing all of
16:49their effort and resources into
16:51designing this application to make it
16:54the best possible and then from there
16:56Excel online and then Excel for Mac are
16:59really just copycats of this anyway the
17:02two main differences and the problems
17:03I've run into in the past that Excel for
17:07Mac doesn't have are in this data tab I
17:10have a lot of different data sources I
17:12can choose from and that's specifically
17:14related to our power query lesson and
17:16then finally it has power pivot which is
17:20just completely non-existent on Excel
17:22for Mac now here I am on a Mac machine
17:25and we can see that it looks very
17:27similar to before but there's a lot of
17:30limitations that we're going to find
17:31with this specifically going back to
17:33that power query not a lot of different
17:34sources you can choose from and then
17:36yeah Power pivot is just completely
17:38non-existent you may be like Luke I have
17:41a Mac machine what do I need to do in
17:43order to have the most premier version
17:45of Excel and use for this well for that
17:48I recommend installing a virtual machine
17:51and virtual machines like parallels
17:53shown here allows you to host a
17:57different operating system on your Mac
18:00machine this Windows example that I was
18:02showing earlier if I actually expanded
18:04out you can see in the background here
18:07I'm running this on a Mac machine and I
18:11have full capabilities en able to carry
18:14out and running Windows on this now I've
18:16been paying for and using parallels over
18:18the past 3 years and I can tell you the
18:20support and the offers from it are
18:22perfectly fine and I love using it now
18:24personally I'm using the Parallels
18:27Desktop Pro Edition but you can get by
18:29with just using the standard edition now
18:31they also have this onetime purchase
18:33that you could do which is 129 but it
18:35doesn't get any further updates and I
18:37really like how it actually updates and
18:40fixes any bugs that may run into now the
18:42other reason why I like parallels is
18:44because it has this coherence mode I
18:46have this blue little icon that I can
18:49click up at the top to go into coherence
18:51mode and then wait for it it allows me
18:54to access any of those windows inside of
18:57my windows vers virtual machine inside
19:00of Mac so here is Excel running right
19:02here inside my Mac and this is not only
19:04limited to Microsoft Excel but also
19:06products like powerbi which I'm using
19:08pretty frequently as a data analyst I
19:10can also run this into coherence mode
19:12but enough about
19:14that now that they got that out of the
19:16way let's actually get into installing
19:19Excel via in your Windows machine or on
19:21your Windows Virtual Machine so the
19:23first thing we need to do is navigate
19:24over to
19:26microsoft.com and I'm going to click up
19:27here to Microsoft 3 365 we're going to
19:30be going through setting up the free
19:3230-day version so I'm going to click
19:33this of try for free and from there
19:36start my one month trial it's going to
19:38ask me to sync my data I'm assume you
19:39don't have it I'm also going to assume
19:41you don't have an account so we're going
19:42to create one I'm going to put in my
19:44email
19:45address and then from there create a
19:47password after providing some personal
19:49information you're going to need to
19:50verify your email with the code they
19:51send you now to be clear this is the
19:54Microsoft 365 family plan which after
19:57that 1 month trial it's going to be
19:59charging you at
20:00$99 every year so if you're just one
20:03person and you're trying to switch to
20:05the personal plane after this you'll
20:06need to do that at the end or near the
20:08end of those 30 days from there like any
20:11company they're going to ask for some
20:12payment methods I'm going to just go
20:13ahead with PayPal PayPal's all set go
20:16ahead and do more paperwork of adding
20:18Bell and address and with that I can
20:20start trial and pay later so now that
20:22I'm logged in I want to install the
20:24desktop app so it gives me access to
20:26right here it's going to go ahead and
20:27begin this it's going to ask if want to
20:29allow this app to make changes to your
20:30device yeah I trust them so only took a
20:33few minutes and all the different
20:34Microsoft 365 office apps were installed
20:37so I just come down to the search bar
20:39down here type in Excel let's pop it
20:42open make sure it's working and in order
20:44to get started you need to sign in in
20:46order to verify that it's your
20:48subscription so I put in my email and
20:51password and already forgot my
20:53password now I'm resetting my password
20:56and now I'm all set up all right and we
20:58got agre to some lawyer talk of
21:00accepting licensing agreements at this
21:02point I'm pretty worn out of going
21:03through this process so I'm just going
21:05to click through everything I'm not
21:06going to send any optional data
21:08personally I don't like to do that I
21:10don't want to personalize right now and
21:13it looks like I'm finally done all right
21:15I'm into it and now that we're into
21:17Excel we can see up here it should have
21:18your name or your account that you're
21:20going into and go in here into the blank
21:22workbook all right so that basically
21:24concludes this lesson on installing
21:25Excel I do want to show real quick how
21:27easy it is to actually cancel your
21:29membership should you want to go about
21:31just getting the free version or the
21:33free 30-day trial and you want to cancel
21:34it before any if I go back to my account
21:37I can go in here to manage
21:39subscriptions and here I'm inside my
21:41Microsoft account which tells me I'm
21:43subscribed to Microsoft 365 family I can
21:45share it with up to zero to five people
21:49and for that I just click on it and I
21:50can copy a link and provide it to
21:52whoever I want to share it with we're
21:54going to cancel it so we can go to
21:55manage subscriptions right here and all
21:57we got to do is click cancel
21:59subscriptions it's going to have me
22:01confirm that I do want to cancel this
22:03family plan makes me scroll all the way
22:05to the bottom after showing me all these
22:06different prices that I could get
22:08instead and I'm going to say yeah I
22:10don't want my subscription and as I'm
22:13filming this on August 27th it basically
22:16says hey you still have access this for
22:1830 days until September 26th so still
22:21technically have access to it so if you
22:23haven't done it already it's your turn
22:24to now go and install Microsoft Excel
22:26the one of the options that I've shown
22:28here in the next chapter we're going to
22:30get into a spreadsheets intro to get you
22:32familiar with how to actually use all
22:35the different functionality or graphical
22:37unit or interface gooey of excel with
22:39that see you in the next
22:44one welcome to this chapter on an intro
22:47to spreadsheets and this chapter has
22:50three different lessons in order to
22:53understand what we're covering those
22:54three different lessons we need to
22:55explore some vocabulary with it so let's
22:57jump into Excel for this lesson we're
22:59going to be focusing on worksheets and
23:02that is basically as you can see this
23:03tab here called sheet one that is how to
23:07manipulate these different cells within
23:10this worksheet or also known as a sheet
23:13in the next lesson we're going to be
23:14going into workbooks so workbooks
23:17basically captures either one sheet like
23:19this one sheet one if I add another one
23:20sheet two so it encapsulates multiple
23:23different sheets within this program of
23:25Excel and then finally in the third
23:27lesson of this chapter we're going to be
23:28moving into the ribbon which is up here
23:30at the top and has a bunch of different
23:32functionality to extend into those
23:34spreadsheets along with using this file
23:37tab up here that has a whole bunch of
23:39features within it as well now this
23:41chapter was designed for those that may
23:43not have experience with using Microsoft
23:46Excel before so if you don't fall in
23:48that category as in you've used excel in
23:51your job and you're pretty familiar with
23:53all those different features I just
23:54shown you can feel free to skip this
23:56chapter and then move into the next one
23:59on functions along with all those
24:00different practice problems but if
24:02you're not comfortable with that stick
24:03around we're going to get into
24:06it all right so the first thing you need
24:08to do is open up that first Excel sheet
24:11in the files you should have downloaded
24:12from GitHub on onecore worksheets inside
24:16of here I have an original sheet that
24:18allows you to actually go in and fill in
24:20everything we're going to be doing and
24:21manipulating during the course of this
24:23lesson then if you get lost along the
24:25way or want to peek ahead to see what
24:26we're actually going to do you can
24:28actually scroll over here or select the
24:29final sheet to see that now I want to
24:32make this as big as possible for you to
24:33see so I'm going to go ahead and close
24:35out this ribbon up here and you can just
24:36do that by double clicking on any one of
24:38these different items up here and then
24:40from there I also want to zoom in so I'm
24:41going to come down here to the bottom
24:43right and I'm going to just zoom in to
24:45about 200% and scroll on over now inside
24:48the spreadsheet it has all these
24:50different cells and it's organized in a
24:52manner where it has rows and the rows
24:55are labeled with numbers 1 2 3 all the
24:58way down to about a million and then we
25:00have the columns and the columns are
25:03alphabetical and they all go all the way
25:05to where they start duplicating where
25:07they'll put another letter in front of
25:08the other and it'll go all the way
25:09through xfd so let's practice some data
25:12entry here I have a table we're going to
25:13be filling in for this lesson basically
25:15has all the different skills associated
25:18with it and then I want you to actually
25:20go through while we're going through
25:21this and you don't have to provide the
25:23values I do you can if you want we're
25:25going to be filling it in based on our
25:27difficulty when we made have started it
25:28or level and then filling out some other
25:30self formulas as we go so we're going to
25:32start first with Excel and then the
25:35difficulty so I'm going to select right
25:36here and I can see which cell is
25:38selected because it's sort of
25:40highlighted here on this B and also two
25:42but also right up here next to this
25:45formula bar I just call that formula bar
25:48we can see that we're calling out the
25:50name of B2 so anytime we reference any
25:54cells it first references the column
25:56letter and then the row number so in
25:59this case I'm selected in C7 so I'm
26:01going to go ahead and give this a number
26:03I'm going to say four for myself as you
26:05notice I I just put it right in the Box
26:07alternatively I can also select the cell
26:10I want to go to and then come up here
26:11into the formula bar press what I want
26:14so I want five for Python and go from
26:16there whenever I press enter it then
26:18goes down to the next cell so
26:20technically I could just go through and
26:21enter this all in using my keyboard and
26:24I don't have to click or move manipulate
26:26at all except to select the cell that
26:28wanted so those were all numerical
26:30values when we move into the skill known
26:33on whether we know it or not we want to
26:35put in whether it's known or not we want
26:36to put true or false this is known as a
26:38Boolean value so typing in something
26:41like true I can see when I press enter
26:44it actually updates to be all caps for
26:47this Tru so it recognizes the data type
26:49of this as Boolean now if you're taking
26:51this course you probably don't know
26:52Excel so we're going to put in false
26:54instead now say I want to update the
26:56rest of these for false false I can yeah
26:59go through and actually type it up or I
27:01can select this lower right hand corner
27:04of cell C2 and now I can drag these
27:07values down and it will autofill it in
27:11now autofills not just limited to
27:14Boolean values let's say I had something
27:16like Luke I could put that here and just
27:19drag it down it's going to fill in Luke
27:21all the way through here a cool feature
27:23about Excel is say I have something like
27:25one and then two I could select both of
27:28these cells and then when I drag it down
27:30it's going to actually fill in three or
27:32four now autofill can also throw you off
27:35especially for dates so let's say we're
27:37filling in when we're starting Excel
27:39which is we'll put in for the today's
27:40date in my case it's August 27
27:4420124 I'm going to go ahead press enter
27:47to save that in it automatically updates
27:48to this formatting here in America if in
27:51Europe you may see the month in a
27:52different location anyway if I select
27:55this and actually drag down what you'll
27:57see is is it will do that auto fill in
28:00but it's not going to keep that same day
28:02per it assumes we want to increment by
28:04one day now specifically with dates if I
28:07want to change the format I can actually
28:09come up here and I'll expand out this
28:11home ribbon again and right now it's Rec
28:14recognizing that the number is of date
28:18and for date I have a few different
28:19options I can do short date which is
28:20shown here or even something like long
28:22date I can also go even further which
28:25we'll explore as we get further into
28:27this course into this more number
28:28formats and date actually has a whole
28:30bunch of other different options that we
28:32can choose from but for right now we're
28:34just going to keep it this simple date
28:35format and I'm going to click okay now
28:38assuming you haven't started any of
28:39these I'm going to go ahead and actually
28:40just select all the different cells that
28:42I want and if you were to press delete
28:45it's only going to delete that top cell
28:47and that's sort of annoying because I
28:49want to delete all these different cells
28:52instead what I'm going to do if I'm on a
28:53Windows machine I'm G to press delete or
28:55in my case I'm using a Mac Windows VM
28:58I'm press function delete and it's going
29:00to delete all the different content
29:01right I'm also going to go ahead while
29:03I'm here delete all that different
29:04content down there we don't need it now
29:05we're going to move on to level type of
29:07diet we're going to put into this is
29:08text so in the case of excel you're
29:10probably a beginner so I'll put in
29:12beginner and then if I want to I can go
29:14through and fill out different levels
29:15for each of these so python Advanced RBI
29:18Advanced and so on for all these now one
29:21thing to notice real quick is for the
29:23date it does specify in here under this
29:26home ribbon that it is a date but all
29:28these other one it just characterizes as
29:30general which is perfectly fine now for
29:33these other options down here let's go
29:35ahead and say I wanted to put in
29:36beginner for all the rest of these can't
29:38necessarily drag and drop this but what
29:39I can do is I can actually copy it
29:41specifically I could right click the
29:42cell and come up here and copy it but I
29:44don't recommend that also over here on
29:46the home menu they have an option as
29:48well to copy or even cut something so I
29:51can select something like copy as well
29:53and it's going to put these marching
29:55ants as they call it around the cell to
29:57tell you that hey it's actually selected
30:00and then if I wanted to paste it I go
30:02ahead and select down here and I could
30:04paste it down below that's not what we
30:05want to do I don't like going through
30:06and actually selecting all these
30:07different buttons I want to minimize it
30:09as much as possible and I want to use
30:11shortcuts so in order to stop these
30:13marching ants I can go ahead and press
30:15escape and I'll select the cell that I
30:17want to copy and from there I'll press
30:20contrl C and that copies it and then I
30:23can go ahead and paste it below by
30:25selecting the cell that I want and
30:27pressing control contr V now you'll be
30:29noticing that when I'm going through
30:31this I have these shortcuts peing right
30:33here next to me on the screen so you'll
30:34be able to follow along as well as I'm
30:36using these shortcuts the other option
30:38is I could cut this so I could press crl
30:41X and then paste it in here crl V but
30:45this is going to go ahead and take this
30:46value out of here we don't want to
30:47necessarily do that so I'll just copy
30:49this again crl C and then paste it right
30:51above here contrl V shortcuts are going
30:54to be a big timesaver and we're going to
30:56be using them a lot throughout this
30:58course in order to save you time and
30:59having you to go back to your mouse in
31:01order to manipulate it and select the
31:03different
31:05cells all right so let's step this up a
31:07notch and we're now going to get into
31:09using formulas and formulas are denoted
31:13by whenever we go into a cell like
31:15difficulty here which we want it to be
31:17on a 1 to 10 scale we denote formulas by
31:21an equal sign and in this case we want
31:24the difficulty to be on a 10-point scale
31:26basically transition from that 5 point
31:28scale so we need to multiply it times
31:29two so we could do something like 4 * 2
31:34and I press enter and it's going to give
31:36me as I expect eight but I actually
31:38don't recommend hardcoding values that
31:41are already inside of excel here
31:44specifically this four so instead of
31:46this I'm going to remove this and I can
31:50either type in the cell coordinates of
31:52the cell so I could type in
31:54B2 and as you notice it's highlighting
31:57one the B2 is blue but then the cell B2
32:01is highlighted in blue alternatively I
32:03can have an equal sign here and just go
32:05over and actually select it as well
32:07whenever I press enter it's going to go
32:09ahead and say a it's four now now that
32:12I'm referencing that four I want to say
32:14that this is 4 * 2 pressing enter we
32:20have 8 once again we're going to use
32:22that power of autofill so I can select
32:24that cell of F2 and now drag it down and
32:28what's going to be pretty interesting
32:30about this is the two as denoted in the
32:33formula bar and actually whenever I
32:35click into it as well the two Remains
32:37the Same but autofill automatically
32:40knows to adjust the formula or the cell
32:44coordinates for the next cell Down based
32:47on how I did that autofill just to show
32:50this as well I could say hey let's equal
32:52this to B6 right below it and then if I
32:55were to drag this over it's going going
32:58to then put in C6 D6 E6 then F6 so
33:02pretty cool I'm going go ahead and
33:03delete this now the last column we're
33:05going to be filling in is skill and
33:07level we're also be using a formula for
33:09this and we'll set this equal to this
33:11skill thing and also this level so I'll
33:15start by putting in an equal sign and
33:17then it's not on the screen right now
33:19but I know it's in b or sorry A2 and I
33:22can see that selected by scrolling over
33:25here now how am I going to get in that
33:28F2 well I can do an Amper sand now and
33:32from there I'll put in F2 and it has
33:35this selected as well pressing enter
33:37ended up in the wrong one sorry about
33:38that should have been E2 and now I have
33:41Excel beginner but there's no space in
33:43between there this is sort of hard to
33:45read so what I can do is actually
33:47manipulate this to include another Amper
33:50sand and then in between this I'm going
33:53to put quotes and this is hey insert
33:55this text character in between it
33:57specifically I want to have a space then
33:59a dash and then another space and then
34:02press enter now if I tried to do this
34:05without the quote if I just did this and
34:08press enter I'm going to get a typo in
34:11my formula you have to actually put
34:13those quotes around to show that it's
34:14text and it's trying to correct it for
34:17some minus sign I don't really like how
34:19it's doing it oh my gosh it's freaking
34:21out now anyway I put the quotes back in
34:23there pressing enter boom we have it and
34:26like before I'm going to just do
34:28autofill to fill all those
34:31in so let's zoom out a little bit cuz
34:34we're going to be now be working with
34:36ranges which is a collection of cells
34:39now if you notice whenever I select in
34:42this case I'm selecting B2 it says B2 up
34:44the top but if I go to select more of
34:46this it will actually call out that five
34:49r or five rows by two c or two columns
34:52and then when I Let Go it just goes back
34:54to B2 anyway ranges are a selection of
34:57multiple of cells so if I come over here
35:00to i1 put it in equal sign and then if I
35:02want to say copy this entire range I can
35:06go ahead and select this all so it's
35:09saying it's A1 colon G6 so start the
35:13upper left hand corner of A1 and the
35:14bottom right hand corner of G6 now this
35:17is pretty cool there's a new feature of
35:19excel of dynamic rages it's going to go
35:21ahead and fill this in there's only one
35:24formula in here of that A1 through j6
35:26but you see that has this Shadow border
35:29around here that's showing that this
35:31dynamic range is now filling in for all
35:34these different things and if we look at
35:35the formula bar it's sort of gray out
35:37here too for it only at the very
35:40beginning does it show that A1 and G6
35:43and then you could manipulate it so if I
35:44wanted to I could change it to G5 and it
35:46would just go down a row now we're not
35:48limited to just that we could in fact
35:51select an entire column so in this case
35:53I'll put an equal sign and let's say I
35:56want to do the the full column of column
35:59a right here I can select up here a it's
36:02going to select all the way down and if
36:05we go over to the formula bar itself we
36:07can see that it's saying a colon a that
36:10means all the contents of column A are
36:12going to be included in this and from
36:14there it's putting a copy putting all
36:16these different things and then when
36:17there's not a value in it because it's a
36:19copy similar to over here for these
36:21dates of zero we're going to see Zero in
36:24all these different values all the way
36:25down now similarly I can also do a copy
36:28of a row so in this case if I wanted to
36:30or multiple rows if I wanted to do rows
36:33five and six I could press enter going
36:35to get an erir with this though and that
36:37has to do with this Q column right here
36:40that we're copy and pasting here so I'm
36:41going to go ahead and delete that real
36:43quick get rid of it and now we have that
36:47rows five and six duplicated below along
36:50with that shadow around it and all there
36:53now these ranges are going to save us a
36:55lot of time later so I'm going to go
36:56ahead and delete this right now I don't
36:57want any of that as later on when we get
36:59into actually using functions within
37:02formulas I can use something like the
37:04average function put in a range in here
37:08so it selects all of it and then get the
37:10average of it in this case now one last
37:13thing to note on this before we wrap up
37:16here on how to save this is you may have
37:18noticed that this date started over here
37:20is a number and that's because that's
37:24how Excel stores dates with within this
37:28spreadsheet right here so if I actually
37:29click on it go back up to home right now
37:32it's St storing it under the format of
37:35General right now so if I were to make
37:38this into an actual date we can see that
37:40it is in fact 827 2024 now just some fun
37:44little trivia if I were to put in number
37:46one and transition it to a date so
37:51coming up here and selecting date that
37:54first date starts at January 1st 19900
37:58and then they move on the numbers from
38:00there all right last thing we need to do
38:02is now save the work that you just
38:04completed with this you can do this
38:06multiple different ways we can come up
38:07here to the top of your Excel workbook
38:11right here and click save you can also
38:13as shown you can use contrs
38:15alternatively you can come over here to
38:17the file menu and then come on down to
38:20save or save as and then if you wanted
38:23to you can specify the location where
38:26you actually want to save your file and
38:28save it there now you do have the option
38:31which I highly recommend if you're
38:32working with real world files you want
38:35to actually save them to save this
38:37autosave feature the one caveat to this
38:41is that your files have to be stored on
38:44one drive right now with the plan that I
38:46have I can store about one terabyte of
38:48files on there so if you'd like to do
38:50that feel free to transition your files
38:52there I'm not going to um and I won't
38:54have Auto saave on for this but for very
38:56important files definitely do have
38:58autosave set up all right for those that
39:00have purchased the practice problems and
39:02notes you have some practice problems to
39:05go through and get even more familiar
39:06with manipulating cells inside of a
39:08spreadsheet after that we're going to be
39:10going into manipulating a workbook with
39:12that see you in the next
39:17one all right we're going to be
39:18continuing on with this spreadsheets
39:20intro focusing now on workbooks so
39:24previously we were focusing on
39:25worksheets which are a sheet inside of a
39:28workbook now we're going to be focusing
39:29on manipulating and moving data between
39:36workbooks now for this I don't want you
39:38immediately jumping into that 2or
39:41workbooks Excel file this really just
39:43has all the answers in it it doesn't
39:45have really what we need for it instead
39:47we're going to be starting with a new
39:49notebook and instead importing in some
39:52data so specifically if we go into this
39:54folder of zore resource
39:58into data sets we have this one Excel
40:01file called Data job salary monthly now
40:06this is similar to the data that we're
40:08going to be using for the remainder of
40:09the course we're actually going to use
40:10another Excel sheet but this one here is
40:12pretty neat because it's broken up by
40:14months into different sheets so all the
40:16job postings for January are in this
40:18sheet called Jan and so on for February
40:22and so on for March so what we're going
40:24to be doing in this lesson is moving we
40:26want to just evaluate the January data
40:28move that into a new workbook so to get
40:31a new workbook as easy as possible we're
40:33going to come over here to the file menu
40:35I'm just going go to new and click blank
40:38workbook now here I have that new Bo
40:40notebook right now it's titled book two
40:42because it hasn't been saved anyway
40:44going back to that file menu just to
40:45show you I have different options I can
40:48get a new notebook so we went into new
40:50and just selected uh a blank workbook
40:53also we could use this Home tab and
40:56select a bank blank workbook based on
40:58that also have a bunch of different
40:59tutorials you can check out also we have
41:01this open tab right here which allows
41:04you on the left hand side to select a
41:06location like this PC or even browse
41:09different locations in your file system
41:11but frankly I'm using more often than
41:13not over here on the right hand side
41:16this right here where this shows a past
41:18history of Excel files I've worked with
41:19so I can go through and actually select
41:21an Excel file pretty easily we're going
41:23to explore more about this file menu
41:25more in a bit let's get moving some data
41:28first now before we get into copying
41:30this data into the new workbook itself I
41:34want to actually just copy it within its
41:38own workbook so if we noce some controls
41:40down here at the bottom we have all the
41:42different Sheets if we want to add
41:43another sheet which I want to copy it to
41:45I'm just going to add this in right here
41:47and I'm going to call this Jan copy
41:50press enter and that's new sheet and I
41:52and I added that by just double clicking
41:54in there and then allowing it to addit
41:56addition I can rightclick it and I can
41:59do things like rename it and that will
42:01do the same thing now there's also some
42:03controls around here you notice there's
42:05some arrows on right here and what that
42:08does is just Scrolls all the way over or
42:11incrementally over so I can see all the
42:13different sheets in this case there's
42:14more sheets than I'd actually see in one
42:17view then we have the scroll bar over on
42:19the right hand side this is actually
42:20just controlling the scroll area within
42:22our new sheet of Jan copy so previously
42:26we we saw how we can copy ranges using a
42:30formula in this case I'm entering equal
42:31to and then I'm just going to select
42:33this range right here press enter and I
42:37can get it inserted in and then actually
42:39looking at the formula it's just equal
42:40to
42:41J1 uh colon p8 and this has its range
42:45right there all right so I want to get
42:47the contents into this sheet so I'm
42:49going to start by putting an equal sign
42:51and then I'm I go over to that Jan sheet
42:54and when I go over here you're going to
42:55notice that now next to that equal sign
42:58I have Jan the name of the sheet and an
43:00exclamation point this is identifying
43:02the sheet and I want all this different
43:04items so as I go to select it all you
43:08can see that it's updating in the
43:09formula bar right now I have A1 through
43:11P2 selected but I actually want to
43:13select everything in this sheet and
43:16we're about at 3,000 rows and right now
43:20I'm only about 500 of those this is
43:22going to take forever so I don't
43:23recommend necessarily doing this type of
43:26method to try to select all your data so
43:28I'm going to go ahead and Escape out of
43:30this and go back to where we were at the
43:33Gen copy instead once again I'm going to
43:35press that equal sign go back to that
43:37Jan sheet right up in the form bar once
43:40again I can see that it has the Jan and
43:41the exclamation point I'm going to
43:43select A1 to start with and I'm going to
43:46press the shortcut contrl shift and then
43:49the right arrow key and now all the top
43:52row is selected from here I'm going to
43:55continue to hold control shift and press
43:57control shift down and it's going to
44:00select all the different arrows so as we
44:03can see up here A1 to P 3103 scrolling
44:06down we don't have any more data now all
44:09I have to do is press enter and I did
44:11this to basically show the nomenclature
44:14now so now we're not only selecting a
44:16range but we're also selecting a range
44:18from a different sheet and this is how
44:21Excel does the nclat or the formula
44:23necessary to make this work and once
44:25again this is a dynamic range appearing
44:28inside of here but we really want to put
44:31it inside of here into this new workbook
44:35so what I'm going to do is I'm going to
44:37actually delete this sheet right here
44:39because we don't need this copy sheet in
44:40here I don't want to actually manipulate
44:42my data at all going to right click it
44:43and select delete it's going to prompt
44:46me any time that hey you're going to
44:48permanently delete a sheet do you want
44:49to continue yeah I want to continue now
44:52once again I'm going to go back to that
44:55original blank sheet that we have I want
44:57to put it into here so I'm actually
44:58going to name this one Jan and then
45:02we'll call this one formula CU
45:03technically it was a formula not a copy
45:04I don't know why I did copy before
45:06anyway back into A1 once again I'll
45:08press that equal sign and then going
45:10back to that other workbook I will
45:13select it the first cell in there which
45:16is actually A1 and now we can see we
45:19have in the formul of the bar which is
45:21actually the front of the bar which is
45:23sort of strange in the other sheet that
45:25our other workbook that we work with we
45:27have inside of brackets the Excel file
45:30name the sheet that we're in and then
45:33the actual uh cell range of A1 we have
45:37dollar signs around this this locks the
45:39references of it which we're going to go
45:41into more detail on but the main thing
45:42to understand is this has A1 selector
45:44right now but we want to select all this
45:45data so that shortcut of control shift
45:48right select all the different columns
45:50and then control shift down okay it's
45:53all selected I'm going to go ahead and
45:54press enter and it's going to take me
45:55back to my original workbook that I was
45:58trying to work with this now that was
46:00using formulas to copy this data we're
46:03going to explore two more options the
46:05second one is going to be somewhat
46:07familiar using copy and paste so I'm
46:09going to create this new sheet I'm going
46:10to call it Jan copy and
46:13paste from here I'm going to go back to
46:15our original data that we have and since
46:18we're at the bottom of the sheet I'm
46:19just going to select the bottom right
46:21hand corner press control shift left now
46:24if you noticed it went and stopped
46:26stopped at this Blank cell right here
46:29which isn't a big deal I'll press it one
46:31more time it'll go to the next cell over
46:33that actually has a value in it and then
46:35once again it's going to go all the way
46:38to the end of a
46:403103 so basically if there's any blanks
46:42while you're trying to do this it's
46:43going to stop at those values there okay
46:46and then from there I'm going to press
46:47control shift up and as we're saying
46:50it's going to stop at every different
46:51Blank cell along the way this is going
46:53to take forever unfortunately I don't
46:55recommend you actually do that ever
46:56again
46:57instead start up at the top left and do
46:59the control shift over to the right and
47:02then all the way down in order to select
47:04all the cells now like we did before we
47:06want to copy it I could either use this
47:07up at the top in the home ribbon right
47:10here I could actually select copy or the
47:13shortcut which I'm going to recommend of
47:15contrl c and from there going back into
47:17our new workbook selecting cell A1 and
47:21then using contrl V and pasting all this
47:24data in now moving on to the third
47:25example which is is actually the one I
47:28recommend you do anytime you need to
47:29move sheets of data basically in both of
47:32those previous approaches you could go
47:34about missing getting data to move over
47:37so I don't really recommend doing that
47:39instead I would come down here to the
47:41Jan sheet write click it and select move
47:45or copy so we have this new window that
47:47pops up and it has two book right now it
47:50has this Excel sheet selected of data
47:52job salary monthly we don't want to move
47:54to that we want to move to book two we
47:57also move to a new book but book two is
47:59open that's what we've been working in
48:00that's what we're going move to okay we
48:02can see we have the different sheets
48:04that we've already made in there and it
48:06says in this dialogue this is where you
48:08want to put this before this sheet and
48:11we want at the end so we'll select move
48:13to end now we don't want to take this
48:17sheet Jan out of here we just want a
48:19copy of it so we're going to select this
48:21create a copy and then click okay now JN
48:24has moved over here but I do want to
48:26actually differentiate this so I'm going
48:28to
48:29put mover
48:33copy now in the next lesson we're going
48:35to be exploring more about the ribbon
48:36but we're going to be exploring now more
48:38about the file menu or also known as
48:42backstage view we've gone through this
48:44home new and open we also have this here
48:47for share this is available for well if
48:50you're sharing it via one drive this
48:52makes it super easy to share with your
48:54co-workers we're not going to go into a
48:56lot of detail but this is a great option
48:58if you're working in one drive and you
48:59want to actually collaborate with other
49:01co-workers you can work on Excel files
49:03at the same time moving down to the list
49:05here we also have get add-ins and we're
49:08going to be actually looking at
49:10different addins we can use in the
49:13advanced chapters whenever we get to
49:15that so we working with some addins with
49:17that next up is info which has over here
49:19on the right hand side some key metadata
49:22about our Excel file itself then if we
49:25want get into actually protecting our
49:27workbook which we're going to cover in a
49:29few chapters down the road you can get
49:31into actually doing that the only other
49:33thing that I find myself doing from time
49:34to time in this section is on version
49:36history once again this requires you to
49:37be using one drive for it but you could
49:40go back and revert back into a previous
49:42version that you work with so it's great
49:44for that now moving into save or even
49:48save as since we haven't saved yes
49:50they're both the same right here I'm
49:51going to go ahead and save this but I
49:53don't want to save this on one drive
49:54personal I'm just going to shave this on
49:56my desktop so I'll come and select
49:58desktop and then I'll name this two
50:01workbooks and save it now Beyond save as
50:05we also have things like print which I
50:07really don't find myself doing that too
50:09often should be sending an electronic
50:11version export if I wanted a pdf version
50:14of something and then finally close as
50:16well same thing as this x up here just a
50:18x out of it and there's two more areas
50:20down here that I want to call out and
50:22that's a count and that allows you to
50:24actually see behind the scenes of what
50:26going on with your Microsoft account and
50:29this is generic to all the different
50:31Microsoft products that you have so not
50:34just Microsoft Excel as you can see from
50:36my information I'm actually inside the
50:38Microsoft 365 Insider program so I get a
50:41lot of access to Insider features get to
50:45experiment with new stuff before any
50:47other people do anyway this is where you
50:48want to come anytime you want to make
50:50sure that you have your Microsoft
50:52products up to dat I have automatic
50:55updates available so even I'm I check to
50:57update now it's going to tell me hey I'm
50:59up to date the other thing to note on
51:00this is the different office themes that
51:02you have on this I'm actually going to
51:03change this right now to use system
51:06settings which on my Mac I use dark
51:09theme so it's going to go to that last
51:11two options are hting down here behind
51:12more I have feedback so if I wanted to
51:15give feedback to this product i'
51:17probably go to something like X or
51:18Twitter instead and then finally options
51:21we'll be getting to options later on in
51:23this but this allows a very much more
51:27advanced features that we can actually
51:28go in and customize using this menu
51:31especially whenever we get into add-ins
51:33we're going to be doing that from here
51:35all right so now you become an expert at
51:37how to manipulate different spreadsheets
51:38or sheets along with manipulating them
51:42between different workbooks in the next
51:45lesson we're going to be going into this
51:47ribbon up here and actually exploring
51:50everything a little bit further and
51:51getting a sneak peek into each one of
51:53these for those that purchase the
51:55practice problems and course notes you
51:57have some practice problems to go
51:58through now and experiment working with
52:01different workbooks with that see you in
52:03the next one where we get into the
52:04ribbon see you
52:08there all right this final lesson of the
52:11spreadsheets intro we're going to be
52:13getting into the ribbon inside of Excel
52:17and better understanding what are all
52:19the different tabs and what are the
52:20capabilities by doing some simple
52:22exercises for this we're going to
52:24continue to be analyzing that January
52:26data set that we worked from the last
52:27Lon and we're going to actually get into
52:29actually performing some data analysis
52:31with it so for this lesson you can open
52:34and use that ribbon menu Excel file
52:37which I have right here and all the data
52:40that we're going to be working with are
52:41that January data is in this data tab
52:43along with all the examples and all the
52:45different tabs but I don't need this I'm
52:47not going to work with this so I'm going
52:48to close this out instead I'm going to
52:50be working off where we left from last
52:52time in that two workbooks where we
52:55actually moved over that January data
52:57set now quick disclaimer for any of
52:59these files that you're opening up if
53:01you're noticing the security warning of
53:03automatic updates of links have been
53:05disabled can go ahead and just enable
53:08the content and then click right here on
53:10do not ask me again for network files
53:13and select yes cuz I want to make it a
53:15trusted document now if you're getting
53:17any of these areas that the file has
53:19been moved renamed or deleted cuz mainly
53:21you have it in a different location of
53:23what I had it here's actually the
53:25address of the file that I'm using I
53:28open it up anyway this is the actual
53:30address of where the file is anyway you
53:32can come down here and select these
53:33three dots on the file in question and
53:35just select change Source go into browse
53:40and then from there inside the actual
53:42file itself select where this is so in
53:45this case it's looking for that data set
53:46file with the data job salary monthly
53:48I'm going to select it select okay and
53:50then it's prompting me now that this
53:52link workbook hasn't been refreshed want
53:54to and go ahead and refresh it and it's
53:55going to update it all right close out
53:57of this now anyway that was all s silly
53:59because I'm going to go ahead and delete
54:02this formula one right here and also
54:04this copy and paste tab right here we
54:07only want to keep the Mover copy which
54:09is the actual sheet that we moved over
54:12that has all the data for this lesson
54:15okay I'm just going to rename the sheet
54:19data so let's dive into this Home tab
54:21and this thing has a lot to do with
54:24formatting the text and how things
54:27appear within the spreadsheet for
54:29example I can select all these top rows
54:32right here so basically A1 all the way
54:34to P1 I can change this font size to
54:37something like 12 for the fill color or
54:39the background color I can change it to
54:41something like a light gray right now it
54:43looks like it's already bold I could
54:45turn it off or turn it back on
54:46inspecting all these different columns I
54:48can see that some of it is hidden
54:50especially here this date column I can
54:52see inside of here this is the actual
54:54value but whenever we actually look look
54:56at it from afar like it it has these
54:58Amper sand signs so double clicking on
55:01the edge of that H column right here it
55:04actually expands out and moves it where
55:05it needs to go you can actually do this
55:07for all the column by just selecting all
55:09of them and then double clicking that
55:12last one and then that expands it all
55:14the way we can see that that last column
55:16is well super long so it has all the
55:17different skills typically these titles
55:19up the top I'd like to maintain centered
55:22so that way I know that it's a title but
55:23I could move it to either side also so I
55:26can move it up or down if I wanted to
55:28but we'll leave it right there in the
55:29center as well getting into the number
55:31formatting itself I can actually go and
55:34select something like job post to date
55:35it's going to select that whole column
55:37if I wanted to I can turn this into a
55:39date so in our case I want to do a short
55:42date now other columns I would want to
55:44format are these salary year average and
55:48also salary hour average so besides just
55:52clicking here I can also just select
55:54that hey I want to use this as an
55:56accounting number format and it's going
55:58to automatically put these decimal
55:59places at the end two decimal places
56:01since we're in the 100 thousands I don't
56:03really care about so I'm actually going
56:04to remove them by saying decrease
56:06decimal I'm going to do that twice now
56:08for something like salary hour average
56:10I'm going to also convert this to a
56:12currency but for these these may have
56:14two decimal places of values included in
56:17it so I'm going to leave it now so for
56:19the Styles and cells portion we're going
56:21to be getting into this more especially
56:23into conditional formatting in the
56:25spreadsheets Advanced chapter and
56:27chapter 4 so we'll save that for then
56:29the next thing I want to do is get into
56:31this editing and this is a pretty
56:33powerful feature we can actually sort
56:36and filter our data if we wanted to so
56:39what I'm going to do is actually select
56:41all these cells from P1 all the way to
56:44A1 and then come in here inside of
56:47editing select sord and filter and apply
56:50this filter so let's actually get into
56:52filtering this data specifically I'm
56:54wanting to investigate
56:57jobs or data analyst jobs in the United
56:59States and specifically full-time jobs
57:02we're going to be looking at the salary
57:03data for this so I want to filter it
57:05down for it so I'm going to select here
57:08I'm going to unclick select all and
57:10select data analyst and now it's going
57:12to filter for all the different data
57:14analyst roles there nothing else that's
57:15not there additionally that job schedule
57:18type I want to be looking at full-time
57:20roles only I don't want to include any
57:22other ones so I'll select fulltime I
57:24want the country I don't want to be
57:26skewed by any other countries I live in
57:28the United States so I'm going to then
57:30select United States and then finally I
57:33only want to look at the salary or the
57:36yearly salary data so I can actually
57:39come over here to the salary rate and
57:40select here I only want to look at the
57:43year data okay so now this has
57:45everything in it that I want we're going
57:46to get to analyzing and visualizing this
57:48in a second before that I want to talk
57:50about two other features addins which we
57:52talked about before on how you access to
57:53the file menu you can get to addin via
57:56this and finally analyze data which in
58:01my opinion isn't that strong of a
58:03feature this tab uses a little bit of
58:06artificial intelligence behind the
58:08scenes for you to investigate so it'll
58:10actually provide you different
58:11visualizations that you could actually
58:13visualize out of your data and or even
58:16you can go as far as asking a question
58:19about maybe you want to see hey the
58:20distribution of salary rate or something
58:22like that all you have to do is come
58:24down here and then insert in the chart
58:26that you want to insert in I'm going to
58:28close out of this now we can see that
58:30we've made this salary distribution um
58:33that we maybe want to visualize overall
58:35though I find that this analyzed data is
58:37pretty hit or miss so I'm not using it
58:40very
58:43often now the insert tab is where I
58:45spend the second most of my time after
58:47the Home tab they conveniently put in
58:48the correct order there's three major
58:50use cases that I'm using out of this in
58:53chapter 4 on the advanced use of spread
58:56sheets we're going to be going into
58:57tables and then in chapter five we're
59:00going to be going into pivot tables but
59:03even closer to that in chapter 3 we're
59:05going to be going all into depth on how
59:07to use these charts but let's get a
59:09sneak peek into this specifically
59:11remember we filtered this table down to
59:13data analyst jobs in the United States
59:16and specifically full-time roles we want
59:18to visualize this salary year average
59:22column so with column M selected I come
59:25up here to recommend charts and it's
59:27going to give me a visualization of some
59:29well recommended charts now there's only
59:30four here I can also select this other
59:32tab up here on all chart and actually
59:35try to see hey what would this look like
59:37maybe in a pie chart or a bar chart
59:40anyway I want this in a histogram which
59:41we're going to go into more detail on
59:42how to read this later what all have to
59:44do is just come in here double click it
59:46it'll insert it in now notice how
59:49whenever this was created we now have
59:52new tabs appear inside of here
59:55specifically with this selected we have
59:56this chart design and format tab if I
59:58select off of it those tabs disappear
1:00:01and select it again they reappear this
1:00:04tab allows me to dive in and actually
1:00:06further customize these visualizations
1:00:08to how I want them to appear I can even
1:00:11move them to let's say a new sheet and I
1:00:14can title this something like histogram
1:00:16and then move it the charge Stone always
1:00:19necessarily appear just like that let's
1:00:21actually do a deeper analysis to see
1:00:23what are the different job title short
1:00:25columns available I want to clear all
1:00:27these different filters on here so I'm
1:00:29going to come back up here with this one
1:00:31row selected come into editing sort in
1:00:33filter and I'm going to say hey clear
1:00:35all the different filters now selecting
1:00:38column A going into insert and into
1:00:40recommended charts it's recommended this
1:00:43clustered bar chart which is actually
1:00:45what I want to view so double clicking
1:00:47on this this provides me a breakdown of
1:00:50all the different counts of the
1:00:52different job titles within our our data
1:00:56set and we can see things like data
1:00:58scientist engineer and analyst are some
1:01:00of the highest amount of job postings in
1:01:03this data set now unlike our histogram
1:01:05example this actually provides this data
1:01:08in a pivot table which we're going to be
1:01:10going into in the pivot table chapter
1:01:12which allows me to further manipulate
1:01:14the data so say I want to actually sort
1:01:16this I could rightclick the values right
1:01:18here and clict hey sort smallest to
1:01:20largest and then closing out this pivot
1:01:23table tab right here I can actually see
1:01:26what is the highest amount of job
1:01:28compared to the lowest which is cloud
1:01:32engineer now there's remaining tabs
1:01:34we're going to be going and hopefully
1:01:35rapid fire in order to cover these as I
1:01:37find I'm using these less frequently
1:01:39than these other tabs that we previously
1:01:41talked about the draw tab allows you to
1:01:43well draw on your spreadsheet so I can
1:01:46just write on it if I wanted to but I
1:01:48don't really find myself doing that
1:01:49except for maybe being I'm building
1:01:50dashboards besides that use case is
1:01:52pretty rare if I want to end do this
1:01:54drawing right here I can come up here
1:01:56and click undo or I can select contrl Z
1:01:59and it'll remove it page layout tab is
1:02:02great if you're having to print out any
1:02:04data for those co-workers that are
1:02:05living in the past and don't know how to
1:02:07accept things digitally you can do
1:02:09everything from adjusting your page
1:02:10layout to adjusting the scale that
1:02:12you're actually viewing things now
1:02:14personally I find myself more using
1:02:16these sheet options right here so if I
1:02:18go to this job count tab right here if I
1:02:20wanted to I could turn off the grid
1:02:24lines on here as you can can see it got
1:02:26white on the background I really like
1:02:28that now if I wanted to make sure they
1:02:30had actual grid lines around my table I
1:02:32come back to the Home tab and for here I
1:02:35can select borders and from there I want
1:02:37to put all borders on there so now I
1:02:39look like I have this table right here
1:02:41along with my graph super fancy next up
1:02:43is formulas this is where you need to go
1:02:45if you can't remember a function that
1:02:47maybe you want to use if it's a text
1:02:49function you come in here select
1:02:50something like text you can scroll
1:02:52through and actually see even a
1:02:54description of of the different
1:02:56functions that are available so in this
1:02:58case replace it tells you hey replace
1:03:00this part of a text string with a
1:03:01different text string depending on what
1:03:03version of excel you have and the newer
1:03:04ones you'll have this insert python to
1:03:07insert python functions and then finally
1:03:09they have more advanced features with
1:03:12maintaining and updating and formatting
1:03:14your different formulas and functions
1:03:16which we'll be diving to in the next
1:03:17chapter now besides the home and insert
1:03:20tab the data tab is the next tab that I
1:03:24find myself using all the time in
1:03:26chapter 7 we'll be diving into Power
1:03:29query and we're going to be focusing
1:03:30heavily on this getting transform data
1:03:32and also queries and connections and
1:03:35then in chapter 8 when we get to power
1:03:37pivot we're going to be going into
1:03:39managing our data model with power pivot
1:03:42in chapter 4 we're going to be going
1:03:43into this forecasting and we're also
1:03:45going to be adding in some extra add-ins
1:03:47that are going to appear in this data
1:03:48tab now I sort of skipped over the data
1:03:50types and sort and filter because we've
1:03:51saw them on the Home tab they're just
1:03:54conveniently located here in bigger
1:03:56format for you use also all right this
1:03:58tab on review is probably the least
1:04:00likely for me to actually use I can
1:04:02actually go through and check things
1:04:03like spelling and add comments or even
1:04:05protect my sheet besides that I'm not
1:04:08finding I'm using that this often view
1:04:10tab is similar to the review Tab and
1:04:12that I'm using it a little bit more you
1:04:14can change the format of how you
1:04:15actually want to view things but mainly
1:04:18I'm finding myself using this the most
1:04:20of freeze pains let's say you see I'm
1:04:22scrolling down here and I don't know
1:04:24what the job or what the he headers are
1:04:26right here so going over to this data
1:04:28tab I can actually come in here to
1:04:29freeze panes and select freeze top row
1:04:32or even freeze First Column so in this
1:04:35case that top row actually stays up
1:04:36there and I really like it like that now
1:04:38let's say I want to freeze both the top
1:04:40row and that First Column there's not
1:04:41really a selection for that so here's
1:04:43what you can do you can come over here
1:04:44to freeze panes and select unfreeze
1:04:46paines and then select something like a
1:04:48cell like B2 that means I want
1:04:50everything above this and to the left of
1:04:52it to freeze so now when I select freeze
1:04:54panes this upper or top row is actually
1:04:57Frozen and then the actual First Column
1:05:00is Frozen as well all right final tab is
1:05:02help and I'll be honest I think this is
1:05:05pretty useless if I get stuck with
1:05:06anything along the way I'm finding
1:05:08myself navigating to something like chat
1:05:10GPT and it's helping me a lot quicker
1:05:13than trying to navigate through this
1:05:14help box that it provides and I'm
1:05:16already getting an error message with
1:05:18even accessing it so you can see how
1:05:19often I even use it
1:05:22then now we've been doing a lot of
1:05:24manual clicking with using the ribbon
1:05:27and I think a good resource that goes
1:05:29with this is shortcuts so if you come
1:05:31inside of the resources folder we have a
1:05:34Excel file here called Excel shortcuts
1:05:37and what this has in it is a list of all
1:05:39the different shortcuts that I find
1:05:41myself using anytime I'm inside of excel
1:05:44so it's worth having all of these I'm
1:05:46not going to lie committed to memory it
1:05:48looks like a long list but I'm telling
1:05:49you by the end of this you're going to
1:05:51have all of these basically committed to
1:05:52memory they're going to be timesaver now
1:05:55although I shed on people that print out
1:05:57stuff this would be something that I do
1:05:59recommend actually printing out and
1:06:01having next to you so that way you can
1:06:02reference really quickly while going
1:06:04through this course all right now I know
1:06:06we move fast through that but we're
1:06:08really going to be diving into as I
1:06:09called out during this lesson all of
1:06:12these different tabs even more as we
1:06:14advance through all the different
1:06:15chapters that was more of a sneak peek
1:06:16into what you're going to be exposed to
1:06:19coming up in this course all right for
1:06:21those that purchas the practice problems
1:06:22you have some problems to go through and
1:06:24actually experiment more with with the
1:06:26tabs in the next chapter we're going to
1:06:27be jumping into functions and also more
1:06:30specifically formulas order to build
1:06:32them out and form data analysis on that
1:06:34data science job posting data set with
1:06:36that I'll see you in the next
1:06:40one all right welcome to this chapter on
1:06:43formulas and functions in this lesson
1:06:46we're going to be focusing specifically
1:06:48on going a deep dive and understanding
1:06:51formulas then in all the follow on
1:06:54lessons this we're going to spend the
1:06:55majority of our time working on
1:06:57functions for that we'll be exploring
1:07:00the entire function Library focusing on
1:07:02the key functions within this library
1:07:05that I find that I'm using time and time
1:07:08again in data analytics so what are we
1:07:10going to be doing in this lesson well
1:07:11we're going to be focusing on a
1:07:13fictitious data set we're going to keep
1:07:15it small in order for us to get more
1:07:17familiar with operating with formulas
1:07:19and operating on this data set
1:07:21specifically by the end of this we're
1:07:23going to be able to input into into this
1:07:26worksheet a number of years of
1:07:27experience or total salary and be able
1:07:30to see whether these jobs meet those
1:07:33conditions specifically me that I meet
1:07:35both of those conditions so for this you
1:07:37can follow along by opening that
1:07:39formulas intro workbook in this workbook
1:07:41will be staying in this data sheet right
1:07:43here all the different answers when we
1:07:45get to the math operators comparison
1:07:47operators or cell referencing are shown
1:07:49via that sheet but we'll just be
1:07:51sticking for data for
1:07:54now first as math operators and as shown
1:07:57by this table here you can use a variety
1:08:00of different symbols for to conduct
1:08:02different multiplication subtraction
1:08:04division operations that you want to do
1:08:06so let's dive into testing some of these
1:08:07out we're going to be filling in each of
1:08:09these columns that correlate with the
1:08:11associated job title as we go through
1:08:13this so the first one's going to be
1:08:15experience pretty simple right we talked
1:08:16about before in order to reference
1:08:18another cell we would use an equal sign
1:08:21and then from there we can either type
1:08:22or select a cell I'm going to recommend
1:08:25just typing it to make it go faster C3
1:08:27it's highlighted blue because that's the
1:08:29cell that's highlighted then we'll be
1:08:30using the autofill feature of this to
1:08:33fill in all the cells below and we
1:08:35notice that it updates to here this
1:08:37one's equal to C12 which correlates to
1:08:39this one right to the left of it so
1:08:41let's calculate our total salary and
1:08:43this is going to be taking our annual
1:08:44salary in column D and adding it to our
1:08:47bonus Max in column e so we can do this
1:08:50by specifying
1:08:52D3 plus E3 and from there there pressing
1:08:56enter once again to autofill it I select
1:08:58that cell that I want and drag it on
1:09:00down now if I want to calculate what is
1:09:02the rate of bonus or the bonus rate that
1:09:05is going to be the bonus divided by that
1:09:09salary so in this case E3 / D3 once
1:09:15again going to use autofill drag and
1:09:16drop it all the way down now for all
1:09:18these values I don't like what it's
1:09:19formatted as right now I'm actually
1:09:21going to change this to a percentage and
1:09:23I want to see one decimal place so I'll
1:09:26press this one to expand out one now
1:09:28anytime I do any type of mathematical
1:09:30operation in Excel I always want to try
1:09:32to confirm it that it's correct I did
1:09:35the operation correctly so in the case
1:09:37of this bonus rate I can do this by
1:09:40confirming what we got for total salary
1:09:43previously so if we took that bonus rate
1:09:45is which we want to confirm right so
1:09:47we're going to take that and multiply it
1:09:50times our annual salary right so that
1:09:53should give us that bonus rate right
1:09:55there then if we wanted to like we said
1:09:57we want to confirm total salary right
1:09:58here so I can just add in that we want
1:10:02to also add in that annual salary itself
1:10:06and we do have that total salary right
1:10:08here to actually confirm what's going on
1:10:10dragging it down and doing an autofill
1:10:12all these values look like they
1:10:14correlate to what it should be for total
1:10:16salary so I feel we calculate a bonus
1:10:18rate correctly now going back into the
1:10:20formula itself you can see we have
1:10:22multiple operations in here how do we
1:10:24know whether multiplication addition
1:10:26subtraction what comes first well really
1:10:29if you know the order of operations it
1:10:31really is the same here here the
1:10:33different operators listed in their
1:10:35order of Precedence exponentiation comes
1:10:38first multiplication division or second
1:10:41then addition and subtraction are third
1:10:43it's Then followed by concatenation
1:10:45which we did in one of the previous
1:10:46lessons followed by the comparison
1:10:48operators which we're about to get
1:10:52to so with that segue here we are
1:10:54comparison operators
1:10:56for this you probably are familiar with
1:10:57the first three the last three are
1:10:59something that get a little bit more
1:11:00complicated whenever you have a greater
1:11:02than or equal to less than or equal to
1:11:04or in this case a not equal to so
1:11:07previously I just sort of did a cursor
1:11:09check to make sure this confirmed t
1:11:11total salary column equals this other
1:11:13total salary column but imagine you have
1:11:16hundreds of thousands of rows how can we
1:11:17actually compare this and find these
1:11:19values well what we can do is we can say
1:11:22hey is G3
1:11:25equal to I3 this looks a little bit
1:11:28confusing right CU you have two equal
1:11:30signs in there but everything to the
1:11:31right of the equal sign it's basically a
1:11:33comparison and from there it either ends
1:11:36up as a true or a false and we can drag
1:11:39and autofile this in and everything is
1:11:41true similarly if we want to find
1:11:43something like is the bonus Max greater
1:11:45than the annual salary we can do hey is
1:11:48bonus Max at E3 greater than that at D3
1:11:53and the typical of any data a science
1:11:55job none of these really exceed that at
1:12:00all all right now that we're familiar
1:12:02with math operators and also comparison
1:12:04operators let's dive deeper into cell
1:12:07referencing and we've been doing this
1:12:09previously whenever we reference another
1:12:10cell like A2 but we're going to add a
1:12:13little twist to this I'm going to go
1:12:14ahead and hide some of these columns
1:12:16that way we clear up the Clutter going
1:12:18to hide column F by right clicking it
1:12:20and selecting hide then I'm also going
1:12:22to select all the columns H through k
1:12:25and also hide them want everything to
1:12:28appear on the same sheet so we're going
1:12:29to be referencing this table down here
1:12:32for this portion of the exercise and
1:12:35this is potentially goals that you may
1:12:37have when you're trying to land a job
1:12:39you may know how many years of
1:12:40experience or you should have know how
1:12:42many years of experience you have along
1:12:43with a goal total salary that you want
1:12:45to achieve and so we're going to be
1:12:47building out formulas with this in order
1:12:49to be able to find out which of these
1:12:52jobs actually meet our conditions of the
1:12:55expected years of experience and total
1:12:58salary for so for this we'll go with
1:12:59that I have five years of experience
1:13:01then I'm looking at
1:13:03$90,000 the first we want to calculate
1:13:05in column L is whether it meets our
1:13:08experience so for this we'll say hey is
1:13:11C15 right here less than or equal to the
1:13:16value right here in our experience and
1:13:19as expected five is less than or equal
1:13:21to basically equal to 5 it's true now
1:13:24we're going to run a problem now when we
1:13:25try to autofill this if I try to
1:13:27autofill this down I'm getting this one
1:13:30is false and then these all is true but
1:13:33I would expect especially this AI
1:13:34specialist at three it would be false
1:13:37and so let's actually inspect this well
1:13:40as we can see from this this is
1:13:42referencing well c23 which is way down
1:13:45here but it's still referencing the
1:13:47correct C11 right here the problem is we
1:13:50didn't really want this value up here
1:13:54this C15 to actually change whenever we
1:13:56went to do the autofill down below it so
1:13:59what we can do here is provide a fixed
1:14:02reference of that cell in order to do
1:14:04this we're going to insert those dollar
1:14:06signs that we saw
1:14:08previously before the column and then
1:14:10also the row so in this case I have C
1:14:14locked and I have 15 locked now the
1:14:16formula itself doesn't change at all but
1:14:18now when I drag and drop this down all
1:14:23of these are updating correctly as
1:14:24expected AI specialist is going to be
1:14:26false whenever I actually click on it to
1:14:28inspect it it's still referencing that
1:14:30C15 C11 next we're going to move on to
1:14:33column M of seeing if it meets our
1:14:35salary requirements so for this one
1:14:37we'll be seeing hey is the salary or
1:14:41total salary in G3 greater than or equal
1:14:45to our total salary down here of 90,000
1:14:49now we already know we need to lock c16
1:14:52of this 990,000 because we're going to
1:14:53be autofilling it down I can manually
1:14:56type in the dollar signs but a shortcut
1:14:58to this is just pressing F4 if you're on
1:15:01a Mac you'll need to press function F4
1:15:04anyway this locks this in so now
1:15:07whenever I drag and drop this down as
1:15:10expected the only other one that's less
1:15:11than 990,000 is this data analyst rule
1:15:13right here now I want to play with this
1:15:15just a little bit more so we talked
1:15:16about this right here putting a dollar
1:15:18sign in front of the column and then a
1:15:20dollar sign some of the row is a fixed
1:15:23reference they also have what is called
1:15:24a mixed reference so I'm going to go
1:15:26ahead and put my cursor right there next
1:15:29to G3 I'm going to press F4 and it's
1:15:32going to do the absolute reference but
1:15:34if I press it one more time it's going
1:15:36to do a mix reference if you notice
1:15:38there's only a dollar sign in front of
1:15:39the three or if I press it again there's
1:15:41only a dollar sign in front of the G now
1:15:45technically this is going to work but
1:15:46fine because we're going to now lock
1:15:48this G column for this but it's going to
1:15:50allow the three to update so I'm going
1:15:52to show you this now by actually
1:15:54dragging and dropping this down and from
1:15:57there inspecting that last cell contents
1:16:00we can see that that g is locked as
1:16:01expected but it moved down now instead
1:16:04of locking just the column we could also
1:16:06lock the rows so I could also do change
1:16:09up c16 now instead and lock the rows of
1:16:13c16 cuz we're going to still stay in
1:16:15that c column right there pressing enter
1:16:17now autofill we don't have to just go
1:16:19down we can also go up so inspecting it
1:16:22locking it didn't really change by only
1:16:24locking the row of 16 so let's wrap this
1:16:27all up by actually def finding out which
1:16:29of these actually meet both of our
1:16:32conditions of 5 years and 990,000 well
1:16:34it turns out that behind the scenes true
1:16:37is equal to 1 and Z is equal to false so
1:16:40if actually were to take this and add
1:16:42this true to this true right here we
1:16:44should get two autofilling it all the
1:16:47way down we have two1 2 1 so basically
1:16:50confirm that hey zero yeah false is zero
1:16:53because 0 plus 0 is Zer now I recommend
1:16:56instead we're going to be going through
1:16:57and doing L3 * M3 so that way anytime
1:17:00either one of these are true they will
1:17:03return a one and now in order to get a
1:17:06true or false back on whether it meets
1:17:08both we can select that N3 and see hey
1:17:13is it equal to one type over there equal
1:17:16to one and it evaluates to true so now
1:17:19I'm going to go ahead and just hide
1:17:21these columns so we can actually see
1:17:22this a little bit better but we can
1:17:25find values in here that meet our
1:17:28conditions of the 90,000 or 5 years and
1:17:30let's say we're doing job searching and
1:17:32it lasts over a year um we have to
1:17:34change this to six this will
1:17:36automatically update the formulas that
1:17:38we've used here as shown here so that's
1:17:41our intro to formulas and for me the
1:17:43hardest thing to wrap my head around
1:17:45when I was first tackling this was
1:17:46around absolute and mixed references so
1:17:49we have some practice problems for those
1:17:51that purchased the course practice
1:17:52problems in order to go through and test
1:17:54this out and understanding what happens
1:17:56whenever you lock the row or lock the
1:17:58column all right and after that we'll
1:18:00next be diving into an intro into
1:18:02formulas which I'll be covering for the
1:18:04remainder of this chapter with that see
1:18:06you in the next
1:18:10one for this lesson we're going to be
1:18:12focusing on an intro into functions
1:18:15specifically we're going to be going
1:18:16over all the different functions that
1:18:18we're going to be deep diving within
1:18:20this chapter itself along with some
1:18:22common problems you may run into and
1:18:24errors and how to troubleshoot it to do
1:18:26this we'll be continuing on from that
1:18:29data set that we used in the last lesson
1:18:31specifically we'll be calculating things
1:18:33like averages and counts and how many
1:18:36jobs actually meet our goals and we'll
1:18:39be using functions for this so you can
1:18:42continue working in that workbook that
1:18:43you had from last time or open this
1:18:46function intros workbook in this
1:18:49function intros workbook I've gone ahead
1:18:51and moved our job goals over here to
1:18:53that column RNs and then added in this
1:18:56bottom portion right here for the
1:18:57averages and total counts really you can
1:19:00do and manipulate as you
1:19:03want so why use functions let's look at
1:19:06a couple quick examples on the
1:19:08importance of these things let's say we
1:19:10wanted to get the average of each one of
1:19:13these Columns of experience annual
1:19:14salary and bonus Max previously we know
1:19:17we can actually reference each one of
1:19:19these cells to calculate the average we
1:19:21wanted to do that we would have to
1:19:23actually add up all the values so I have
1:19:25to go through select C3 C4 all the way
1:19:28down to
1:19:29C12 and we would need to divide it by
1:19:33that total number of 1 2 3 4 5 6 7 8 9
1:19:3710 in that case we' get the average also
1:19:40that me count that 10 wasn't necessarily
1:19:42perfect so I don't really recommend
1:19:43doing this but anyway nonetheless we can
1:19:45actually do autofill to calculate the
1:19:48averages as the is as well as it
1:19:50automatically update the referencing
1:19:52correctly to it but I don't recommend
1:19:54doing that instead I recommend using
1:19:57functions specifically we can use
1:19:58something like the average function as
1:20:00soon as I start typing a function a in
1:20:04this case all the functions that have
1:20:06the a name pop up if I wanted to well I
1:20:08do know I want average right here I can
1:20:11select it it provides a brief statement
1:20:13of what it's actually going to do and
1:20:15then I can doubleclick it to insert it
1:20:18below here it actually specifies what's
1:20:21going on with this function here and
1:20:24specifically to provides me to hey
1:20:26provide in these numbers now I could
1:20:28select these number by number as we can
1:20:30see that there's in Brackets here this
1:20:32number two that means it's an optional
1:20:34parameter but instead what we'll do is
1:20:36we'll just provide a range providing it
1:20:39from C3 all the way to C12 in that case
1:20:43I got 5.3 similar to above and then
1:20:45dragging this over we can get all the
1:20:47other values as well as a quick example
1:20:49also previously we had made this sort of
1:20:52convoluted formula in order to calculate
1:20:54calate whether we met both conditions of
1:20:58mean our experience and also our salary
1:21:00which we're specified over here well
1:21:02there's actually a formula for that and
1:21:04it's called the and formula and what it
1:21:07takes for its arguments are logical
1:21:09values so it can take a logical one for
1:21:11the first parameter I can specify L3 and
1:21:15then for the second parameter I can
1:21:17specify M3 and notice how this second
1:21:20parameter now highlights or becomes more
1:21:22bold as I put it in so you can keep
1:21:24track of where you are in the formula
1:21:26any I'm going to close the parenthesis
1:21:27press enter and it evaluates to True
1:21:30dragging it all down these should match
1:21:32these other ones and yeah this is
1:21:34definitely something I'd use over these
1:21:35formulas that I've used
1:21:38before so let's dive into this formula
1:21:41tab more and understand the capabilities
1:21:44that we're going to be carrying out the
1:21:45next lessons in this chapter the most
1:21:48powerful of these especially for those
1:21:49new to excel is this insert function
1:21:52anytime you're looking for a function
1:21:54and maybe can't can't recall the name
1:21:56and you're not sure what even starts
1:21:57with you can put something in here so
1:21:59say I wanted maybe the average I can
1:22:02type in average and then everything that
1:22:04basically calculates a different average
1:22:07off of it even if they're closely
1:22:08related like this rank average will pop
1:22:10up in here along with a description
1:22:12below explaining it if you've used a
1:22:15formula recently you can come in here
1:22:17under recently used and I frequently
1:22:20find myself just going back to this in
1:22:21order to select something I may have
1:22:23used recently now in the next seven
1:22:24lessons we're going to be diving into
1:22:27each one of these all the way through it
1:22:29from logical and text to look up and
1:22:32also math and trick now one note we
1:22:34won't be going into detail on this
1:22:37financial functions because I find
1:22:39they're sort of nuanced but we will be
1:22:41going into all the different ones that
1:22:43I'm using on a daily basis as a data
1:22:46analyst that aren't specific to
1:22:49financial
1:22:52applications so let's get into
1:22:54understanding the basics about formulas
1:22:57by calculating these different counts
1:22:58and especially counts around whether any
1:23:01of these jobs meet our goals for this I
1:23:04know I want to use a count function so
1:23:06I'm going to go to this insert function
1:23:08I'm going to type in count now there's a
1:23:10bunch of different ones that pop up
1:23:12count itself just counts the number of
1:23:13cells in a Range that contain numbers it
1:23:15has to have numbers in it if I wanted to
1:23:17do something more around text I would
1:23:19say hey count the number of cells in
1:23:21range that are not empty I could do even
1:23:24do something conversely of counting the
1:23:25number of blank cells for us we want to
1:23:27actually do count so as we showed before
1:23:30I'm just going to come in type count
1:23:32it's going to prompt me that I need to
1:23:33at least put at minimum a value and I
1:23:36want to count all these cells here so
1:23:39using autofill to fill it over um we can
1:23:43see that all the different values are 10
1:23:44nothing really spectacular here but now
1:23:47let's get into a pretty unique use case
1:23:50of count so in this scenario that I'm
1:23:52count trying to calculate in cell c16
1:23:54I'm trying to find out how many jobs
1:23:57above here in these 10 right here how
1:24:00many meet our goal of less than or equal
1:24:03to 5 years and I want to count the
1:24:06number of these so I know I want a type
1:24:09of count I can go into insert function I
1:24:11know it's here inside these different
1:24:13statistical functions specifically I
1:24:16have these different counts right here
1:24:19and I'm going to scroll over this count
1:24:20if right here and it's going to provide
1:24:21me a description it says Hey counts the
1:24:23number of cells Within range that meet
1:24:25the given condition and that's what we
1:24:28want to do we want to meet a condition
1:24:29of a certain amount of experience now it
1:24:31provides this box in order to help me
1:24:34input in these values so for the range
1:24:37here what I can do is specify hey I want
1:24:39to count inside of here if they meet a
1:24:43certain criteria and just going back to
1:24:45that range right here we can see that it
1:24:47already input all those different values
1:24:50into an array likee object okay so the
1:24:53criteria right now is NX want to put
1:24:54something in here I can also press this
1:24:57box and it'll make it disappear and I
1:24:59want to compare it to this experience
1:25:02but I want it to be less than or equal
1:25:04to five so I can press enter to accept
1:25:06it but the problem is it's going to
1:25:11evaluate whether five is any of these
1:25:15columns here and right now we see that
1:25:17there are two I'm going go ahead and
1:25:18close up so we can see this better right
1:25:20now we can see that there's two fives in
1:25:22here that's not what we we want we want
1:25:25to see everything that is less than or
1:25:27equal to 5 so instead what we need to
1:25:30put in here is less than or equal to 5
1:25:34now I'm going to press enter and we're
1:25:36going to get an error this is pretty
1:25:38common whenever you are manipulating
1:25:41different formulas and you have in this
1:25:43case I have this less than or equal to
1:25:45right here so Excel is confused by this
1:25:48what we need to do is actually put
1:25:51parentheses around this which basically
1:25:54sort of makes it into a string or text
1:25:56if you will but now it knows hey I want
1:25:59you to look for less than or equal to 5
1:26:01I want you to evaluate this entire thing
1:26:03pressing enter bam we have six values
1:26:06here that are less than or equal to five
1:26:10now similarly I can drag this over
1:26:12because we want to also do this for
1:26:13experience but I don't want to do less
1:26:16than or equal to five I want to do
1:26:17greater than or equal to
1:26:2090,000 and in this case we have nine cuz
1:26:23we only have have one that's less than
1:26:25this but as you find out on this course
1:26:27I don't like hardcoding values into my
1:26:31formulas in this case I have five inside
1:26:34of here but I'm already having five
1:26:35right here what happens if I want to
1:26:36change this maybe to say something like
1:26:38three well it's not going to actually
1:26:41update these values right here so I'm
1:26:44going to go ahead and actually change
1:26:45that back to five and we're going to
1:26:47make another formula that actually fixed
1:26:49this so I want to drag these down but we
1:26:52actually didn't lock either one of the
1:26:54these cells and it will cause errors if
1:26:57we do so I'll just select right next to
1:26:58it press F4 next to C3 I'll do the same
1:27:02of f4 doing the same in this cell as
1:27:04well all right now I'll take this and
1:27:06I'll drag this down so now let's
1:27:09actually fix this to be more Dynamic we
1:27:11don't want it to be less than or equal
1:27:13to this five right now what we can do is
1:27:15that Amper sand operator and then from
1:27:19there put in reference to S3 which
1:27:24contains our five pressing enter bam we
1:27:26got six same thing here I can delete
1:27:29that 90,000 put in an Amper sand and
1:27:32then from there we're going to be
1:27:34basically putting it to mashing it
1:27:36together with that 90,000 and it
1:27:38evaluates now when we change this
1:27:39experience to say something like two we
1:27:42can see that it actually updates
1:27:44appropriately to see that oh only one
1:27:46job meets this requirement so pretty
1:27:49cool I'm going change that back to
1:27:52five now frequently you're going to run
1:27:54into errors with your formulas let's say
1:27:57I wanted to divide one by zero not a
1:28:00good thing that we need to do anyway I'm
1:28:02going to get this error right now you
1:28:03can notice it because it has this green
1:28:04check on the upper left hand corner but
1:28:07also it starts with this hashtag and
1:28:09it's saying hey you have a divide by
1:28:12zero error I can even come down into
1:28:15here and it tells me even more on this
1:28:19provides help on this or if I wanted to
1:28:20even ignore it now in this sheet of this
1:28:23work workbook I have a bunch of
1:28:25different errors in here that you may
1:28:27run into from time to time again and
1:28:29we're going to be running into these
1:28:31errors as we go through the rest of this
1:28:32chapter so if you get stuck along the
1:28:35way while we're going through this I
1:28:37feel like this is a good reference for
1:28:38you to maybe save somewhere in order to
1:28:40understand what is going on with the
1:28:42different errors you may encounter now
1:28:44the biggest time saer I've found with
1:28:46any of these errors is using some sort
1:28:49of chatbot specifically me I'm going to
1:28:50go to something like chat GPT or even
1:28:52claw they're going to be able to provide
1:28:54really quick help in understanding what
1:28:56an error is and what I need to do to fix
1:28:58it all right so now it's your turn to
1:29:00dive into and test Out These intro into
1:29:03functions and play with them and
1:29:04experience some of the errors of your
1:29:06own after that we'll be diving into
1:29:08logical functions a major type of
1:29:10function that you need to be aware of
1:29:11with that I'll see you in the next
1:29:16one now that we have the basics down on
1:29:19formulas and also functions we're going
1:29:21to be moving into one of the most
1:29:23important typ of functions to know
1:29:26logical ones the most popular of these
1:29:28are an if condition basically looking at
1:29:30something and then providing a response
1:29:33based on it so for this analysis we're
1:29:35going to be jumping into our data
1:29:37science job salary data set but we're
1:29:40only going to focus on the first 20 rows
1:29:43of it here and on the next few lessons
1:29:45as well as I don't want to overwhelm you
1:29:47with the all the data just yet now for
1:29:50the final results we're going to be
1:29:51doing two major things the first is
1:29:54determining within this list of jobs
1:29:57whether they meet our conditions of
1:29:59finding the job we want of a data
1:30:01analyst or business analyst and will
1:30:03Market not desired or Ro desired
1:30:05additionally we're going to do a common
1:30:07practice and analytics of bucketing
1:30:09basically taking those salaries and
1:30:11depending on the amount value putting it
1:30:13into a certain bucket for us we're going
1:30:15to be looking at whether they have
1:30:17salary data in this data set or more
1:30:20specifically if they are greater than
1:30:22our goal of 85,000
1:30:27so why are these logical functions
1:30:28needed well let's jump into that last
1:30:30data set real quick and simplify how we
1:30:33can actually use these as a quick
1:30:35example previously in this P column we
1:30:37were evaluating whether they met both of
1:30:40our conditions of experience or salary
1:30:42we can use an if statement in order to
1:30:46clarify this so I can specifically call
1:30:48out with an if statement saying if it
1:30:51has The Logical test that we want to
1:30:54actually evaluate so I'm going to put in
1:30:56P3 in this case as it's going to return
1:30:58true or false and then from there the
1:31:01next value in there is value if true
1:31:03which what do we want to return if it is
1:31:05true well that our goal is met and then
1:31:08if it's not met we want to have well not
1:31:11met okay and then this whenever we drag
1:31:14this down will provide not met or goal
1:31:18met depending on if this is true or
1:31:20false and so that's the power of these
1:31:23if statements in helping us actually
1:31:25provide this
1:31:28value so that was just a quick example
1:31:30of if let's actually jump into some more
1:31:32examples so you get more familiar with
1:31:33how to use this so here we are in this
1:31:35data set and I don't need all the
1:31:37columns of this data set so I'm just
1:31:39going to select the columns that I don't
1:31:41need I'm going select B through G and
1:31:44then hide it additionally I'm not going
1:31:46to need I or J so I'll hide these as
1:31:49well so our first goal is to identify
1:31:52whether these jobs meter conditions of
1:31:54either a data analyst or a business
1:31:56analyst we're going to start simple by
1:31:58just finding out which one is a data
1:32:00analyst first and then which one is a
1:32:01business analyst and meets those
1:32:03conditions so once again we'll start
1:32:05with that if condition and for this
1:32:07we're going to put in that logical test
1:32:08remember pretty the example we need to
1:32:09have a return either true or false so
1:32:13we're wanting to check whether senior
1:32:14data engineer in A2 is equal to data
1:32:19analyst in K1 now we're going to be
1:32:22autofilling this down so we need to make
1:32:25sure that the A2 we're fine with it
1:32:27actually adjusting as necessary K1 we
1:32:29want it to lock at least lock on the row
1:32:33value of one then if it's true we'll be
1:32:35roll desired and if it's not it's not
1:32:37desired as expected senior data engineer
1:32:40is not desired let's drag this all the
1:32:42way down and just double checking it we
1:32:44see that the data analyst roles are R
1:32:45desired okay so I can drag this over now
1:32:49and just to double check it shifted over
1:32:51to B2 but it's still but it's selecting
1:32:53a right one of L1 so actually what I'm
1:32:55going to do is I'm going to delete this
1:32:57go back up here I'm sort of a
1:32:59perfectionist I'm going to end up
1:33:01locking that a value so it stays in that
1:33:04a column none my values are going to
1:33:06change here and then when I actually
1:33:08drag it over I can check that okay A2 is
1:33:11the correct one I once selected to
1:33:13compare it to business analyst in
1:33:15L1 okay then I'm going to autofill all
1:33:17the way down looks like there's only two
1:33:20business analyst roles here so now how
1:33:22can we identify that it meets both of
1:33:25those conditions both data analyst and a
1:33:28business analyst well we're going to do
1:33:30one approach first and it's called a
1:33:32nested if statement and it's not really
1:33:35the approach I'm going to recommend but
1:33:37it's something that you should be aware
1:33:38of so what I'm going to do is I'm going
1:33:39to select cell K2 I'm going to go ahead
1:33:42and copy this formula plugging it in
1:33:45here we have it here and making sure
1:33:48that it operates correctly yep it does
1:33:50so how does this nested if statement
1:33:52work well we're going to still evaluate
1:33:54our first condition is the first role
1:33:57evaluated as data analyst does it meet
1:34:00that if it is we want to mark it as rule
1:34:02desired now we get into what happens if
1:34:05it's not a data analyst well now we want
1:34:07to now check if it's a business analyst
1:34:09so I'm going to close out this and what
1:34:11we can do is I'm going to take this
1:34:13business analyst formula right here
1:34:15everything up to the if and I'm going to
1:34:17go back in here and I'm going to drop it
1:34:20in right here inside of the value if
1:34:24false so it's an nested if statement an
1:34:27if inside of another if so now if we
1:34:30don't meet this first condition of the
1:34:31value if isn't true it will go into the
1:34:33nested if statement and start checking
1:34:35this condition is now the software data
1:34:38engineer equal to data analyst if it is
1:34:40it's R desired if not it's not desired
1:34:43so let's now drag and drop this all the
1:34:45way down I'm going to expand this out a
1:34:47little bit and now we can see if it's
1:34:50data analyst we get rule desired along
1:34:52now with if it's business analyst also R
1:34:57desired but I'm not a fan of nessf as
1:35:00they're hard to read instead I like
1:35:02using the functions of and and or and
1:35:06should be a little bit familiar because
1:35:08we saw it from the intro lessons that we
1:35:10did previously with and it evaluates
1:35:14whether both conditions are true so in
1:35:17this case I'll put in condition one of
1:35:20B3 and then condition two of C E3 and
1:35:24both conditions are true so it satisfies
1:35:26as true dragging this all down in all
1:35:29the following condition cases they're
1:35:32not true for both conditions so
1:35:34therefore it evaluates as false in or it
1:35:37checks whether condition one or
1:35:39condition two is true and then will
1:35:42return true so inputting in the
1:35:45conditions of B3 and C3 one of the
1:35:47conditions here are true actually both
1:35:49are dragging it down I expect yeah the
1:35:52second and third rows are also true
1:35:54where the final one both are false so
1:35:55therefore it is false so let's run the
1:35:57same Andor logic that we've run before
1:36:00in order to determine which one we
1:36:01actually use so in this one we're
1:36:03checking whether both of these jobs of
1:36:05data analyst and business analyst are
1:36:07equal to this one here senior data
1:36:08engineer as expected false and what
1:36:12should we should expect for all of these
1:36:13all of them are false because none of
1:36:15these are going to be both data analyst
1:36:16and business analyst so as you can
1:36:18probably guess or it's probably going to
1:36:20be the one that's going to work for us
1:36:21we're evaluating whether either data
1:36:23anal or business analyst are going to
1:36:25match up to that value of senior data
1:36:27engineer in this case we're getting
1:36:29those tree values for data analyst and
1:36:31True Values for business analyst so now
1:36:33we're going to put that or function
1:36:36inside of that if for The Logical test
1:36:39and from there we can determine whether
1:36:41it's rule desired not desired dragging
1:36:43all down all of it's matching as
1:36:45expected okay I'm going to go ahead and
1:36:46hide these
1:36:50rows so now what happens if we don't
1:36:52want just a evaluate for a true or false
1:36:56condition basically we want to evaluate
1:36:57for multiple different conditions well
1:37:00that's going to be something that comes
1:37:01up if you need to ever bucket data which
1:37:03we're going to be doing with salaries
1:37:04now for this first one we're going to
1:37:06just use a simple if statement we want
1:37:08to determine whether a salary is greater
1:37:11than 85,000 or if it's not we want to
1:37:14just specify that the salary is low so
1:37:16for this we're going to be evaluating if
1:37:18H2 we're going to go ahead and lock that
1:37:20H column is greater than that 85,000
1:37:26which will lock that completely for the
1:37:2885,000 then we want to say the salary is
1:37:31greater than 85,000 conversely if it
1:37:34doesn't meet this we want to say that
1:37:35the salary is low I'm going to expand
1:37:38this out a little bit and then we're
1:37:39going to drag this down as expected we
1:37:43have the values returning those are
1:37:4585,000 and then this one at 35,000 it is
1:37:48Mark is low now the problem we're
1:37:50running into and why we need multiple
1:37:52conditions is this is the salary is low
1:37:55but there's actually no data there we
1:37:57need specify in these conditions that
1:37:58well there's no data so for this we can
1:38:01use an ifs formula and what happens with
1:38:05this is you provide a test and then a
1:38:07value if true and that's just the first
1:38:09one we can then provide another logical
1:38:12test and the value of true so the first
1:38:14thing I'm going to test is if there is
1:38:16no value there I'm going to go ahead and
1:38:18lock that H column as well and when I'm
1:38:20looking for a blank I'm just going to
1:38:22put in two quot Mark say signifying that
1:38:25it's blank and the value of true is no
1:38:28data okay put another comma we can see
1:38:30we now we're on to logical test number
1:38:32two the next thing we want to test is if
1:38:34it's greater than 85,000 so we'll see H2
1:38:38again locking that H and we want send it
1:38:41if it's greater than or equal to that
1:38:4485,000 which will lock if it is we want
1:38:47to return back that salary is greater
1:38:50than
1:38:5185k and finally we're on to the final
1:38:53logical test and basically we want all
1:38:56of them to pass this condition so
1:38:59instead of providing hey salary less
1:39:01than 85,000 we're just going to pass in
1:39:03true because we want it to be true and
1:39:05we would expect this to be any values
1:39:08between a number that are between 0 and
1:39:1085,000 so like before we're going to
1:39:12specify salary low running this we going
1:39:15to expand this out and then drag this
1:39:19down we have when it returns no data no
1:39:23data salary less than 85,000 return
1:39:25salary low and then whenever it's
1:39:27greater than 85 the correct results now
1:39:30if s functions are one of the more
1:39:33complex functions to work with so you do
1:39:35need some practice with this like for
1:39:37those that purchased course practice
1:39:38problems you have some now to go into
1:39:40and actually try this out manipulate and
1:39:42better understand how to work with this
1:39:44with that in the next one we're going to
1:39:45be jumping into my next favorite type of
1:39:47functions math functions which heavily
1:39:49used in data analytics all right with
1:39:51that I'll see you in the next one
1:39:57now in this lesson we're going to be
1:39:59using math functions and also some
1:40:01statistical functions in order to
1:40:03perform Eda or exploratory data analysis
1:40:07on our job posting data set and for this
1:40:10we're going to be focusing on the five
1:40:11major functions of count sum average and
1:40:16also Min and Max and we're not only
1:40:18going to focus on the core versions such
1:40:20as just count but also the if an ifs
1:40:24version so they have multiple different
1:40:26versions that we're going to get to now
1:40:27for our analysis we're going to be
1:40:29diving into the full data set of the
1:40:31data science job postings which has over
1:40:3330,000 different job postings and in it
1:40:36we're going to be specifically diving
1:40:38into data jobs that are in the United
1:40:41States for data analyst and we're going
1:40:43to be able to use these sort of
1:40:45different functions that incorporate if
1:40:47and ifs in order to fine-tune in what
1:40:50we're looking for one quick note you're
1:40:51not limited to using un States and data
1:40:54analyst you can use the scenario that
1:40:56you're in of what country you're in and
1:40:58what job title you're most interested in
1:41:03instead so we're going to be filling out
1:41:05this table right here and we're going to
1:41:07start on Row three focusing on those
1:41:09count functions first now the data set
1:41:12is actually much larger than this three
1:41:14columns I actually I'll unhide between a
1:41:17through K but we're not using any of
1:41:19these columns in between here so I'm
1:41:22just hiding that them and making it
1:41:24easier for us to work with for this
1:41:26we're going to focus on the core
1:41:27function of only count and we're going
1:41:30to be looking at those that have all the
1:41:33yearly salary data in it as you can see
1:41:35over here that there's missing blanks in
1:41:36here so we don't want to count those
1:41:38that are missing anyway what I'm going
1:41:39to do here is Select column M and as you
1:41:43knew it selects the range of M colon M
1:41:46and then from there press enter so what
1:41:48we're finding is that around 22,000 jobs
1:41:51out of these 30,000 we're going to find
1:41:52out have salary data and how do I know
1:41:55about that 30,000 well let's actually
1:41:57see we can actually use instead we can
1:42:00use a count a function which stands for
1:42:04count all and it counts the number of
1:42:06cells in a Range that are not empty
1:42:09specifically I want to capture those in
1:42:12the job title short column right here so
1:42:14I'll do a colon alen running this we get
1:42:17to see that it's around 32,000 jobs One
1:42:20technical note before we continue these
1:42:22are since we're doing the columns
1:42:24themselves in this case the count M it's
1:42:26also counting that column header in this
1:42:29case so if we want to be exactly
1:42:31accurate which in this case I just need
1:42:33roundabout numbers if we want to be
1:42:35exactly accurate technically we would
1:42:36want to go in and S say subtract one to
1:42:40get what the actual value is but frankly
1:42:43I'm just trying to look at General
1:42:44numbers right now I'm not too car about
1:42:46one or two off so now let's dive into
1:42:49analyzing this further on my needs
1:42:51looking for specifically focus on the
1:42:53United States first so we're going to
1:42:55find those that have in the job country
1:42:58here United States and for this we're
1:43:01going to use the count if function and
1:43:05this counts the number of cells within a
1:43:07range that meets the given condition so
1:43:10you provided a range in this case we're
1:43:12going to provide the range of that
1:43:14column K and then the criteria itself we
1:43:16want to filter for United States which I
1:43:18conveniently typed above so we'll select
1:43:20it right there I'm also going to lock it
1:43:22by pressing F4
1:43:23and then running this we get that about
1:43:2625,000 jobs contain United States so now
1:43:29let's evaluate those data analyst jobs
1:43:31using that same thing of ctif once again
1:43:35we provide the range in this case we're
1:43:36looking at that job title short column
1:43:38and for this we want to look for data
1:43:40analyst locking this cell we get about
1:43:439600 jobs for data analyst now next up
1:43:47we're going to be using count ifs
1:43:49specifically we're doing this because we
1:43:50want to find jobs that contain not only
1:43:52data an but also contain that they're
1:43:55from the United States now we can't just
1:43:58add these two columns together because
1:44:00one it's going to as we once we add it
1:44:02up we see that's even greater than all
1:44:03the jobs there that's not what we
1:44:04actually want we want conditions like
1:44:06here on row 16 where it's a data analyst
1:44:09and United States whereas something here
1:44:12on roow 223 where it's a data analyst in
1:44:14s that's not going to meet our condition
1:44:17so we wouldn't count it so using count
1:44:20ifs this counts the number of cells
1:44:23specified by a given set of conditions
1:44:26or criteria for this we need to specify
1:44:28a range and then the criteria first
1:44:31we'll focus on the range of a for job
1:44:33title short and we're looking to match
1:44:36that of data analyst which I'll lock by
1:44:38pressing F4 then now we're moving on to
1:44:41criteria range number two where for this
1:44:43one we're looking at Job country now and
1:44:46for that we want to look for the
1:44:48criteria of United States locking this
1:44:50with F4 closing this with parenthesis
1:44:53and then running it we get around 8,000
1:44:56jobs and this makes sense right because
1:44:58it would be less than that 9,000 data
1:45:01analyst because some of these aren't
1:45:03going to be from the United States now
1:45:05with how this is Flowing we could
1:45:06actually make a visualization out of
1:45:09this data right here so going into
1:45:11insert and then recommended charts we
1:45:14have here a funnel chart so I'm going go
1:45:17ahead and insert that in and this
1:45:19basically shows the funnel if you will
1:45:21of jobs we have we started with almost
1:45:2332,000 jobs and we got towards the end
1:45:26of the jobs that we actually care about
1:45:28us and data analyst at around 8,000 I'll
1:45:31go ahead and move this off to the side
1:45:32for
1:45:34now all right next moving into the sum
1:45:37function and the core one itself of
1:45:40actual sum itself it's pretty simple we
1:45:43have to just we're going to obviously
1:45:44using salary year average column for
1:45:46this because we want to sum up the
1:45:47numbers in them and I'm put in that
1:45:49column of M and we get the sum of values
1:45:51there now unlike count where a count has
1:45:53a count a or count all where we're
1:45:56trying to find if there's blanks or not
1:45:58that's not really applicable In Sum and
1:46:00average and also in Min or Max so I'm
1:46:04actually going to go ahead and just gray
1:46:05these out because we're not going to
1:46:06need them now moving into suth which
1:46:09adds the cells specified by a given
1:46:11condition or criteria this one is a
1:46:15little bit more complex than we dealt
1:46:17with with count because we first want to
1:46:20provide the range that we're going to be
1:46:22evaluating for a certain criteria which
1:46:25in our case the range you want to
1:46:26evaluate is job country because we're
1:46:29evaluating for if it contains United
1:46:31States which I'll lock with that four
1:46:33but we're not summing the countries
1:46:36because there are text column so we have
1:46:37to provide this sum range which is
1:46:40column M similarly once again we can do
1:46:42that sum if looking for data analyst so
1:46:45in this case we're going to be looking
1:46:46at column A to evaluate if it has data
1:46:49analyst in it and then from there the
1:46:51sum range once again is going to be that
1:46:53column M now the sum ifs similar to that
1:46:57count ifs adds the cell specified by a
1:47:00given set of conditions or criteria for
1:47:03this one we provide the sum range first
1:47:07so it gets a little bit confusing you
1:47:08got to make sure that you're actually
1:47:09reading the formulas in this case we're
1:47:10going to use M because that's the sum
1:47:12range we want to use and then we're
1:47:14first going to evaluate for that job
1:47:16title short that column A which we're
1:47:18going to evaluate for data analyst and
1:47:21then we'll evaluate for the job country
1:47:24evaluating for United States closing the
1:47:27parentheses and running this bam as
1:47:29expected this value is less than that of
1:47:33the data
1:47:36analyst now moving into the last three
1:47:38of average men and Max which I think are
1:47:40actually more valuable than that sum one
1:47:42we did I'm not going to walk through
1:47:44actually typing in all these in because
1:47:47now you've had a familiarity with how I
1:47:48did the sum which follows the same
1:47:50example for average men in Max feel free
1:47:52to if you want to you can go through and
1:47:54type it out on your own to get more
1:47:55experience doing it but overall I think
1:47:58this has some very unique insights from
1:48:01it from this analysis we did in it we
1:48:03can see that salaries in the United
1:48:05States are around 125,000 where the data
1:48:08analyst is only around 93 and
1:48:11specifically us data analyst is around
1:48:1394 so data analysts in general are lower
1:48:16salaries than the other jobs in the data
1:48:19science Industry as far as Min and Max
1:48:21go we're having as low as
1:48:2425,000 but we're having as high as well
1:48:27at least for a data analyst up to
1:48:29650,000 and apparently there's a job in
1:48:32here around
1:48:34$960,000 and you may be wondering what
1:48:37jobs correlate to this $155,000 or
1:48:39$960,000 well we're going to be diving
1:48:42into that further when we get to that
1:48:43lookup functions one last note on errors
1:48:46before we go I commonly find the most
1:48:47common error with these functions is a
1:48:50value error and that usually occurs
1:48:52whenever in this case we had column a
1:48:54selected initially for criteria range
1:48:56number one let's say we accidentally
1:48:58selected multiple different columns for
1:49:01this obviously we're not trying to
1:49:02evaluate all the different columns we
1:49:03only want to evaluate one column for
1:49:06that criteria of if data analyst Falls
1:49:09in it anyway when I run this I get a
1:49:12value ER anyway this is a common one
1:49:15that I see come up time and time again
1:49:17so anytime you're going through this any
1:49:19of these or the practice problems
1:49:20themselves make sure you're
1:49:22investigating to see that you've
1:49:24actually input in the correct ranges to
1:49:27evaluate cuz it's commonly causing those
1:49:30value errors all right with that you
1:49:32have some practice problems to dive into
1:49:34and next we'll be diving into even more
1:49:36statistical functions in order to really
1:49:38dive into how deep you can go with Eda
1:49:41or exploratory data analysis all right
1:49:44with that I'll see you in the next
1:49:48one we're now going to be taking this up
1:49:51a notch shifting gears from focusing on
1:49:53math functions now to statistical
1:49:55functions for this we're going to be
1:49:57using our job posting data set and
1:49:59analyzing the salaries in this
1:50:02specifically looking at common
1:50:04statistical functions like median
1:50:06standard deviation and even quartiles
1:50:09once we have the basics we're going to
1:50:10shift into an actual analysis looking at
1:50:13what is the average salary of different
1:50:16job titles and we'll even get a sneak
1:50:19peek of visualizing it for this lesson
1:50:21you can start by opening this syst
1:50:22statistical functions workbook we're
1:50:25going to be starting by filling in this
1:50:27table here on the different statistical
1:50:29functions we're going to be filling out
1:50:31and we're still working with that data
1:50:32set we did previously if you noticed
1:50:34I've hidden a lot of the columns that we
1:50:35won't be using for
1:50:38this so we've done a few of these
1:50:40different type of functions already
1:50:42let's go ahead and fill these in for
1:50:43count we'll be using the count function
1:50:45specifically on that M column of salary
1:50:47or average and like before we have
1:50:49around 22,000 values for average we'll
1:50:51be doing the same on that M column we
1:50:53find that's around
1:50:55123,000 for men we'll also run this on
1:50:57the M column and that's around 15,000
1:51:00for Max that's going to be around
1:51:02960,000 so let's move on to our first
1:51:06true statistical function we're actually
1:51:07going to go into this to actually see
1:51:09what it does and that's median it
1:51:11Returns the median or the number in the
1:51:14middle of the set of given numbers so
1:51:18let's go ahead and type that out median
1:51:20and in there we need to specify number
1:51:22or numbers we can specify a range we're
1:51:24just going to keep it simple right now
1:51:25to actually show what this function is
1:51:28actually doing it's selecting the middle
1:51:30of numers so I'm just going to select
1:51:31these top three numbers right now and
1:51:34what I expect for this function to do is
1:51:36to provide basically in a set of numbers
1:51:38given provide the middle number so it
1:51:40should provide us 140,000 which is the
1:51:43center number of these three we don't
1:51:46care about the center of just three
1:51:48values we care about the center of
1:51:50basically all of our different values so
1:51:53I'm going to place the entire M column
1:51:54into it and that is around
1:51:58115,000 now why is this average higher
1:52:01than this median well let's actually
1:52:03visualize it I'm going to select this m
1:52:04column and go to the insert tab going to
1:52:08histograms I'm going to insert a
1:52:10histogram and what this is showing is
1:52:12the distribution of salaries from 15,000
1:52:16all the way to 950,000 bottom xaxis is a
1:52:20little confusing to read but it's
1:52:22basically a range so this case 87,000
1:52:2593,000 how many counts of salaries are
1:52:28falling in between that and that's how
1:52:30large the bar is next to it anyway
1:52:33getting back to that original question
1:52:35why is the average higher than the
1:52:38median itself if you call back from
1:52:40definition a median is the middle number
1:52:43in our set of our list but our average
1:52:47however is taking all the different
1:52:49values and well averaging it out and as
1:52:52we can see from it we have a large
1:52:54amount of salaries around well
1:52:58$100,000 but we do have some up here
1:53:01that are getting close to a million
1:53:02dollar these basically outliers are
1:53:06causing us to have a higher average so
1:53:09basically those values that are near
1:53:11960,000 are dragging that average way
1:53:14higher so that's why I prefer to use
1:53:16something like the median when I can in
1:53:18order to analyze these salaries because
1:53:21they're not skewed by the these outlier
1:53:23salaries that are just something that
1:53:25you're probably not going to get all
1:53:26right next up is standard deviation and
1:53:29for this you have two options standard
1:53:31dev. p and standard dev. s the P stands
1:53:35for population and the S stands for
1:53:38sample this data set is around 30,000
1:53:42salaries and there's way more than
1:53:4430,000 data science jobs available so
1:53:47that's a sample of the actual population
1:53:50so we're going to be using standard Dev
1:53:52s and for this we can insert a range
1:53:54into it so what does this value actually
1:53:57mean well if we had something like a
1:53:59normal distribution which our salary
1:54:01data is somewhat close to that we'll
1:54:03find that one standard deviation from
1:54:06something like the average has in this
1:54:08case right here 34,000 so if we went
1:54:11above and below the average by one
1:54:14standard deviation around 68% which is a
1:54:18heck a lot of data is within this one
1:54:21standard deviation so in our case if I
1:54:23was to take the average and then
1:54:26subtract this standard deviation along
1:54:28with taking the average and then adding
1:54:31the standard deviation around 70% of the
1:54:35salaries are going to be between 75,000
1:54:37and 170,000 but what if we wanted to be
1:54:40more precise about finding say something
1:54:42like where does 50% of the data actually
1:54:45fall well we can use quartiles in this
1:54:49case specifically calculating the first
1:54:51and third quartile here's a graph that I
1:54:54did from my python course which when you
1:54:56get done with this course feel free to
1:54:58check it out but anyway it looks at the
1:54:59salary distribution of data analyst
1:55:01United States has this histogram right
1:55:03here very similar to what we plotted
1:55:04previously in Excel but in it I'm able
1:55:06to plot out cortile one where the
1:55:08quartile one starts and then quartile 3
1:55:11where that one starts so between this
1:55:13quartile 1 and quartile 3 marker lines
1:55:1750% of the data Falls here with this red
1:55:20dotted line being the media again which
1:55:23let's actually get to calculating this
1:55:25so if we want to do something like the
1:55:26cortile we're going to see that there's
1:55:28a few different functions available for
1:55:30this we have exclusive and inclusive
1:55:33we're going to do inclusive first and
1:55:34then I'll show The Exclusive after to
1:55:36basically show how it's different so
1:55:38this takes two arguments the first is
1:55:40the array so I'll put in that range of
1:55:42the M column and then lastly it takes
1:55:46the quartile and we have one for the
1:55:48first quartile two for the median three
1:55:50for the third quartile anyway I have
1:55:52these values over in the U column so
1:55:54I'll just select that and use that for
1:55:57this and for the second quartile we're
1:55:59seeing that basically as a just red
1:56:01that's also equal to the median now I'm
1:56:04going to go ahead and get rid of these
1:56:05Min and Max CU we can also use that by
1:56:08with our quartile function and I'm going
1:56:11to go ahead and drag and drop this up
1:56:13and then also below so what we can see
1:56:17from this with this first and third
1:56:18quartile is that around 50% of the data
1:56:20Falls between 90 ,000 and
1:56:23150,000 so frankly when it comes to
1:56:26using quartiles like here and standard
1:56:29deviation I find myself more gravitating
1:56:32towards quartiles anyway what about that
1:56:34other quartile function specifically
1:56:37that one around exclusive values well
1:56:40once again I can select the array that
1:56:42we're going to use we're going to use M
1:56:44and then finally the quartile itself now
1:56:47notice for this one this one doesn't
1:56:49have a value of 0 and four that you
1:56:52actually can put in for the Min and Max
1:56:54it's exclusive so it excludes those
1:56:57outliers basically of the Min and Max so
1:57:00specifying that column next to it when I
1:57:03actually drag this down we can see that
1:57:05the Min and Max AR provided in this but
1:57:08it's the same values for that se first
1:57:10second and third quartile if you notice
1:57:12here we get this numb error and as we
1:57:15inspected when going through this
1:57:16formula zero and four were not available
1:57:20to actually input into the formula so
1:57:22any time you're inputting things into a
1:57:24formula that doesn't necessarily exist
1:57:26you're going to get this numb error all
1:57:28right the last function to investigate
1:57:30is the mode and this returns a vertical
1:57:33array of the most frequently occurring
1:57:35or repetitive values in any array in our
1:57:38case we'll once again provide column M
1:57:41and surprisingly we find that 90,000 is
1:57:43one of the most repetitive values if we
1:57:45go back to that histogram we plotted
1:57:47earlier we can see that the largest line
1:57:50right here with a value of 19 25
1:57:52occurrences occurs between 87,000 and
1:57:5593,000 so this makes sense on the 90,000
1:57:58being the
1:58:01mode so let's get into some data
1:58:03analysis Now by actually ranking the
1:58:05average salaries of these different job
1:58:08tiles I'm going to go ahead and hide the
1:58:10columns v through R now in order to rank
1:58:13the salaries of the different job tiles
1:58:15that I have this list here for you where
1:58:17you need to First calculate the average
1:58:19salaries of each of these job titles so
1:58:21for this we're going to be using as last
1:58:23time average if first we need to specify
1:58:26the range that we're going to be
1:58:28basically running that if on not
1:58:29necessarily the values but the range of
1:58:32the job titles next we need to provide
1:58:34the criteria for this we'll provid it of
1:58:36data analyst which is in W2 and then
1:58:39finally the actual average range of
1:58:42column M dragging this all the way down
1:58:46we have our different averages for all
1:58:47the job tiles one note real quick in
1:58:49future lessons we're going to be jumping
1:58:51into using
1:58:52median to evaluate these job tiles cuz
1:58:54personally I like that more but that's a
1:58:56slightly more complex problem so we're
1:58:58going to stick simple for now anyway
1:59:00with these advertisers we can now
1:59:02actually rank it and this Returns the
1:59:05rank of a number in a list of numbers it
1:59:07size relative to other values in the
1:59:10list so first we need to put in the
1:59:13number that we want to rank in this case
1:59:15we're want to do that of data analyst
1:59:16and then from there they have the ref or
1:59:18the reference array in this case we're
1:59:20going to provide it right here from X2
1:59:23all the way down to X11 now I can change
1:59:26this from descending to ascending but
1:59:28I'm going to keep it how it is now I'm
1:59:30going to drag and drop all the rest of
1:59:31these and we had a little bit of an
1:59:33issue CU we have repeating numbers right
1:59:35here it's obviously because I didn't
1:59:37lock my cells appropriately so selecting
1:59:40this range that I want to actually lock
1:59:42and pressing F4 go ahead and lock that
1:59:45and then we'll drag and drop this again
1:59:47hopefully this works this time and boom
1:59:49we have all these ranked from highest to
1:59:51lowest we can see business analyst or
1:59:53some of the lowest thata analyst not far
1:59:55behind and Senior data scientist has the
1:59:57highest I'm going to take this one step
2:00:00further I'm going to highlight
2:00:01everything from job title down to the
2:00:03bottom salary for software Engineers
2:00:05going to go into insert in here and go
2:00:07to recommended charts and basically the
2:00:09first one that pops up this clustered
2:00:10bar chart I'm going to insert in and I
2:00:13can just change the salary up here by
2:00:15double clicking in here and I put
2:00:16average salary of data science jobs and
2:00:20there we have some data analysis is
2:00:22actually viewing these one minor touch
2:00:24to this I really don't like how these
2:00:25are unordered right now so I could
2:00:27actually go up here select these three
2:00:29titles right here and then under the
2:00:31Home tab select I want to actually
2:00:33filter it and then order this rank from
2:00:37well we'll say largest to smallest one
2:00:39note you may not have been able to see
2:00:40it but it actually rearranged the data
2:00:42inside of our data set that's not a big
2:00:44deal for me I'm not caring too much but
2:00:47that is something that will be effective
2:00:48whenever you do this anyway with this we
2:00:50can see things like senior roles or
2:00:52getting paid the most and things like
2:00:54analyst are sometimes getting paid the
2:00:56least compared to these all right you
2:00:58now have some practice problems to go
2:01:00into and thus practice your skills with
2:01:02these statistical functions after that
2:01:04we're going to be jumping in the next
2:01:05lesson into arrays which is a super
2:01:08powerful feature sort of new to excel in
2:01:10the past few years all right with that
2:01:12I'll see you in the next
2:01:16one we're going to be now shifting gears
2:01:19and jumping into a more advanced topic
2:01:22of arrays and with arrays what you can
2:01:24do is typing a formula in a single cell
2:01:27we can use this to fill in cells below
2:01:30it or cells to the side of it all with
2:01:32one single formula so we're going to be
2:01:34slowly working up to an easy then a
2:01:37medium and then a hard problem and how
2:01:40to use these first up with the easy one
2:01:42we're going to go through and basically
2:01:44identify all the unique job titles and
2:01:47then go through and actually sort it
2:01:49alphabetically using arrays next we're
2:01:51going to move into to our median problem
2:01:53of calculating the median salary if you
2:01:56recall back to our last lesson we were
2:01:58calculating the average salary based on
2:02:00a job tile well we can use a raise to
2:02:02calculate the median and then finally
2:02:05one of the most hardest problems we're
2:02:07going to get into actually looking at
2:02:09based on the month how many different
2:02:12jobs were submitted during that month
2:02:15and before this we'll be using the Su
2:02:17product formula and a combination of
2:02:19other ones using arrays for this be
2:02:22using the arrays formula Excel
2:02:26workbook now before we jump into those
2:02:29problems we need to First understand
2:02:31that there's actually two different
2:02:33types of arrays we're going to start
2:02:35with the first one of modern dynamic
2:02:36arrays which we've seen before and with
2:02:39this what we can do is using a formula
2:02:41we can specify a range to identify and
2:02:44then whenever we press enter B2 to B5
2:02:46it's going to actually fill in with all
2:02:48these we can see that it's modern or
2:02:51dynamic because it has this Shadow
2:02:54around the edge if I select any of the
2:02:56other ones and not the core one where
2:02:58this one's actually highlighted when the
2:03:00other ones these are grayed out taking
2:03:03this a step further with array
2:03:04multiplication we can actually go in and
2:03:07multiply this column one of A2 to A5 and
2:03:11multiply it times B2 to B5 anyway in
2:03:15this sequence you can see that it goes
2:03:16down 1 * 1 is 1 whereas 4 * 4 is well 16
2:03:20anyway that's modern dynamic arrays
2:03:22classical arrays let's say we want to do
2:03:24the same thing in this case well we're
2:03:26going to have to go about it a little
2:03:28bit differently we need to select all
2:03:30the cells that we want to fill in first
2:03:32is a very key concept to get for right
2:03:34first then from there we can start
2:03:36entering our formula so I put in equal
2:03:38in this case we want to do the same
2:03:39array multiplication I'll take A2 to A5
2:03:43times it time B2 to B5 now whenever I am
2:03:48done with this and I want to actually
2:03:50execute this I don't just press enter I
2:03:52have to press contrl shift enter and
2:03:54then it fills in the array notice it's
2:03:56not grayed out around the edges like
2:03:58this as a shadow this one does not do
2:04:01that and all of the different formulas
2:04:03are now filled in below this and you'll
2:04:05notice that there's a curly bracket
2:04:07around this this was used prior to
2:04:10around
2:04:112020 and so you may come into contact
2:04:15with Excel spreadsheets that have this
2:04:17and if you don't know about it if you
2:04:18come into here and say you want to like
2:04:20mess with this formula and you press
2:04:22enter you're going to get an error
2:04:23message but now let's say we have some
2:04:26additional values in it we'll say We'll
2:04:27add five to each to the bottom of these
2:04:29if I wanted to adjust this array if I
2:04:31came in here and then change this to six
2:04:34for both the bottom and the top and
2:04:37press control shift enter it's only
2:04:38going to adjust the ones that were
2:04:40previously selected so now if I want to
2:04:42include this bottom row right here for
2:04:43modern dynamic arrays it's pretty easy I
2:04:45can just come in here and adjust this to
2:04:47six and this is done however for
2:04:50classical arrays or classic arrays not
2:04:53classical I have to actually select all
2:04:56these different cells and then go in and
2:04:58actually enter the formula that I want
2:05:00to enter if I try and press enter it's
2:05:02going to give me an error message and I
2:05:04realize okay I have to press control
2:05:06shift enter and it'll actually fill in
2:05:08anyway the main point of this is
2:05:09classical arrays or a mess we're going
2:05:10to be focusing on Modern and or dynamic
2:05:12arrays for the remainder of this course
2:05:14but you need to be aware of classical
2:05:16arrays in case you encounter them in the
2:05:18wild
2:05:21so jumping into our data analysis we're
2:05:23going to be focusing with the data set
2:05:25that we've been focusing on before and
2:05:26I've hidden any columns that I don't
2:05:28feel are relevant for our future
2:05:30analysis that we're going to do anyway
2:05:32the first thing we're going do is find
2:05:33the unique job titles and for this we
2:05:36can use the unique function and this
2:05:39Returns the unique values from a range
2:05:41or array so the first thing we need to
2:05:43do is actually put in the array itself I
2:05:46don't want to actually select this
2:05:47column A because I don't want this job
2:05:48title short to appear so I'm going to
2:05:50select A2 and then press control shift
2:05:53down to select all the way to the bottom
2:05:56I'm going close this parenthesis and we
2:05:58have all the different unique titles in
2:06:00there now I want to get the sorted job
2:06:03titles out of this so as you guess we're
2:06:05going to use the sort function and for
2:06:08this all we really need to do is specify
2:06:09the array now if you notice from this
2:06:12one whenever I went ahead and selected
2:06:13it it specifies that R2 pound and that
2:06:17basically says that hey there's an array
2:06:20basically formula inside side of R2 we
2:06:23want to extract all the contents of that
2:06:25using R2 pound and so that's going to
2:06:29work to be able to provide us all those
2:06:30values and then it's going to sort it in
2:06:32this case we have it sorted in
2:06:33alphabetical order one thing I haven't
2:06:35called out both times is these are once
2:06:36again dynamic or modern arrays you can
2:06:39see that gray box around each of these
2:06:42but just to show this also works by
2:06:44specifying R2 to R11 it's going to
2:06:48provide us the exact same results but I
2:06:50really like the shorthand nomenclature
2:06:52of the R2 hashtag
2:06:56sign now we're going to get into
2:06:58calculating the median salary and if you
2:07:02recall back to our last lesson on
2:07:04statistical functions we went through
2:07:06and calculated the average salary for
2:07:09each of these job titles using an
2:07:12average IF function but as it discussed
2:07:15last time when comparing something like
2:07:16the average to the median the average in
2:07:19this data set is slightly higher due to
2:07:22those basically outliers of those High
2:07:25salaries around 960,000 so we want to
2:07:28use median so what are we going to be
2:07:32eventually calculating and now that's
2:07:34this table right here where we sorted
2:07:37our business or job titles themselves
2:07:40and then we go into actually calculating
2:07:42the median salary based on these
2:07:45different job titles from our data set
2:07:47now there's a pretty complex formula
2:07:50going into here so because of this we're
2:07:53actually going to break it down step by
2:07:55step by step going through each columns
2:07:58explaining how this actual process works
2:08:00in order for us to get to this final
2:08:02value for this we're going to be doing
2:08:04it for data analyst only as we can see
2:08:06the final value we're going to get to is
2:08:07990,000 which over here which our final
2:08:10results 90,000 so I'm going to go ahead
2:08:12and delete this to actually start with
2:08:14now we need to look for two separate
2:08:16conditions the first one we need to look
2:08:19to find do the job titles here actually
2:08:23match up to this value here of data
2:08:27analyst and this provides booing values
2:08:30back where we get to this value down
2:08:32here for true as expected in row 16 we
2:08:34have data analyst now if we scroll down
2:08:37further we can see that our next data
2:08:39analyst job doesn't have a salary for IT
2:08:43these type of things will throw off our
2:08:45final median function that we're going
2:08:47to actually be calculating and so we
2:08:49need to basically filter it out as well
2:08:51well so with the salary dat data set
2:08:54selected I'm going to then go through
2:08:55and filter this basically not equal to a
2:08:59blank value and as expected we're
2:09:01getting false values for these blank
2:09:03ones now similar to what we saw in the
2:09:05intro in arrays where we were
2:09:07multiplying different arrays together
2:09:10we're going to do the same thing here
2:09:11with these bolean values for this I'm
2:09:14taking that formula and wrapping it in
2:09:15parenthesis it needs to be in
2:09:16parenthesis in order to execute properly
2:09:19for the we contains that analyst and
2:09:22then the second condition that the
2:09:23salary can't be blank whenever we
2:09:26multiply these two Boolean values
2:09:28together we get returned back either a
2:09:31zero or one and the only way we get a
2:09:33one back is if both these values are
2:09:37true which is the condition we want to
2:09:38meet now for zero or one values we can
2:09:42actually see if we did an if statement
2:09:44here if we did a logical test of zero
2:09:47what is it going to return whether true
2:09:49or false so for Z returns false and for
2:09:53one we'd expect to turn true anyway we
2:09:55don't want to necessarily return true in
2:09:57this case we want to return the salary
2:10:00that corresponds to that Row in the data
2:10:03set so I'm going to go ahead and delete
2:10:05this so for this we're going to start
2:10:06with that if function itself then I want
2:10:09to place all the different contents that
2:10:11we saw in that previous V column now we
2:10:13want to return the salary which are
2:10:15these contents right here so I'll be our
2:10:18value if true and then if false we just
2:10:20want it to be FAL false which we can
2:10:22just leave blank so now scrolling down
2:10:24we can see that we have nothing but
2:10:26those values for data analyst scroll
2:10:28over just to confirm 129 yep that
2:10:30analyst all right the last step we need
2:10:33to go ahead and put inside of our median
2:10:35formula all those contents that we had
2:10:38before that entire if statement itself
2:10:41to evaluate so that array that it's
2:10:42going to basically find out for all
2:10:45those salary for data analyst and it's
2:10:47return back the median salary now this
2:10:49also works for other function so let's
2:10:52say we wanted to use the mode we want to
2:10:55use a mode if condition they don't have
2:10:57this available so we could just plug
2:10:59this inside of mode and then running
2:11:02this we can see that well the most
2:11:03common value for thata analyst
2:11:05apparently also the median of 90,000 so
2:11:07going back to our data sheet let's
2:11:09actually go through and stepbystep
2:11:11calculate it for each of these different
2:11:14sorted unique job tiles that we did
2:11:15previously and we're going to be
2:11:17building this step by step how i'
2:11:18normally build a for so the first thing
2:11:20we going to look for the job titles
2:11:21itself do they match to that business
2:11:23analyst so selecting column A2 and then
2:11:26control shift down to select all the
2:11:28contents on the cell we want to see if
2:11:31that's equal to this business analyst
2:11:34rule right here and now remember we're
2:11:35going to be dragging these Downs do an
2:11:37autofill so we need to be particular
2:11:39about how we lock these cells
2:11:41specifically we do need to lock these
2:11:43values right here and just for safe
2:11:45measure I'm going to lock the column of
2:11:48this one okay pressing enter all right
2:11:51we we have our array back looking for
2:11:53business analyst and we can see that
2:11:55it's working by what we see down here in
2:11:57row 84 so let's actually do that array
2:12:00multiplication by now filtering out
2:12:03salary that doesn't have values or
2:12:06blanks so we're going to put another set
2:12:08of parentheses next to it we'll put in
2:12:10our salary data and that it's not equal
2:12:13to blank running this we confirm that
2:12:16the first value of business analyst that
2:12:19has a salary has a one now we need to
2:12:21wrap this all inside of an if to
2:12:24basically return instead of that one we
2:12:25want it to return the salary itself so
2:12:29for the value of true I'm going to put
2:12:30in the selection of the salary yearly
2:12:33running this we confirm this is again
2:12:36correct looking at row 180 almost done
2:12:39just now need to wrap this all inside of
2:12:42a median function and Bam
2:12:4685,000 and hopefully we actually locked
2:12:50all the cells properly dragging it Down
2:12:53Bam looks like we got all our things and
2:12:55we slightly messed up our formatting
2:12:57here so I'm going to go ahead and put a
2:12:59thick outside border on again to make
2:13:01that right again all right so that's how
2:13:03you basically transform any function in
2:13:07Excel that doesn't have that you know
2:13:09count if or average IF function or
2:13:12capability into other
2:13:16functions now moving into probably the
2:13:19most complex example that we're going to
2:13:20be be using not only this lesson
2:13:22probably in the entire course so if you
2:13:24get around this you're going to be good
2:13:26to go for the rest of the course anyway
2:13:28what we're trying to look at here is the
2:13:30count of job postings based on the month
2:13:34that it was posted in and we're going to
2:13:37be using the sum product function for
2:13:40this now sum product is not anything
2:13:43that you should be afraid of basically
2:13:44before we were doing whenever we were
2:13:47doing the intro and we were talking
2:13:48about array multiplication how went
2:13:51through line by line based on this and
2:13:54we have our values of 1 4 9 16 and 25
2:13:57line by line well if we were to do the
2:14:01sum
2:14:02product of the values in column A along
2:14:06with the values in column B we're going
2:14:08to get 55 which when we look at the sum
2:14:13of these values here we can see that it
2:14:16is 55 so it's a sum of the product of
2:14:22the arrays so getting back to our
2:14:25example that we're going to be solving
2:14:26we're trying to aggregate it by these
2:14:28names of these months if we actually
2:14:30scroll over to the data set itself the
2:14:33job posted date is in a date time format
2:14:37so similar to the last example I'm going
2:14:39to be walking you through column by
2:14:41column by column on how we get to this
2:14:44final value that we're going to be
2:14:46eventually putting into our table here
2:14:49to thus calculate these values for the
2:14:51counts per month so we go ahead and
2:14:53clear these cells to start and we're
2:14:54going to start first by we want to
2:14:57extract out the month from this job
2:15:00posted date column so for this we can
2:15:03use the text function which we're sort
2:15:06of jumping ahead because we'll be doing
2:15:08text functions upcoming lessons but
2:15:10there a good little sneak peek anyway we
2:15:12can plug in here something like a date
2:15:15time value and then from there we wanted
2:15:18to Output what is the format text for
2:15:21well I know that if we do three M's it's
2:15:25going to provide me the shorthand month
2:15:28of this additionally if I do fourms it's
2:15:31going to provide me the lonand month of
2:15:33this and there's a host of different
2:15:35format codes that you can provide sh by
2:15:37this table here when I'm looking it up
2:15:39in something like perplexity that says
2:15:41that hey if you provide certain things
2:15:42like if I provided a Double Y it's going
2:15:45to provide the two-digit year and so on
2:15:48for other values you look this up in
2:15:50something like chat GPT anyway get back
2:15:52to this example itself I want to
2:15:53actually autofill this all the way
2:15:55through it's not around any other
2:15:57columns that I can actually autofill all
2:15:59the way down and I don't want to sit
2:16:00here and drag it all the way so what I
2:16:02can do is select the column itself and
2:16:04then when it has these basically four
2:16:06arrows I can then drag it where I want
2:16:08I'm going to drag it right next to here
2:16:10and then now actually autofill it all
2:16:12the way down now that I have it complete
2:16:14I'm just select this column again make
2:16:16sure I have those 4 hours again and drag
2:16:18it back to the column it needs to be now
2:16:20seeing what you did here you probably
2:16:21like Luke can't you use something like a
2:16:23count if in order to calculate the
2:16:25months now using this and you'd be
2:16:28correct with that remember call back for
2:16:30the count if s we can provide a criteria
2:16:33range in this case we're going to
2:16:34provide it column V and then for the
2:16:37criteria itself will provide the actual
2:16:40month and then actually dragging and
2:16:42dropping this all the way down once
2:16:44again my formatting got messed up so I'm
2:16:46put that thick outside border back on
2:16:48there anyway these values here for what
2:16:50we're going to get finally are the same
2:16:54and so you really could stop this lesson
2:16:56right here and if you want to do this of
2:16:58creating a new column and then just
2:16:59using count ifs you can do that but this
2:17:02is a lesson on arrays so we're going to
2:17:04get more complex with this in order how
2:17:06to use the arays in order to actually
2:17:08calculate this without having to create
2:17:10these extra columns so I'm going to go
2:17:11ahead and hide this cuz we're not going
2:17:13to use it so before we can actually
2:17:15summing up we need to get an array of
2:17:17all the values that we'll say equal to
2:17:19January so so we'll start by creating
2:17:21that text function it's going to be
2:17:22slightly different before cuz we're
2:17:24going to be making it out of array we
2:17:25want to actually select all the values
2:17:28from H2 all the way down to the bottom
2:17:31we want to then go ahead and lock it we
2:17:33want it to be evaluating for that long
2:17:36month name so four lowercase M's and
2:17:40when I want to check if it's equal to in
2:17:42our case we're looking for January we'll
2:17:44look up here at this U2 or U1 I got a
2:17:47typo up there update that to U1 anyway
2:17:49we now have okay that this value is true
2:17:52right here and we can tell from row 11
2:17:54that this is in January it is true so
2:17:57it's working out just fine so now if I
2:18:00tried to actually run a su product which
2:18:02is what we're finally trying to do on
2:18:05all the contents of this array itself
2:18:08we'll do W2 uh hashtag we're going to
2:18:11get back zero because this isn't in the
2:18:14format that we want we actually need to
2:18:15convert this unfortunately although it
2:18:18is on the back end is zero and on the
2:18:20actual functions themselves can't
2:18:22actually calculate it so we can do this
2:18:25by basically converting it and the first
2:18:27thing we can do actually is just we'll
2:18:29put one negative sign and then I'll put
2:18:32in that W2 hashtag and what this does is
2:18:35it negates the Boolean values so
2:18:37basically true which is normally a one
2:18:41it negates it and makes it negative one
2:18:43zero a negative Z is negative anyway we
2:18:46need to actually apply two negative
2:18:48signs CU we don't want it to be negative
2:18:50one we want it to be positive one so
2:18:53doing this one more time we now have
2:18:55positive ones in there so now we are
2:18:57using some product because some product
2:19:00I feel are better with arrays but we
2:19:02could use in this case where it's a
2:19:04single array we could use actual just
2:19:08sum itself I didn't want to show that
2:19:10and we get that value of 3102 which
2:19:13correlates to what I expect as the value
2:19:15but we're going to use some product
2:19:17because as you'll find out in future
2:19:19lessons we're actually going to be
2:19:20modifying it even further what's inside
2:19:23of here and so we need this Su product
2:19:25in order to do those anyway we get the
2:19:27same value of
2:19:293,12 so going back to our data tab let's
2:19:32actually calculate this fully for all of
2:19:35these different values walking through
2:19:36it step by step by step as we do
2:19:38previously we're going to start with our
2:19:40text function and we want to look at
2:19:43that job posted date column I'm going go
2:19:46ahead and lock all those cells it's very
2:19:47important for this going be dragging and
2:19:48dropping that down and remember for the
2:19:50format text to this we want it to be
2:19:52four lowercase M and in this we're
2:19:56checking whether it's equal to this
2:19:59value here of V2 which is January and
2:20:01I'm going to go ahead and actually lock
2:20:03just that column pressing enter to make
2:20:05sure it goes correctly yep we got True
2:20:07Value here for our row 15 value first
2:20:10thing we want to do is do that double
2:20:13negation which we need to actually wrap
2:20:15these in this whole formula itself in
2:20:18parenthesis in order to get our Z and
2:20:21one values and then finally we're going
2:20:23to wrap this all once again in Su
2:20:26product putting that closing parentheses
2:20:29on there pressing enter get 3102 and
2:20:32then doing autofill all the way down we
2:20:34have all our values once again format is
2:20:37messed up I'm going put that thick
2:20:38outside border now the other reason why
2:20:40we're using some product in this case is
2:20:43because in older versions of excel
2:20:45before we had these uh modern dynamic
2:20:48arrays some is not going to to be able
2:20:51to work over a raise and you actually
2:20:52have to use some product so this allows
2:20:55us also to have a safe way to
2:20:59calculate using arrays and then give it
2:21:01to people that may be archaic and have
2:21:04older versions of excel all right it's
2:21:06your turn now to jump into some practice
2:21:08problems to get more familiar with
2:21:09working with arrays inside of formulas
2:21:13in the next lesson we're going to be
2:21:14getting into probably I think one of the
2:21:16most funnest types of functions lookup
2:21:19functions like vlookup and X look up and
2:21:21things like that which are super helpful
2:21:23for data analysis all right with that
2:21:26I'll see you in the next
2:21:30one lookup functions are one of the most
2:21:34I'd say funnest functions whenever
2:21:37you're learning to be a freak in the
2:21:38sheets specifically we're going to be
2:21:40focusing on three different lookup
2:21:42functions vlookup H lookup and X lookup
2:21:46V and V lookup stands for vertical H and
2:21:49H lookup stands for horizont and x and x
2:21:51look up just uh they wouldn't be
2:21:53different in order to learn about these
2:21:54functions we're going to be performing
2:21:55some data analysis and if you recall
2:21:57back from our math and statistical
2:21:59functions lessons we found out what the
2:22:02median Min and Max salaries were but for
2:22:06the things like the Min and Max what
2:22:08were those different job postings that
2:22:11correlated to that well based on the
2:22:13structure of our data set we can use the
2:22:14vlookup and also X lookup functions in
2:22:18order to find this out now because of
2:22:20the structure of our data we're going to
2:22:22have to do something different in order
2:22:23to implement H lookups and for this
2:22:26we're going to be able to get out or
2:22:28extract out horizontal type data we're
2:22:31going to basically transpose it into a
2:22:33vertical format using H lookup but if
2:22:35there's anything you remember from this
2:22:36lesson it's that of X lookup this one is
2:22:40the most dynamic and flexible and how it
2:22:42can be used and we're going to be doing
2:22:43in a final example using this in order
2:22:46to bucket our salary data set allowing
2:22:50us to categorize it into different
2:22:52ranges and whether it has data or not
2:22:56all using xlup for this we can start
2:22:59using the lookup functions workbook we
2:23:02have two main tabs in this data and
2:23:04dataor 2 Data ones where we're going to
2:23:07start in first for this section on
2:23:09vlookups so for this we're going to be
2:23:11using that job posting data set I've
2:23:12hidden any unnecessary columns and we're
2:23:14going to be filling in this table right
2:23:16here so what I'm trying to do with this
2:23:18is fill in based on this Min as you can
2:23:21see the formula for Min the formula for
2:23:23max and the formula for median where we
2:23:25actually calculate this from the Sal
2:23:27year average column we want to then
2:23:29extract out based on these values the
2:23:31company name a job title associated with
2:23:34it and then the country associated with
2:23:38it so we're going to start with V lookup
2:23:41first and V lookup looks in a vertical
2:23:44type format specifically it says it
2:23:46looks for a value in the leftmost column
2:23:48of a table and then returns a value in
2:23:51the same Row from a column you specify
2:23:54so for the first value of this we want
2:23:56to provide the lookup value in this case
2:23:58we want to look up 15,000 from that
2:24:01salary year average column then from
2:24:03there we need to provide the table array
2:24:05now remember for this it needs to be the
2:24:07leftmost column of the table and we want
2:24:11to get columns M and O I'm going to
2:24:12select column o because if we start at M
2:24:15and try to go down it's going to mess up
2:24:17cuz there's blank in it so I'm just
2:24:18going to do control shift over and then
2:24:20control shift down to select all the
2:24:22data and then change this a column to M
2:24:25instead the next thing we need to
2:24:27specify is the column index number and
2:24:30right now we're in column M so that
2:24:33would be the First Column so MN o we're
2:24:37in the third column you can imagine if
2:24:39we have a buttload of columns what kind
2:24:42of problems are going to run into so
2:24:44we'll get to that when we get to it okay
2:24:46now they have a range lookup we're going
2:24:48to leave that blank for the time being
2:24:50we're just going to execute this formula
2:24:52as is and for this we're getting an NA
2:24:55error if we actually click into it value
2:24:57not available error and why is that well
2:25:01if we actually go back to that vlookup
2:25:02function in the definition that it
2:25:05provides for it the last statement is by
2:25:07default the table must be sorted in
2:25:10ascending order right now our salary
2:25:14values are not sorted so it's having
2:25:16issues going through it and actually
2:25:18finding that 15,000 because it's
2:25:20unsorted anyway we're not going to
2:25:22actually sort that table that's going to
2:25:24be too much work we can actually now go
2:25:26into that fourth parameter of range
2:25:29lookup and instead of doing an
2:25:31approximate match which was the default
2:25:33we're going to do an exact match by
2:25:35providing false in that case we find
2:25:37that net two Source Inc is the company
2:25:39name of the job with 15,000 now I want
2:25:43to autofill for this but we need to
2:25:45actually lock some cells real quick so
2:25:47I'm going to lock this right now by
2:25:48pressing F4 then from there we'll drag
2:25:50it down now one thing to note on vlookup
2:25:53X lookup and also H lookup this is just
2:25:55going to return the first value so in
2:25:57this case of this 115,000 it says it's
2:26:00Volt Technical Resources however I do a
2:26:02contrl f of
2:26:04115,000 we'll find that yes it's at row
2:26:0719 for Volt Technical Resources the
2:26:09first one that provides but it's also in
2:26:11row 42 with northr Gan so it's only
2:26:14providing that first match now what
2:26:17happens if we wanted to next get things
2:26:19like the job title or the country itself
2:26:23well if I were put in the first two
2:26:24values the lookup value and then the
2:26:26table array what will we put for the
2:26:28column index number remember in vlookup
2:26:31the leftmost column of the table itself
2:26:34is what we're going to be looking up but
2:26:36however columns A and K are even well
2:26:39more left of that table so unfortunately
2:26:42we can't use vck up for this but we will
2:26:45be using X lookup for this that's why
2:26:47I'm going to recommend it over vlookup
2:26:49but I think you guys start at the Bas
2:26:50phics
2:26:52first however before we get into that
2:26:55we're going to now shift gears and cover
2:26:57H look up in order to look up values in
2:27:00a horizontally oriented table this case
2:27:04this is horizontally oriented because we
2:27:06have things like the months across the
2:27:09Horizon if you will and then we have in
2:27:12the columns in the column standpoint we
2:27:14have the job titles of the different
2:27:16ones of data analyst and your data analy
2:27:18so on now the data in this table is
2:27:21calculated using the data from the data
2:27:24tab in order to get the counts of months
2:27:26and you've previously seen this in the
2:27:28last lesson where we went in that hard
2:27:31example of some product where we now go
2:27:33through and do some array multiplication
2:27:36in order to find out the different
2:27:37counts for the job titles based on a
2:27:40month anyway for this H look up we want
2:27:42to look up based on a month what is the
2:27:46associated job count for a specific job
2:27:49type
2:27:50so let's say we want to just look at
2:27:52that may column well we can put in h
2:27:55lookup and this looks for a value in the
2:27:59top row of a table or array of values
2:28:02and Returns the value in the same column
2:28:04from a row you specify so only selects
2:28:08from that top row for this we provide a
2:28:11lookup value in this case let's say
2:28:12we're looking up January then from there
2:28:15we provide the table array itself we can
2:28:19go and just select this data now I could
2:28:21technically I could select all this data
2:28:23because it's just going to go to the
2:28:25associated column associated with this
2:28:28so that we included row a doesn't really
2:28:30matter then from there we want the row
2:28:33index number what value do we want from
2:28:37this January do we want data analyst
2:28:39senior data analyst senior data
2:28:40scientist so we can just count down what
2:28:42we want we'll start with data analyst
2:28:44first so we'll put in that's the second
2:28:47row in this so let's try to enter this
2:28:50and for this we get 753 which if you go
2:28:53back to this we're doing Jan A1 through
2:28:56M7 and then the second one so why are we
2:28:59getting
2:29:00753 well once again this has to do with
2:29:04the range lookup we're doing an
2:29:07approximate match similar to vlookup it
2:29:11expects that these values for that top
2:29:14row are in in this case alphabetical
2:29:17order in order to perform that
2:29:19approximate maass
2:29:20these aren't in alphabetical order
2:29:21they're actually in chronological order
2:29:23so instead we need to specify false now
2:29:27running it we get the correct value of
2:29:29982 now we can also apply this to a
2:29:31situation where maybe we want to
2:29:33transpose these values into this new
2:29:36table that we have here on month and
2:29:38count and then up here I'm going to also
2:29:40just specify what we're looking at we're
2:29:41going to look at data analyst now with
2:29:44our H lookup we're going to be providing
2:29:46that lookup value the table array and
2:29:49then the row index number say if we
2:29:52wanted to go in here instead of data
2:29:53analysts we wanted to look at data
2:29:56engineer instead how can we get this to
2:29:59update well we can use another function
2:30:02for this specifically we can use the
2:30:04match function for this and this Returns
2:30:07the relative position of an item in
2:30:09Array that matches a specified value in
2:30:12a specified order so in this case I want
2:30:15to look up data engineers in the array
2:30:19from a 2 to A7 it's providing me a one
2:30:23cuz it's not it's doing the approximate
2:30:25match again once again they're not in
2:30:27alphatic so I have to specify exact
2:30:30match using zero okay and now I get data
2:30:33engineers in the fifth place I'm also
2:30:35going to move this column over and make
2:30:37this a little bit bigger going back into
2:30:39that H lookup that we're going to use
2:30:40for this we're going to provide that
2:30:41lookup value which we want to actually
2:30:44lock by pressing F4 then we're going to
2:30:46provide the table once again I said you
2:30:48can select that a column if want or not
2:30:50we're going to lock all these values as
2:30:53well because we'll be dragging it down
2:30:55from there we'll be providing the row
2:30:57index number which we've calculated
2:30:59right here in this P based on that match
2:31:01that we're performing want to lock this
2:31:03as well and as far as the range lookup
2:31:05well we want to do exact match running
2:31:08this we get an NA error because I was
2:31:10silly and the lookup value we want to
2:31:12actually do is for the month of January
2:31:14not the data engineer actual lookup
2:31:17confusing this with h lookup sorry about
2:31:18that so we'll put in 03 for this instead
2:31:21and then running it and now we're
2:31:22getting back to 236 which is not that
2:31:24Engineers thing we're one off and this
2:31:27has to do with how we did our match up
2:31:30here which specified A2 to A7 basically
2:31:33we're counting down from the second one
2:31:36where in h lookup we included all the
2:31:39way up to that first row so this is just
2:31:42a simple fix by changing this one up
2:31:43here to A1 and now our values update
2:31:46appropriately and then I can go ahead
2:31:48and just drag and drop this all the way
2:31:50down and once again going to get into
2:31:52some troubleshooting because this is all
2:31:54the same values and that's because I
2:31:56fully locked this actual month number
2:31:59and instead I wanted to press F4 and
2:32:01only lock the column of O now finally
2:32:06getting to the final answer we have it
2:32:09and we can confirm this that data
2:32:11Engineers should have 396 on the
2:32:12December value that's correct and now we
2:32:15can do things like this where I can go
2:32:16in and say hey instead I want to look at
2:32:19Dana analyst and it will update for this
2:32:22instead now once again with h lookup we
2:32:25run into issues like vlookup if there's
2:32:28values Above This top row I can't really
2:32:31think of that any applications that
2:32:33that's applicable in this but it is a
2:32:34limitation anyway this is why we're
2:32:36going to be shifting to the next
2:32:40topic and that is using xlookup to now
2:32:43based on these salaries that we were
2:32:46previously trying to identify
2:32:47identifying a job title and a country
2:32:50associated with it so what is the
2:32:53definition of xlup and this searches a
2:32:56range or an array for a match and
2:32:59Returns the corresponding item from a
2:33:02second range or array by default an
2:33:06exact match is used that's pretty
2:33:08awesome considering all the issues ran
2:33:10to with h look up and V lookup anyway
2:33:13instead of using a single table we're
2:33:14going to be using multiple ranges for
2:33:16this let's get into it first we're going
2:33:18to provide the lookup value which in
2:33:20this case is 15,000 and then we want to
2:33:23provide the lookup array so we need to
2:33:26select this entire M column here for
2:33:28what we want to actually look up but we
2:33:31have these blanks in here so I'm going
2:33:32to just do a trick of selecting the O
2:33:35column selecting all the way down and
2:33:38then from here I'm going to just go in
2:33:39and actually change these values to M
2:33:42instead now we want this to remain the
2:33:45same so I'm going to press F4 to
2:33:47actually lock this now that was our
2:33:49lookup array now we want to get into
2:33:52what return array or where we want
2:33:54actually look to see and that's to the
2:33:56left of this in this job title short
2:33:59column these arrays have to match up in
2:34:02where they are uh where you're selecting
2:34:04them so in this case I selected over
2:34:05here in the second row I need to do the
2:34:07same for the job title then from there
2:34:09pressing control shift down I select all
2:34:12of them once again I'm going to lock all
2:34:14of these by pressing F4 now let's close
2:34:16the parentheses and go ahead and execute
2:34:18it we can see see that data engineer is
2:34:21the lowest paid salary with this 15,000
2:34:25now we can also add in this default
2:34:27parameter in case you can't find a value
2:34:30you can put not found but in our case we
2:34:32made or we calculated this minmax and
2:34:34median from our data set so technically
2:34:37this isn't really necessary anyway let's
2:34:39see what the other job titles are for
2:34:41these Max and median looks like it's
2:34:43data scientist and then the data
2:34:45engineer for the median which is that
2:34:47first one that appears right over here
2:34:48in row 19
2:34:50now doing the same for the country I'm
2:34:52going to go ahead and just copy and
2:34:54paste that formula in that we had from
2:34:56the other cell and I'm going to just
2:34:57adjust this now to use column K instead
2:35:01of column A for the actual return array
2:35:05okay with that updated press enter and
2:35:07we can see Brazil has the lowest one and
2:35:11what is the highest one United States
2:35:12and also the median United
2:35:16States all right we're going to crank
2:35:18this up a notch and now we're going to
2:35:20jump into actually bucketing our salary
2:35:22using X lookup specifically I want to
2:35:25use this table that I've created in
2:35:28order to properly categorize different
2:35:32values based on this so in this case we
2:35:34have this value of 140,000 it's going to
2:35:36fall into our bucket of
2:35:38125,00 th000 there's no data in this one
2:35:40so I want to say no data this one's
2:35:42greater than 200,000 so I want to say
2:35:44greater than 200,000 so for this we're
2:35:46going to be creating a new column column
2:35:49Q and we're going to call it salary year
2:35:53bucket I'm going to go ahead also and
2:35:55cod this column o for the time being we
2:35:58don't really need this for this now
2:36:00technically you already have the
2:36:02requisite knowledge in order to bucket
2:36:03it I could put in a nested IF function
2:36:08similar to below and it has 1 2 3 four
2:36:12five if you will nested ifs to go
2:36:14through and basically check each of the
2:36:16different values as it's going through
2:36:18in order to bucket it appropriately
2:36:20in this case it correctly categorizes it
2:36:23and then if I wanted to I can drag and
2:36:25drop it all the way down but now this
2:36:28sheet is filled with all of these nested
2:36:32if statements this is really going to
2:36:34slow your spreadsheet down so I don't
2:36:37recommend doing this also building
2:36:39something this like this you've now
2:36:41hardcoded in your values into it and
2:36:44what is if you want to change this later
2:36:45you'd have to update all your formulas
2:36:47it's a mess don't recommend doing it so
2:36:49I'm going to select all this control
2:36:51shift down and then just delete it all
2:36:53instead we're going to be using X lookup
2:36:56for this specifically we need to look up
2:36:58the lookup value which is going to be
2:36:59the same one that we did before that M2
2:37:02and then we want to look up the lookup
2:37:04array now I conveniently made this table
2:37:05here that it's providing values at if
2:37:08you will the higher end of the bucket so
2:37:11we're not going to necessarily do an
2:37:12exact match for this we'll get to that
2:37:14in a second anyway now we want to look
2:37:16at what do we want to return the return
2:37:18array which is on the left side of this
2:37:20table that's the values I actually want
2:37:22to return back in that column value if
2:37:24not found is not necessarily applicable
2:37:27so now getting into how we're actually
2:37:29going to match based on these salary
2:37:30buckets based on these values
2:37:32highlighted in this T column right here
2:37:35well we need to do not exact match we
2:37:38need to do exact match or next larger
2:37:42item and this is the value of one
2:37:45basically in this case of this 12850
2:37:48it's going to look for initially an
2:37:50exact match of 128 of 50 and it's going
2:37:53to see that nothing's there so then it's
2:37:55going to look for the next larger item
2:37:58which is that 200,000 so therefore it's
2:38:00going to return as we're going to find
2:38:02out the
2:38:03125,000 to
2:38:05200,000 now I can try to drag and drop
2:38:07this down but I'm going to run into
2:38:08errors because I didn't lock my formulas
2:38:10correctly so I need to go back in lock
2:38:13that s column with F4 and lock that t
2:38:16column with F4 and then I'm just going
2:38:18to autofill all the way down and now we
2:38:21have all of our different job postings
2:38:24bucketed into these different salaries
2:38:27so instead I wanted to go through and
2:38:28actually change this to be
2:38:30150k and then match this to
2:38:33150,000 I go do it and it would update
2:38:35appropriately I also need to update this
2:38:37column as well but now it all updates
2:38:39and it's in one single location so this
2:38:41is really the power of using that X
2:38:44lookup over the ifs in order to perform
2:38:46this type of bucketing all right you now
2:38:48got some practice problems go through
2:38:50and get more familiar with using these
2:38:52different Lookout functions as I said
2:38:54before make sure you're prioritizing
2:38:56understanding that X lookup it's the
2:38:57most powerful but the one caveat to X
2:39:01lookup is that it was introduced around
2:39:04the 2020s so anybody using once again an
2:39:06archaic version of excel Beyond or
2:39:10before this year they're going to have
2:39:11compatibility issues using this so
2:39:14that's why you need to also be familiar
2:39:16with that V lookup and also H lookup are
2:39:19going to encounter them in the while all
2:39:21right with that I'll see you in the next
2:39:22one where we're jumping into text
2:39:27functions now I know this is a course on
2:39:29data analysis but text functions are
2:39:32actually imperative for performing
2:39:35analysis on Text data and for this we're
2:39:39going to be working in this lesson on a
2:39:41data set of job applicants and we're
2:39:44going to take it a step further using
2:39:47text functions in order to analyze
2:39:50specifically for our final analysis we
2:39:52have information on the different skills
2:39:55that each one of these job applicants
2:39:56knows so we're going to be able to
2:39:59perform an analysis to see what are the
2:40:01most common skills from these applicants
2:40:04but before we get to that final analysis
2:40:06we first need to beef up our knowledge
2:40:08we're going to focus on three main areas
2:40:10the first one is text combination we're
2:40:12going to be working to combine different
2:40:15columns into a single column from there
2:40:17we'll move into the second one of of
2:40:19text extraction being able to out of a
2:40:22single column extract multiple values
2:40:25and finally in the third one performing
2:40:28some sort of text search in order to
2:40:31also extract out in this case we're
2:40:33going to be extracting out the state
2:40:36name from an address that contains a
2:40:39city state and area code so for this you
2:40:41can start up by opening up the text
2:40:42functions workbook and in the data tab
2:40:45we have this data set which you haven't
2:40:47seen before it's only about 20 R and
2:40:50includes a list of job applicants now
2:40:53we're not using the full data science
2:40:54job posting data set because a lot of
2:40:56the examples we're going to do in this
2:40:58it would be basically bogged down your
2:41:00Excel spreadsheet so especially how
2:41:02we're going to be implementing these
2:41:04it's really meant to be used for smaller
2:41:07data sets you may be like Luke what ends
2:41:09if a bigger data set and need to clean
2:41:10up the text well that's where power
2:41:12query comes in which we'll be covering
2:41:14in the advanced chapters so stick around
2:41:16for that
2:41:20anyway moving into text combination we
2:41:22want to Target these columns right here
2:41:24f and g we want to combine them into one
2:41:27line to have a single address so I'm
2:41:30going to go ahead and hide this column H
2:41:32for the time being we're going to be
2:41:33putting that full address in column J
2:41:36and this one's pretty simple all we're
2:41:38going to do is text join which
2:41:41concatenates a list or range of text
2:41:44strings using a delimiter the first
2:41:46thing I need to specify is the delimiter
2:41:48how am I going to to separate that
2:41:49street and the city state all I want to
2:41:51do is a space so I'll do that en closing
2:41:54it in double quotes next is ignore empty
2:41:58basically if there was an empty cell in
2:42:01here it would just ignore this and it's
2:42:03not going to input multiple different
2:42:05spaces between it just ignore it so we
2:42:07want to in that case we're just going to
2:42:08put in true the final one is text and we
2:42:12can specify you could do text and then
2:42:14comma and then text to um that's really
2:42:17Vose I don't really like doing that
2:42:19instead I'm just going to select the
2:42:21range of F2 to G2 now we can see that
2:42:25the address is fully concatenated and we
2:42:28can drag it on down and it works for all
2:42:30of
2:42:32it now the opposite of combo is
2:42:35extraction which we going to get into
2:42:36next and in this case we're just going
2:42:38to use a single column and extract out
2:42:41multiple values in this case we have
2:42:43this full name column we want to extract
2:42:44out the first name and the last name go
2:42:47ahead and hide these other columns we're
2:42:48not using
2:42:49in this case we're going to specify the
2:42:51text split and it says it splits text
2:42:55into rows or columns using delimiters so
2:42:59we'll first start by specifying the text
2:43:01which is B2 in this case and then the
2:43:03column delimiter which in our case is
2:43:06going to be that space once again we're
2:43:08going to use that double quotes for that
2:43:10space and then end Double quotes and
2:43:12then this is going to be a dynamic array
2:43:15and it has these two values here now
2:43:18dragging this all down down we see that
2:43:20it fills in for all these different
2:43:21names now we just split text there also
2:43:24could be cases where we maybe want to
2:43:26extract out certain amount of values or
2:43:30certain amount of text from a column in
2:43:32this case we also have our application
2:43:34ID number which is a combination of
2:43:37letters and numbers but as you can see
2:43:38from this there's some values in here
2:43:41that are actually repeating sometimes we
2:43:43want to refer to this the shorthand of
2:43:46this and let's say we only want to get
2:43:47the last three digits of the applicant
2:43:50ID because we know that's always
2:43:52different well in this case we can
2:43:54specify the right function and it
2:43:57Returns the specified number of
2:43:59characters from the end of the text
2:44:01string we specify the text itself and
2:44:04then the number of characters in this
2:44:05case we can just say three and it's
2:44:07going to provide back that 548 we could
2:44:09also just change that to include all the
2:44:11text numbers in case this number gets
2:44:13bigger than that and then go ahead and
2:44:15drag it all the way down
2:44:19now one last one before we get into
2:44:21actually performing that analysis we
2:44:22want to we want to go through and
2:44:24extract out the state from this city
2:44:28state and zip and as you notice from all
2:44:30these they have a common format in that
2:44:33the city has a comma and then the state
2:44:35starts and then there's another comma
2:44:37following that so we're going to be
2:44:39using those basically delimiters if you
2:44:42will in order to identify where we
2:44:44should potentially extract out this
2:44:47state value from where this state these
2:44:49two L twetter value so the approach
2:44:52we're going to use for this is as we go
2:44:54through this is we're going to find the
2:44:55location first of that first Common
2:44:58space before this the next we'll find
2:45:01where it actually ends and then finally
2:45:04well using those values will actually
2:45:05extract out using the mid function that
2:45:09state abbreviation so the first thing we
2:45:11need to do is find that comma and this
2:45:14Returns the starting position of one
2:45:15text string within another text string
2:45:17so in this I'm going to specify that
2:45:19we're the fine text we're going to be
2:45:21looking for is the comma itself and
2:45:24we're going to be looking at within text
2:45:27obviously G2 now we also need to find
2:45:30the second comma in this we can use that
2:45:33find function again specifically we're
2:45:36finding that comma specifying that
2:45:38within text of G2 and now we have the
2:45:42second optional parameter of start
2:45:44number we want to start from nine which
2:45:48is the first one we found this in
2:45:50running this we get nine now the problem
2:45:53here is because we're starting as the
2:45:55exact number that the comma actually
2:45:58starts that's why we're getting that
2:46:00back that value of nine we need slightly
2:46:02actually bigger than nine but anyway
2:46:04we'll fix that in a bit instead let's
2:46:07actually get into extracting out that or
2:46:09at least trying to extract out that CA
2:46:12of this value and then we'll fix that
2:46:13issue in cell R2 so for this we're going
2:46:16to be using the mid function which which
2:46:19similar to that right function is
2:46:21Returns the characters from the middle
2:46:23of a text string given a starting
2:46:25position and length so in this case we
2:46:29want to extract out G2 and we'll provide
2:46:33it the start number of well what's
2:46:35valuable in Q2 and the number of
2:46:39characters and for right now we'll just
2:46:41put in we know we want to extract out
2:46:43two so we're going to put in two now
2:46:45we're running into issues we're only
2:46:47getting back a comma if will and if we
2:46:50actually make this longer to actually
2:46:51zoom in on here we get commas space CA
2:46:55now when providing four and this has to
2:46:57do with right here this value on the
2:47:00start number isn't correct this nine
2:47:03right here is exactly at the comma we
2:47:06need to actually specify for that start
2:47:09number of where the C is and these are
2:47:13all two spaces over so I'm going to come
2:47:15in here and I'm just going to modify
2:47:17this shortly and add two to this this is
2:47:20also going to fix our previous one that
2:47:23we had when finding this of 13 because
2:47:2613 now has all the way over and then
2:47:29finally that mid is fixed we can change
2:47:31this now to back to two now you know me
2:47:34I don't like hardcoding values something
2:47:36like this to and really what we're doing
2:47:40here is we're doing adding two based on
2:47:42the length of the comma and then the
2:47:47space after it so there's two characters
2:47:49in there two this is still that 11 value
2:47:52that we saw before similarly inside of
2:47:54our mid function I don't like doing this
2:47:57two here because States maybe could be
2:47:59more than two so I don't want to hold it
2:48:01necessarily to that so instead I'm going
2:48:03to do R2 minus Q2 which in our case is
2:48:08going to be two and we have California
2:48:11all right now we can take all the
2:48:12different values actually drag it on
2:48:14down and we get all of our states
2:48:16extracted from this
2:48:21all right diving into our final analysis
2:48:22we're actually combine all of these
2:48:24different functions we just learned
2:48:25about specifically with this data set we
2:48:28have this column H right here and it's a
2:48:32list of different skills that each one
2:48:34of these job applicants have we want to
2:48:36combine this and aggregate this in order
2:48:38to analyze the most common skills for
2:48:40this we're going to have to walk through
2:48:42four different steps in order to get
2:48:45this into our final visualization that
2:48:47we can actually visualize and see here
2:48:50so I'm going to go ahead and clear all
2:48:51these values so we can get started
2:48:54actually doing this the first thing I
2:48:56want to do is actually combine all of
2:48:59these values into a single long text
2:49:02string and we're already having the
2:49:05separator of a comma and space between
2:49:07each skill so we're going to use that
2:49:09same separator to continue separating
2:49:11this so using text join we're going to
2:49:15first specify the delimiter of that
2:49:17comma and a space it's asking if I want
2:49:19to ignore those hidden or empty cells I
2:49:22do and then finally we need to provide
2:49:24the actual text itself so we'll go down
2:49:28through and select in our data tab H2 to
2:49:32h21 going back up into the formula bar
2:49:35closing this parenthesis and then
2:49:36pressing an enter look I have a like
2:49:38slight typo in here I need to actually
2:49:41put double quotes around both of them
2:49:42you can't mix double and single quotes
2:49:45now we have this super long list uh that
2:49:48has all of our different skills in it it
2:49:49looks like it's properly delimited now
2:49:52that we have all these values in one
2:49:54cell we can then use the text split
2:49:57function to now separate this into
2:50:01different cells because we're going to
2:50:02want to then move into transposing it
2:50:04next and for this once again the
2:50:06delimiter we're using is that comma and
2:50:09space running this we have all the
2:50:11different values separated out by
2:50:13different cells so now almost there we
2:50:16need to get into making a table
2:50:19right here basically having skills in
2:50:22the left hand column and then the counts
2:50:24of those skills from what's above here
2:50:26so first thing we need to do is get the
2:50:28unique values of this but if we just run
2:50:31unique on that row six we're going to
2:50:35run into an issue to where it actually
2:50:37goes out to the right and actually
2:50:38doesn't get the unique values for all
2:50:40these so the first thing we need to do
2:50:42is actually
2:50:44transpose which moving it from
2:50:47horizontal to vertic vertical of that
2:50:50row six okay so it's now up and down all
2:50:53the way now in this case we want to run
2:50:56the unique function on this to extract
2:50:59out all those unique values and
2:51:02scrolling down looks like we have all
2:51:03the unique values it does have a zero
2:51:06because that we did that row six and so
2:51:08when we get to these empty cells over
2:51:10keep on scrolling over here it records
2:51:12as zero I'm fine with that for the time
2:51:14being and we'll continue last thing we
2:51:16had to do is use basically a count if to
2:51:19count these different skills based on
2:51:21whether they appear or how often they
2:51:23appear in this row of six so I'll type
2:51:27in count if we need to specify the range
2:51:30first and we'll do six I want it to stay
2:51:32there uh as we're because we're going to
2:51:34autofill down so I'm going to F4 that
2:51:36and then from there specify the criteria
2:51:39which is going to be a n Kafka okay so
2:51:43three values for that one and then
2:51:45dragging this all the way down bam got
2:51:47this all filled in all right the last
2:51:49thing we need to do is actually
2:51:50visualize this cuz we want to visualize
2:51:53these skill counts select the area that
2:51:55we want we're going to go in and insert
2:51:57in under recommended charts you can do a
2:51:59bar chart but I'm more a fan of
2:52:01horizontal bar charts especially when we
2:52:02have text values and we need to be able
2:52:05to see all the different names so I'm
2:52:07going to have to expand that out a bit
2:52:09and I'm going to change this title up
2:52:11here just to something like skill count
2:52:14of applicants and Bam now we can see
2:52:18things some Trends out of this that a
2:52:20lot of people are claiming to have
2:52:22experience with data bricks which that's
2:52:25unusually high there probably something
2:52:26I want to investigate for this but a
2:52:28good little thing that we actually can
2:52:29analyze and see from this analysis that
2:52:31we did one minor note I would normally
2:52:33go through and actually sort this from
2:52:36high to low and you can definitely do
2:52:39this you'd have to copy and paste the
2:52:41values over you wouldn't be able to use
2:52:43these values right here and S sort and
2:52:45filter them because we're using the
2:52:48modern or dynamic array to find these
2:52:51unique values so that's definitely an
2:52:53option if you want to do and I
2:52:54definitely would recommend you do
2:52:55something like that before sharing some
2:52:57sort of visualization like this all
2:53:00right you now got some practice problems
2:53:01to go through and get more familiar with
2:53:03these text functions which like I said
2:53:05are imperative for de analysis in the
2:53:07next lesson we're going to be moving
2:53:09into our last one in this chapter on
2:53:12formulas and functions on date and time
2:53:15functions with that I'll see you in the
2:53:17next one
2:53:22all right saving the shortest lesson for
2:53:25last we're going to be focusing on date
2:53:26and time functions and for this we're
2:53:29going to be using that same data set
2:53:31from that last lesson which is about 20
2:53:34rows of job applicants now similar to
2:53:37text functions we're not using that full
2:53:39data science job data set that we've
2:53:41been using previously because I find
2:53:43it's not common to really use these date
2:53:47and time functions on a large set of
2:53:49data because it's going to slow down
2:53:51your sheets so that's why we're using
2:53:52this smaller data set for this once
2:53:54again if we're needed to actually clean
2:53:56up date and time stuff we're going to
2:53:58use something like power query which
2:53:59we're going to be getting to in the
2:54:00advanced chapter anyway we're going to
2:54:02be focusing on two main types of
2:54:03functions first up our date functions
2:54:06which going to be able to extract out
2:54:07things like month day and year and then
2:54:10from there we're going to transition
2:54:11into time functions extracting things
2:54:13out like hour minutes and seconds
2:54:15finally we're going to move into that
2:54:16final analysis looking at what is the
2:54:19time that is most likely for applicants
2:54:21to apply to jobs for this we're going to
2:54:24be using the date and time functions
2:54:27workbook and we'll be working in this
2:54:29data sheet for this filling in certain
2:54:31values as we go through this I'm going
2:54:33to go ahead and hide some of these
2:54:35unnecessary columns so we have more
2:54:37space to work with
2:54:40this anyway jumping right in if we want
2:54:43to calculate what the month is we have
2:54:45something like the month number putting
2:54:47that in that's D2 similarly we can get
2:54:50the day by using something like day and
2:54:53once again providing it D2 then finally
2:54:55something like year we can provide D2 We
2:54:59Get 2023 now if I wanted to only extract
2:55:02out of this date out of this date time
2:55:05if I were to use this date function it
2:55:07Returns the number that represents the
2:55:09date in Microsoft Excel okay date time
2:55:11code got it we're going to put in the
2:55:13year so we need to provide the year
2:55:15first month and then from there day boom
2:55:19and analyzing this we see it is febru 14
2:55:212023 one quick refresher on how Excel
2:55:25stores those datetime objects so right
2:55:28now it's in as a the number format of
2:55:30date if I change this back to General
2:55:33it's going to shift to this number and
2:55:35if we recall this stores the values in
2:55:38it if we start at something like one
2:55:41converting it to a short date we can see
2:55:43that it starts at January 1st 1900 now
2:55:47if you're working with dates before 1900
2:55:50let's say we put in something like
2:55:51negative 1 I converted it here to a date
2:55:54it's going to just provide all these
2:55:55different Amber Sands here there's a few
2:55:57different workarounds for that that's
2:55:58beyond the scope of this course main
2:56:00thing to understand is how it's actually
2:56:01stored within Excel anyway I'm going to
2:56:04convert this back up into a date and for
2:56:08each of these I want to actually fill in
2:56:10the values all the way down bam all
2:56:13right close up this home ribbon all
2:56:15right next up is today say we needed to
2:56:18today's date well I can put in the today
2:56:20function this actually takes no
2:56:22arguments and will provide us the date
2:56:24I'm filming this on September the 3rd
2:56:27now the last common function that I find
2:56:28myself using all the time are when I
2:56:30want to calculate the days since
2:56:32something happen in this case we want to
2:56:34find out how many days has it been since
2:56:38they have applied to the job so we can
2:56:41use the date diff function for this now
2:56:45the one thing to note with this is I'm
2:56:47typing it in there's no if I type in
2:56:49just date there's no date diff in there
2:56:52there's no documentation that Excel
2:56:55natively actually includes for you to
2:56:58use this so this is like a function you
2:57:00just have to know about anyway it takes
2:57:02three parameters basically the start
2:57:05date that we want to start from the
2:57:08reference date that we want to basically
2:57:10subtract from this which is today we
2:57:12want to actually go ahead and lock this
2:57:13I'm going to lock this with F4 and we
2:57:15want to provide this in the format of
2:57:18days which we provide this text
2:57:20character of D and this tells us it's
2:57:22been about 567 days since Valentine's
2:57:25Day in 2023 anyway updating all these
2:57:28cells for this we now have this
2:57:32data shifting gears into our time
2:57:36functions as we can expect a lot of
2:57:38these are going to be the same hour we
2:57:40use hour function minute has a function
2:57:43as well as second but this doesn't
2:57:45really to show seconds but we can see up
2:57:47here it is is actually included in your
2:57:49data similar to the date function for
2:57:52time we have to provide three parameters
2:57:54of hour minute and then also second drag
2:57:58and drop this all the way down we can
2:57:59see that yep it's correlating correctly
2:58:02one note for the hour that we previous
2:58:04calculated this is in military time or
2:58:06if you're in Europe you also do it this
2:58:08way anyway I really like this for an
2:58:10analysis purpose especially when we get
2:58:11into analyzing it now conversely we can
2:58:14also use for time and also date you
2:58:17could use the text function which we
2:58:19previously saw when we were extracting
2:58:21out the month out of date Times by
2:58:23providing a value and then the format
2:58:26text which we're going to say in this
2:58:28case is just hour hour minute minute if
2:58:31I wanted that am PM format not that
2:58:33military time format I can just add in
2:58:36Here Am Pm and it converts it
2:58:39appropriately dragging this all down and
2:58:41then filling it in we get
2:58:45it now moving into that final analysis
2:58:47we want to analyze when are these job
2:58:50postings Happening by hour of day the
2:58:53first thing we need to actually do is
2:58:55get a colum here of the hours in the day
2:58:59so we can do some sort of like count if
2:59:00on it in order to calculate that so for
2:59:02this I'm going to use the sequence
2:59:05function and I went 24 rows with it
2:59:08column's going to leave blank and I want
2:59:10to start at one and it's going to fill
2:59:12down from 1 all the way to 24 and now we
2:59:16need to run a c if basically for each
2:59:19one of these conditions run down this
2:59:21list basically matching to see what is
2:59:23the hour for these things so I have it
2:59:25hidden but I'm going to go ahead and
2:59:26make column again for hour and I'll put
2:59:29in here hour and unlike last time I'm
2:59:31actually just going to put the whole
2:59:32range in here and it's going to provide
2:59:34me back it in a modern array now with
2:59:37this I can actually now use this in the
2:59:39count if we want to First provide it a
2:59:42range which is our modern array so it's
2:59:44going to do I2 hashtag and then a
2:59:47criteria for the hour we want to search
2:59:49for we want to search for that one A2
2:59:51from here we want to fill it all in and
2:59:53we have some reference errors because we
2:59:56didn't lock our cells specifically we
2:59:58didn't lock this cell right here this I2
3:00:01so I'm going press f4 on that to
3:00:02actually lock that then dragging it all
3:00:05the way down we have it okay our last
3:00:08portion of this is actually visualizing
3:00:10this so we're going to go in select all
3:00:12that data go to insert go to recommended
3:00:14charts and I'm more of a fan of column
3:00:18charts with this type of data so I'm
3:00:20going to go ahead and put this in and
3:00:21I'm going to change this to job postings
3:00:25per hour and Bam now from this we're
3:00:28seeing that basically people are
3:00:31applying during normal working hours and
3:00:33apparently they're waiting until the end
3:00:35of the day to actually submit their job
3:00:37applications maybe to get in before a
3:00:39deadline or something all right this is
3:00:40the last lesson on functions and
3:00:43formulas in the next chapter we're going
3:00:45to be moving deeper into understanding
3:00:47how to actually make these different
3:00:50visualizations I've only been showing
3:00:51you a sneak peek at it right now to get
3:00:53you familiar with how to easily create
3:00:55it but we're going to go in into a lot
3:00:56greater detail up coming up next now we
3:00:59spent almost nine lessons on these
3:01:01functions and it's because I feel
3:01:04functions are one of the most important
3:01:05things to understand about Excel because
3:01:07it also transfers to other portions
3:01:10specifically we're going to be learning
3:01:12more about the Dax language in the
3:01:15advanced chapter and we're going to
3:01:16apply a lot of our knowledge that we
3:01:17already know about these Excel functions
3:01:20to Dax functions they're very similar
3:01:22anyway you got some practice problems to
3:01:23go through in work in order to
3:01:26understand better how to use these
3:01:27datetime functions and from there we'll
3:01:28get into that chart chapter with that
3:01:30I'll see you in the next
3:01:35one welcome to this chapter on charts
3:01:38and as much as I love using something
3:01:41like python a programming language for
3:01:43making
3:01:44visualizations I feel that Excel has
3:01:47some capabilities built into it that
3:01:50allow it to basically exceed any
3:01:52programming language and the
3:01:53customization that you can do to charts
3:01:54that we'll be finding out in this
3:01:56chapter for this chapter we have four
3:01:59lessons this lesson right here is an
3:02:01intro to chart so we're going to be
3:02:03focusing on understanding the basics of
3:02:04using charts and specifically looking at
3:02:08three types of charts specifically line
3:02:10charts pie charts and bar or column
3:02:12charts so technically that's four in the
3:02:15second lesson we're going to move into
3:02:16more advanced charts such as Scatter
3:02:18Plots and also map charts along with
3:02:21understanding more advanced
3:02:23customizations that we can do to these
3:02:25charts in the third lesson we're going
3:02:26to go Harden the paint in order to
3:02:28understand statistical charts
3:02:30specifically histograms and then also
3:02:32box and whisker charts which are
3:02:35imperative to understand statistical
3:02:37distributions of our data we'll finally
3:02:39wrap this all up with a final lesson
3:02:41focusing on spark lines which basically
3:02:43allow us to put charts inside of
3:02:46individual cells
3:02:48in Excel pretty neat all right for this
3:02:50lesson we're going to be using the
3:02:52charts intro
3:02:56workbook first thing to understand is
3:02:58terminology Microsoft refers to all
3:03:02these different visualizations diagrams
3:03:04plots whatever you want to call it they
3:03:06refer to it as chart basically they want
3:03:09to use a safe term that encompasses all
3:03:11the different type of visualizations we
3:03:13can build with this so you may hear me
3:03:14from time to time call this a plot or
3:03:16visualization basically mean a chart
3:03:18anyway why do we use charts well looking
3:03:22these six examples here we can see some
3:03:24different characteristics about this
3:03:25data that we're looking at but what if
3:03:27we looked at just the core data itself
3:03:30which is this table right here looking
3:03:31at what is the number of job postings
3:03:35per month if we look at this visually
3:03:38we're not able to see necessarily what
3:03:41is the highest month and also what is
3:03:44the lowest month I mean you can figure
3:03:46out eventually but it's not easy to spot
3:03:48and that's why charts are so powerful
3:03:51and so I have a variety of
3:03:52visualizations here in order to Showcase
3:03:55that same table that we were just
3:03:57looking at in basically a variety of
3:04:00different forms here even have a few
3:04:02below here down below it but we need to
3:04:05understand which chart to use because
3:04:08let's say we wanted to use this pie
3:04:10chart here is that actually a good chart
3:04:13to use to visualize this or instead
3:04:15should we be using something like this
3:04:18line chart to better show a trend over
3:04:20time while also showing a magnitude of
3:04:23difference anyway as we go through this
3:04:25lesson I'm going to be calling out when
3:04:27you should use certain charts as best
3:04:30practice along with my recommended tips
3:04:32for how to customize it to show them
3:04:38best so for our first chart as I hinted
3:04:40to we're going to be making this job
3:04:43posting count into a line chart and this
3:04:47is the chart I'd use typically for any
3:04:49time series like data as it's great at
3:04:52showing a trend over time and how it's
3:04:54connected so how do we do this well
3:04:56we're going to select all the data here
3:04:58all the way from A1 down to B13 come up
3:05:02into insert and we're going to dive into
3:05:05each one of these charts individually
3:05:06but I would encourage you to actually
3:05:08just start with recommended charts I
3:05:10really jump to it every time I use it
3:05:12anyway first thing they has two tabs
3:05:14here recommended charts and all charts
3:05:15for recommended charts usually provides
3:05:19a lot of good tips that you could
3:05:21potentially use for different charts
3:05:24sometimes however I do find that I want
3:05:26a particular chart and it's not here and
3:05:27that's when I'm going to go to this all
3:05:29charts Tab and frankly it provides a lot
3:05:31more control while allowing you to
3:05:34actually visualize your different data
3:05:36in our case I know I want a line chart
3:05:38on this but now I can go in and actually
3:05:41plot it with markers or even change it
3:05:44into a 3D line chart highly don't
3:05:46recommend this we're going to be
3:05:47sticking to a line chart for this and
3:05:49I'm going to go ahead and click okay I'm
3:05:51not going to lie this chart is getting
3:05:54us 90% of the way there now if you
3:05:57notice for this when we clicked on the
3:05:58chart we have certain values highlighted
3:06:02here basically this purple outline is
3:06:04showing that this is the X values right
3:06:07here and then the blue coordinates right
3:06:09here are showing the actual values
3:06:11themselves and then conveniently they
3:06:13put the job posting count which is
3:06:15highlighted in Orange as the title we'll
3:06:18be jumping into how to customize this
3:06:20area in the advanced section but that's
3:06:23in the next lesson now for those new to
3:06:25charts there's a bunch of different
3:06:27elements and I can come up here and I
3:06:28can click this plus icon right here and
3:06:30it shows all the different elements on
3:06:33here I can use the checkbox to control
3:06:35whether I want to include the axes or
3:06:38not in this case I do want to include it
3:06:40and then I can even find tune it further
3:06:42to select which one I'm talking about am
3:06:44I talking about the horizontal or am I
3:06:46talking about the vertical
3:06:48just going through these in Rapid
3:06:49fashion access titles allow us to
3:06:51provide titles for the X and Y AIS the
3:06:53chart title shown above I can remove it
3:06:56or keep it on if I want to include data
3:06:58labels I can do this along with
3:07:00controlling what position of them I want
3:07:02to go with I could also include
3:07:04something like a data table below but
3:07:06personally I find this is sometimes
3:07:07sensory overload I don't really use that
3:07:09much next are airb bars for data grid
3:07:12lines whether I want to have horizontal
3:07:15vertical some minor ones or some other
3:07:17minor ones a legend if there's more than
3:07:20one data I probably want this a trend
3:07:22line which will be adding in this a
3:07:24little bit and then up and down bars
3:07:27which are going to show whether the data
3:07:28goes up or down based on each set but
3:07:30not really necessarily applicable to
3:07:31this one now I find this plus icon is
3:07:34where I go most of the time but I could
3:07:36also go to this chart design tab up here
3:07:40and it has this box of add chart
3:07:41elements and basically you can go
3:07:43through and adjust all the different
3:07:45ones along with showing a more visual
3:07:48indication of what's going on here here
3:07:51showing that I was actual up down bars
3:07:53to actually see what they actually look
3:07:55like you can also use this quick layouts
3:07:57to quickly try out different themes that
3:08:01Excel has so doy myself from time to
3:08:03time using this so this chart is almost
3:08:06done all I do want to do first is change
3:08:08the title and I usually like to either
3:08:11provide some sort of snippet of
3:08:13information from it or ask a question
3:08:16that I want the reader of this graph to
3:08:19understand or take away from this chart
3:08:22so I can put in something like how did
3:08:24jobs Trend in 2023 so it also tells what
3:08:28year what's going on here and it asks
3:08:30them to look at hey what is the trend
3:08:32going on here which it looks like we
3:08:33have a peak up in January and a peek up
3:08:35in August now I try to minimize the
3:08:38amount of access titles on here because
3:08:39like in the month's case that's pretty
3:08:41self-explanatory however the number in
3:08:43the y- AIS is not so self-explanatory so
3:08:47in that case I would want to include it
3:08:50in this case give it a representative
3:08:51name of counts of jobs the last thing I
3:08:54want to do with this is just add a trend
3:08:56line and there's multiple different
3:08:57options for this we can do linear
3:08:59exponential a linear forecast where it
3:09:01actually goes into the future and then
3:09:03even a two period moving average which
3:09:06is pretty neat I'm going to just stick
3:09:08with the basic one right now of linear
3:09:10and Bam that's our first chart so let's
3:09:12move in the next
3:09:15one now if we go back to our original
3:09:17data set in the data tab we have a
3:09:20column here on job no degree mention and
3:09:24basically this column right here
3:09:27includes whether there's a mention of a
3:09:30degree in a job posting so in this case
3:09:34where we have two different values we're
3:09:37trying to determine what are the
3:09:39proportions of each a way to compare
3:09:42this we could either compare this in
3:09:44like a bar or column chart but I feel a
3:09:46better one for this is a pie chart so
3:09:48I've gone through and calculated a count
3:09:51of the jobs with a no degree mention
3:09:54along with those that have a mention of
3:09:57a degree I calculated the total and then
3:10:00from that I calculated their individual
3:10:03percentages now I'm not going to just
3:10:05select all the data here because I don't
3:10:07want to plot all of it I'm going to
3:10:08select the first two values here of A2
3:10:11A3 press control and then also select C2
3:10:14to C3 then from here now I'm I'm going
3:10:17to go insert those recommended charts
3:10:20like got a lad two bar and column charts
3:10:23come up but the one we're going to be
3:10:24using for this it's a pie chart so I'm
3:10:26going to go ahead and insert that in now
3:10:28personally I'm not a fan of this layout
3:10:31here so I'm going to come up into chart
3:10:32designs into Quick layouts and I'm going
3:10:34to just experiment with different ones
3:10:37looking at them and frankly I like the
3:10:39one this one right here actually where
3:10:42we've removed the legend and put the
3:10:44actual values themselves along with
3:10:46their titles inside the pie chart itself
3:10:49to make it super simple to see which one
3:10:51is which now Excel sometimes gets crazy
3:10:55with the colors I actually don't
3:10:57recommend using a lot of different
3:10:59colors because it could be very
3:11:00confusing for viewers on where to look
3:11:03personally I want to highlight more of
3:11:05the no degree mentioned so I'm going to
3:11:09use this single color palette right here
3:11:12or this monochromatic color palette
3:11:14right here that has these different
3:11:15shades of blue and and I feel the ey is
3:11:18going to go more to the darker blue now
3:11:20with each of these labels here I can
3:11:23actually select it I double clicked it
3:11:25over time I can actually drag it and
3:11:27drop it and move it around where I want
3:11:29it to be I would probably want it to be
3:11:30more over here I want the degree
3:11:32mentioned to be stacked basically I want
3:11:35them opposite of each other now you may
3:11:37have noticed I can't really read this
3:11:40text right here and even this text is
3:11:42hard to read as well so what I can do is
3:11:45I'll just click outside real quick and
3:11:47clicking back in I'm going to double
3:11:49click and this is going to bring up the
3:11:51format data labels if double clicking
3:11:54isn't work you can just select it go
3:11:56into the format tab up here and select
3:11:59format selection anyway there's a lot to
3:12:02unpack in this Pane and we'll be
3:12:03unpacking it as we go along this entire
3:12:05chapter but the main thing to understand
3:12:07is they have label options and text
3:12:09options we want to adjust the text
3:12:12options and this has things like text
3:12:13fill and outline text effects and then
3:12:16also the text box for this we're trying
3:12:18to fill the text fill and outline
3:12:21specifically this drop down here of text
3:12:24fill we want to change the color so we
3:12:27want to change it to White now if you
3:12:29notice only one of these change and
3:12:31that's because I only had one of the
3:12:33boxes selected so actually actually
3:12:36click out of this double click back into
3:12:38this and then make sure both of these
3:12:40are actually selected go back into text
3:12:42options go into text fill and then
3:12:44change this color and then it's going to
3:12:45change both of these colors
3:12:47now I'm fine with this text now but
3:12:49let's say I wanted to customize further
3:12:51the percentage here maybe I want to
3:12:53include one more decimal place clicking
3:12:55on the box itself I can now have this
3:12:58option for label options and then under
3:13:01well label options again I can scroll
3:13:03all the way down or I can actually cover
3:13:05this up and then unhide this number I
3:13:08can change the number formatting itself
3:13:10in this case I do want to still do a
3:13:12percentage and then maybe I want to do
3:13:14one decimal place personally I think
3:13:16there's a little a little bit too much
3:13:17dat so we're just going to keep it with
3:13:18the zero all right that's the final
3:13:20customization the last thing we want to
3:13:22do is just add a title and I want a very
3:13:25compelling title what do they want to
3:13:27look at for this I want them to
3:13:28understand what jobs mention a degree
3:13:33and now with this we have a pretty great
3:13:36visual indication of that about one of
3:13:40jobs have no degree mention in them
3:13:44which personally I think that's a pretty
3:13:45high percentage and hopefully gets
3:13:50higher so we have data similar to our
3:13:53first chart that basically explains how
3:13:55many counts of jobs for the different
3:13:58job titles now this isn't chronological
3:14:01so I don't necessarily recommend using
3:14:04something like a line chart for this
3:14:05that's why we're going to be making
3:14:07column and bar charts for this also let
3:14:09explain the difference between the two
3:14:10anyway I'm using the formulas that we
3:14:12previously have covered you can dive
3:14:14into it if you want to basically using
3:14:15unique and then also a c if formula in
3:14:19order to count each one of these in
3:14:21their data tab anyway if I actually go
3:14:23to graph these by selecting all these
3:14:26things go to insert and recommended
3:14:28charts here provides the recommended
3:14:30charts and we're going to start with a
3:14:32column chart first I start with this one
3:14:35first because we're already running into
3:14:37problems with how long these labels are
3:14:41we can see that we have these three
3:14:42ellipses here basically telling the that
3:14:45the rest of the name is hidden here so
3:14:47not all the names are shown here the
3:14:50other problem that we're getting into
3:14:52with this column chart um named after
3:14:55the fact that it looks like columns is
3:14:58that it's not in an organized manner I
3:15:00would expect to see it high to low to
3:15:02make it more easily to compare values to
3:15:05each other and also how they rank so
3:15:07we'll go ahead and delete this bad boy
3:15:09anyway this table is organized based on
3:15:12this unique function which doesn't
3:15:15necessarily put things in the correct
3:15:17order and I won't be able to actually go
3:15:18through and filter it or soter it
3:15:21appropriately So Below this I made a
3:15:25different table that I basically use
3:15:27sort to sort these values from above by
3:15:32their job count in descending order now
3:15:36since it's in this order I could
3:15:37actually select a few less of this
3:15:39remember how it was cut off last time I
3:15:41could select only the top six go into
3:15:44here go into recommended charts and once
3:15:46again and put in our clustered column
3:15:49chart now this one I can play around
3:15:52with and as you see as I expand it out I
3:15:55can actually see all the different names
3:15:58here but once again I'm not a fan of
3:16:00this column chart I'm not going to be
3:16:02using it for this case instead we're
3:16:03going to try out a bar chart instead so
3:16:06selecting all this data to show the
3:16:08power of these bar charts and then
3:16:11coming in I can put in that bar chart
3:16:13now I do like this one better because
3:16:15all the titles are organ ganized and
3:16:18they're right off to the side and so
3:16:19this is a much more easier read the
3:16:22problem now is I'm really nitpicky with
3:16:24my charts the problem now is I don't
3:16:26like the order that this is in what
3:16:28happens is is Excel starts plotting
3:16:31these although it's in descending order
3:16:33in our table as shown over here it's
3:16:36going to be plotting them starting at
3:16:38this zero axis up here and then plotting
3:16:40from there so technically we don't even
3:16:42want it like this instead what I can do
3:16:46is reverse the sort order here I'm just
3:16:49controlling it by using uh either one or
3:16:52netive one in that sort order portion
3:16:55anyway with this order now now we can
3:16:58finally get into the final bar chart
3:17:00that we want to actually put in and I'm
3:17:01just going to skip this recommended
3:17:02charts come up here into the column and
3:17:04then the bar charts we want this one
3:17:06inserted in and I'm also going to zoom
3:17:08out some now this is more in lined with
3:17:12what I want let's actually clean up this
3:17:14visualization to identify what we want
3:17:17I'm actually more curious about what are
3:17:19the top jobs in data science so that's
3:17:22what we'll name it additionally feel the
3:17:24titles are pretty self-explanatory based
3:17:27on that title but I would need something
3:17:29for the x-axis down here so we'll add an
3:17:31axis title calling this count of job
3:17:34postings now with this question I'm
3:17:36asking of what are the top jobs in data
3:17:39science I'm not really feeling like we
3:17:41need to include things like machine
3:17:44learning Engineers software Engineers
3:17:45cloudware Engineers or business business
3:17:47analyst how could I actually adjust this
3:17:50well one way is I could control what
3:17:53areas are highlighted over here and I
3:17:55could actually drag this and change this
3:17:58to whichever ones I want um but I'm not
3:18:01necessarily going to recommend that
3:18:02instead I'm going to select our data
3:18:03make sure all the columns are selected
3:18:05themselves rightclick it and then go to
3:18:08select data and this new window is going
3:18:11to pop up here this tells us a lot of
3:18:14great things about our visualization
3:18:16first is the chart data range it tells
3:18:18us we're selected from a25 to b35 so we
3:18:21could change that here if we wanted to
3:18:23the next thing is the two windows down
3:18:26here of the legend entries and the
3:18:28horizontal axis so this controls our job
3:18:31count I'm going to scroll this over here
3:18:33we could just remove job count but it's
3:18:35not going to do anything this guys
3:18:36mainly right here the access labels we
3:18:38can control so I know I want data
3:18:40analyst and all the way up down to
3:18:42senior data analyst I can actually go
3:18:44through and select remove business
3:18:46analyst machine learning engineer
3:18:49software engineer and Cloud engineer and
3:18:52then click okay and it will remove it
3:18:54from this visualization while still
3:18:56keeping this data here so I can easily
3:18:58go back and add or remove job titles as
3:19:01necessary and now we have our final
3:19:04visualization earlier I did go through
3:19:06and actually delete the chart and start
3:19:09over but you do have this option in the
3:19:11chart design tab of change chart type
3:19:14and allows you to basically go through
3:19:16and try out different ones if I wanted
3:19:19to go back to that column chart I could
3:19:22and it would show me an example of what
3:19:23it looks like now there is one last
3:19:26thing that I want to format on this I do
3:19:28find it a little difficult to read
3:19:29exactly what are the amount of job
3:19:32postings that they have here so I'm
3:19:34going to add data labels to this we have
3:19:37a couple different options we can be
3:19:39inside end which can't read at all
3:19:41inside Base outside end which I'm more
3:19:43for and then also a data call that's
3:19:45just too much there we're going to do
3:19:46outside end now with this these numbers
3:19:49I don't like the level of detail I don't
3:19:51need down to the single or the on
3:19:54digigit place to tell what it is instead
3:19:56I would rather it shows something like
3:19:599.6k or 9.6000 so we can actually format
3:20:03that so double clicking on one of those
3:20:05labels this format short area is going
3:20:07to pop up again and for this I'm going
3:20:09to go under label options and then label
3:20:12options again and finally number and for
3:20:15this I'm going to use use instead of uh
3:20:18any one of these I'm going to use a
3:20:20custom type now I have a few of these
3:20:22already built into here and so they may
3:20:25not pop up to you but this is actually
3:20:27sneak peek this is actually what we want
3:20:29but if you don't have this popping up
3:20:30right now what you can do is actually go
3:20:33in in this case I'll just show a
3:20:34different value what we're going to
3:20:35first say is how we want this formated
3:20:38with how many decimal places so I want
3:20:40all the values before the decimal place
3:20:42then a decimal place and then I only
3:20:44want in this case let's go with two
3:20:46places after the decimal place and then
3:20:48from there I want a K on the end so
3:20:51basically to show this as a thousand so
3:20:53I'm going to use a parenthesis put a k
3:20:56and then close parenthesis and I'm going
3:20:58to click add okay so now this changes it
3:21:00to the double digits for explaining that
3:21:04this is the thousands this automatically
3:21:07whenever I do that K parentheses it
3:21:09automatically does the math to basically
3:21:12divide that by a th and transfer this to
3:21:15K instead of the thousands anyway I
3:21:18don't really I'm going to go with the
3:21:19original one I had of only one decimal
3:21:21place and Bam that's our final
3:21:23visualization and we can see from this
3:21:26that we have a lot of insights into
3:21:27understanding that more Junior roles
3:21:29like dat analyst dat scientist dat
3:21:31Engineers are more prevalent than the
3:21:32senior roles and that luckily it seems
3:21:35like there's a lot more data analyst
3:21:36roles than data scientists and data
3:21:38Engineers all right you now some
3:21:39practice problems to go through and get
3:21:41more familiar with those four major type
3:21:44of visualizations that frankly I feel
3:21:47I'm using on a daily basis anytime I'm
3:21:49making visualizations so don't think
3:21:51that they're just too plain or too
3:21:52simple they're really powerful and
3:21:54explaining data in the next lesson we're
3:21:57going to be jumping into not only more
3:21:58advanced charts but even more advanced
3:22:01customization so with that I'll see you
3:22:03in that
3:22:07one we're going to crank this up a notch
3:22:09and get into some more advanced
3:22:12visualizations specifically on this
3:22:14we're going to be doing a deeper dive
3:22:16dive into the pay of different jobs not
3:22:20only based on the different job titles
3:22:23but also based on where a job is located
3:22:27using things like a map chart and so for
3:22:30all these charts also we're going to be
3:22:31looking into how we can further get into
3:22:34deeper customization of
3:22:39these so Scatter Plots are great at
3:22:42comparing two numerical values in our
3:22:45data set we have these two columns here
3:22:49one on the salary year average and the
3:22:51other on the salary hour average just as
3:22:54a background on why it's called average
3:22:56at the end of these sometimes job
3:22:58postings have a range of salary and so I
3:23:01took the average of the Min and Max and
3:23:04hence I named this average anyway we
3:23:07have yearly salary data and we have
3:23:09hourly salary data what it did next is
3:23:12get the unique value of the job titles
3:23:14and then from there using that median
3:23:17basically modified median IF function
3:23:19got the yearly median salaries and then
3:23:22the hourly median salaries so because we
3:23:25have these two numerical values to
3:23:28compare basically we want to see if
3:23:30there's a trend correlated between the
3:23:32two because well there is we're going to
3:23:34find out I'm going to go ahead and
3:23:36select these all then from there go into
3:23:38insert and we can come into charts I
3:23:41know I want a scatter plot and if we go
3:23:43to insert it in can't see cuz it's
3:23:46hidden behind here well we'll just go
3:23:48ahead and show it this isn't necessarily
3:23:50showing us what I want us to show with
3:23:54this it's basically showing hey this is
3:23:56the yearly data up here in the blue and
3:23:58then this is the hourly data since
3:24:00hourly data it's super low it didn't
3:24:02work out how I wanted to by selecting
3:24:04all the data like we've previously been
3:24:06doing instead I'm going to go ahead and
3:24:07delete this what we're going to do is
3:24:10we're only going to select basically
3:24:12this B and C column of data once again
3:24:15we're going to try again inserting that
3:24:17scatter plot and at this point it's
3:24:20actually working correctly as we want it
3:24:22unfortunately we can't tell there's no
3:24:25basically like data labels for this to
3:24:28understand what are the different job
3:24:29titles associated with it even with the
3:24:31graph we can see that it's only
3:24:32highlighting this also the incorrect
3:24:34titles up here it's not just hourly
3:24:36median salary we're going to fix all
3:24:38this anyway the first thing that I want
3:24:40to clean up is actually the selection of
3:24:42data right now we can see these numbers
3:24:43are overlapping down here also it goes
3:24:45all the way down to this zero axis on
3:24:48both the X and Y I want to change that
3:24:52so I'm going to double click this x axis
3:24:54and format access pane pops up and we
3:24:57can see that we have bounds here 0 to
3:24:59180,000 I can see that there's no values
3:25:01under about 75,000 so I'm going to go
3:25:04ahead and put that in for the minimum
3:25:06and press enter so it's going to jumate
3:25:08this way now I want to do the same thing
3:25:11for the Y AIS I'll just double click it
3:25:14and this one didn't necessarily go where
3:25:16I wanted it to go I wanted to actually
3:25:18change the values here so we can go
3:25:20under access options under access
3:25:23options again and under access options
3:25:25again we can change this minimum maximum
3:25:28I'm going to change it to looks like
3:25:30there's nothing above 20 or below 25 so
3:25:33we're going to go with that now even
3:25:35with this change in the formatting of
3:25:37the values here the minimum I can still
3:25:40see that there's overlap here so I want
3:25:43to update this similar to last time
3:25:45basically cut it off the thousands place
3:25:47and place and put a k at the end so
3:25:50under access options access options
3:25:52again I'm going to close this drop down
3:25:54of access options also instead we're
3:25:56going to go to number for this we want a
3:25:58custom type and I do have some values in
3:26:02here but we're just going to go if you
3:26:04don't have them in here we're going to
3:26:05add a new one specifically with this I
3:26:08wanted to show one I wanted to show a
3:26:11dollar sign at the front and I don't
3:26:12want any decimal places whatsoever so
3:26:15I'm just going to put a zero in there
3:26:17and then from there like last time I
3:26:19want to format this in the thousand's
3:26:21place so I'm going to put a comma and
3:26:24then double quotes to put around the K
3:26:28which signifies I want to formulate this
3:26:29in the thousand's place I'm going go
3:26:31ahead and click add and now this is much
3:26:34more readable not so much sensory
3:26:37overload for our y AIS I don't care at
3:26:40all about this decimal place right here
3:26:42so going back into numbers again I can
3:26:45just format the decimal place places as
3:26:47zero and I'll just leave this one as an
3:26:49accounting category now which one's
3:26:51yearly and which one hourly salary well
3:26:53we need to include actual access titles
3:26:57for this so I'll go ahead and enable
3:26:59that and then for this we're going to do
3:27:01something a little bit different I'm
3:27:03going to select this ya AIS title and
3:27:07instead of actually typing in values in
3:27:09I want to use actually the column header
3:27:12right here so I'm going to come up into
3:27:13the formula bar type equal to I'm going
3:27:16select C1 and then press enter and now
3:27:21this updates for that column head I can
3:27:22do the same thing here for the x-axis
3:27:25title selecting it then from there going
3:27:27to the formula bar put an equal and
3:27:29selecting cell B1 and pressing enter for
3:27:32the title we don't want that hourly
3:27:33median salary we're really trying to
3:27:35find out what jobs have the highest pay
3:27:38and we can basically tell it from this
3:27:39all right so let's actually finally get
3:27:41to adding data labels to this and we can
3:27:43see what data labels are actually
3:27:44available but scrolling over the
3:27:46different options here we're going to
3:27:48just go with above for the time being
3:27:50then I'm going to close on out of this
3:27:52and I'm going to select the data labels
3:27:54themselves and format data labels should
3:27:56pop up if it doesn't you can also go
3:27:58about doing it by right-clicking this
3:28:01and going to format data labels anyway
3:28:03for this I don't want to actually show
3:28:06The X or the Y value for this anyway uh
3:28:10I made I made it disappear by actually
3:28:13closing out of that so actually I going
3:28:14have to add those data labels again
3:28:16again anyway going back into it under
3:28:17label options label options then label
3:28:19options again I'm going to leave that y
3:28:21value selected for right now but what I
3:28:23want to do now is provide the job title
3:28:26itself right next to the data point so I
3:28:30can do this option here so label
3:28:31contains value from cells and it's going
3:28:35to ask me to select the data label range
3:28:38and so now this is when I'm going to
3:28:40select all of these different job titles
3:28:43here and press okay so now we have these
3:28:46values from cells I no longer want this
3:28:48y values and I do want to include this
3:28:51leader lines because we're going to be
3:28:52actually dragging this around because as
3:28:54you can see some of these values are
3:28:56overlapping now also I'm noticing that
3:28:59this is really busy right now with all
3:29:00this text and stuff so I'm actually
3:29:02going to remove the grid lines for the
3:29:04time being actually for the remainder of
3:29:05this cuz I I don't feel like it really
3:29:07needs the grid lines in general and now
3:29:09I have a little bit less sensory
3:29:10overload so I can go through and
3:29:13actually clean up where a lot of these
3:29:15different job titles are located by just
3:29:17selecting it and then dragging it and
3:29:20you notice uh we had that leader line
3:29:21selected so I have arrows or basically
3:29:24lines going to each of these ones to
3:29:27signify which one is which so now I've
3:29:30dragging these all over so that way
3:29:31they're basically more represent I want
3:29:34sometimes if I dragged off of this and
3:29:36drag maybe the whole chart itself and
3:29:37make a mistake I press just control Z
3:29:40and it reverts it back to where I'm
3:29:41going and then I just continue on to
3:29:43selecting the box that I want and moving
3:29:45it anyway this is pretty neat now I
3:29:47could actually go in if I wanted to and
3:29:50add a trend line to this and basically
3:29:53it shows for an increase in that yearly
3:29:55salary I expect the same with the hourly
3:29:57data in this case I don't find it as
3:29:59much useful so I'm going to just keep
3:30:02leave that off but in general it is
3:30:04pretty neat to see the trend that's
3:30:06going on with this that senior data
3:30:08Engineers although they're underpaid
3:30:10compared to senior data scientist in
3:30:13yearly salary you could get the hookup
3:30:15if in instead you look for an hourly gig
3:30:17instead in order to get a little bit
3:30:19higher pay a similar Dynamic happens
3:30:22between business analysts and data
3:30:24analyst so if you're a data analyst and
3:30:26you're looking for a job maybe on upwork
3:30:28maybe you should advertise as a business
3:30:29analyst
3:30:32instead all right going back to our data
3:30:34set itself we have another column in
3:30:37here I want to investigate and that's
3:30:39specifically around the country is
3:30:41called job country basically where the
3:30:44job is located at and I like to
3:30:46visualize these type of things well on a
3:30:48map to actually see how it affects
3:30:50others so I've made this table here
3:30:53under the map chart tab where we have
3:30:55our all the different countries in the
3:30:57data set then from there we use a count
3:30:59if to determine how many counts for each
3:31:01of the countries and then our modified
3:31:03median if in order to determine what the
3:31:06median salary is in each of these
3:31:08countries I've also had to wrap this one
3:31:10in an if error because some of these if
3:31:11there's no values it throws an error and
3:31:14I didn't want that popping up in the
3:31:15chart so so I had it disappear or make
3:31:17it basically a blank value if it does
3:31:19have an error anyway let's get into
3:31:21visualizing this we're going to first
3:31:23just visualize what are the counts of
3:31:26these different jobs based on the
3:31:28country so I'm going to select column A
3:31:30and B go to insert and then maps and go
3:31:34to this map chart now you may have a
3:31:37pop-up warning that comes up during this
3:31:39that says data needed to create your map
3:31:41chart will be set to B and I'm fine with
3:31:45sending this data to being you should be
3:31:46fine too with it so feel free to accept
3:31:48this then you shouldn't get this pop up
3:31:50anymore anyway this chart's pretty neat
3:31:53because it goes and shows we have a
3:31:55heavy concentration of jobs basically
3:31:58from the United States for my job
3:32:00scraper I'm heavily aggregating jobs
3:32:03from this country compared to other
3:32:04countries sorry other countries out
3:32:06there but I am still n less collecting
3:32:08from other countries like us has 25,000
3:32:10India is around 580 for this one I'm
3:32:13going to change the title to where are
3:32:16most jobs in Luke's data set from
3:32:17there's not to say the United States has
3:32:19more jobs than other countries this is
3:32:20just how my data set is and how I
3:32:22extracted the data so don't want you to
3:32:24come up with the wrong conclusions from
3:32:26this now the visualization that I really
3:32:28care about is comparing these countries
3:32:30to the median salary so holding control
3:32:32I select a and then C I'm going to do
3:32:35recommended charge from this cuz I'm
3:32:36having problems using the maps one
3:32:39anyway I see that it has the filled map
3:32:40here I'm going to select okay and I have
3:32:43all the data filled in all right with
3:32:44this visualization we we can now dive in
3:32:47we can see that we have a range of these
3:32:49median salaries from over 157,000 down
3:32:52to 30,000 with country like China having
3:32:55around 68,000 and then over in Africa we
3:32:58have Algeria at
3:33:0045,000 so looks like we have a lower
3:33:02salary in the African continent over in
3:33:05North America and also South America
3:33:07pretty high salaries along with
3:33:09Australia as well anyway pretty cool
3:33:11visualization we were able to generate
3:33:13out of this I mean I love data and I
3:33:15just love this visual a with this I'm
3:33:16going to change the title to what are
3:33:20top paying countries now the last thing
3:33:23is a minor Point sometimes if you're
3:33:24going ahead and actually moving maybe
3:33:27columns around you'll notice that my
3:33:29visualization is also moving as well and
3:33:32this can wreak havoc especially whenever
3:33:35you've made your dash or made your chart
3:33:37a certain size and then move columns
3:33:38around and it messes everything up we
3:33:40can fix this so I'm going to go ahead
3:33:42and contrl Z both of those column moves
3:33:44to get it back to where I had previously
3:33:47and then from there I'm just going to
3:33:48double click on the chart itself go
3:33:49under chart options and once again this
3:33:52like resizing one here and going under
3:33:55properties right now it's selected under
3:33:57move and size with cells we don't want
3:34:00to do that basically we don't want to
3:34:02move or size with the cells so I'm going
3:34:05to select that now closing out of this
3:34:07whenever I go to adjust the column size
3:34:09it's not going to adjust the
3:34:10visualization at all this is much more
3:34:13of what I want also one last note on
3:34:15this I do do have a filter currently
3:34:17applied to this data set specifically I
3:34:19go into it it's a custom filter and I
3:34:22wanted to make sure that I had basically
3:34:25removed any na values so I put hey I
3:34:28want values that are median Sal greater
3:34:30than zero and are less than 200,000 so
3:34:34if I go ahead and clear this filter we
3:34:37can see that we have some other values
3:34:39up here basically rushes up here at
3:34:41300,000 for a median salary and if we
3:34:44actually go in investigate Russia we'll
3:34:47see that they only have around four jobs
3:34:49with salary data listed so I feel like
3:34:52this salary is more of an outlier than
3:34:55anything so that's why I'm applying this
3:34:57filter of 0 to 200,000 applying this
3:35:00filter again we get final visualization
3:35:03now you could also play around with this
3:35:05and filter it based on the number of
3:35:07counts to make sure you have values that
3:35:09are above a certain count that's also an
3:35:11option and probably maybe even a better
3:35:13option as well all right chch turn now
3:35:16to dive into those practice problems to
3:35:18try out some different Advanced
3:35:19visualizations and along with some
3:35:21Advanced customization with that in the
3:35:24next lesson we're going to be diving
3:35:26deeper into understanding how to use
3:35:28statistical analysis specifically box
3:35:30and wher charts and also histograms and
3:35:32how to read them with that see you in
3:35:34the next
3:35:38one this lesson is going to be focused
3:35:40on actually visualizing a lot of the
3:35:43things that or a lot of the functions
3:35:44that we used in that statistical
3:35:47functions lesson where we're looking
3:35:49visually at things like the median and
3:35:52core tiles specifically we're going to
3:35:54do a refresher on histograms we've seen
3:35:56it a few time reality but we're going to
3:35:58dive into further understanding how
3:36:02salaries are distributed specifically
3:36:04for a target audience of data analyst in
3:36:06the United States you can feel feel free
3:36:08to do whoever you want and then from
3:36:10there based on the limitations of it
3:36:12only be able to visualize one job title
3:36:15we're going to shift Vex to looking at
3:36:17box and whisker charts and these are
3:36:20great at also showing statistical
3:36:22distributions like a histogram but we
3:36:24can take it a step further and we
3:36:26compare different values specifically in
3:36:29this case we're going to compare them
3:36:31across the different job titles on how
3:36:32they're distributed now box and whisker
3:36:35charts aren't probably a chart that
3:36:37you're familiar with or most people are
3:36:38familiar with so we're going to go
3:36:40through a review and understand and
3:36:42break them down to understand those
3:36:44Concepts we talked about previously
3:36:46about median and quartiles and where
3:36:47they fall into this for this we're going
3:36:50to be using the charts statistics
3:36:53workbook specifically we're going to be
3:36:54starting in this data Tab and for all
3:36:57this we're going to be analyzing salary
3:36:59data in this video we're going to be
3:37:00focusing specifically though on that
3:37:02yearly salary
3:37:06data so let's actually go back into
3:37:09breaking down how to read a histogram we
3:37:11go back into insert recommended charts
3:37:14and then from there select histogram and
3:37:16insert in the histogram I don't like
3:37:19where it is right now I'm actually going
3:37:20to move this chart into a new sheet now
3:37:25quick refresher on histograms each one
3:37:27of these bars represents a count of
3:37:29values within a range so in this case
3:37:33there's 920 values between the range of
3:37:37oh my gosh so hard to read 75,000 to
3:37:4181,000 and as we're noting by this we
3:37:43have a large number over here if gets
3:37:45even out to 960,000 this would be called
3:37:48a skewed right distribution now this is
3:37:52different from a column chart because
3:37:55this data down here on the xaxis is
3:37:58basically continuous data when one bin
3:38:01stops so this first bin of 15,000 to
3:38:0421,000 the next bin picks up now the
3:38:07first problem with this histogram is
3:38:10this is for all salary data specifically
3:38:12all job titles across all countries I
3:38:15want to actually find tune to look at my
3:38:18specific use case of data analyst in the
3:38:20United States so you can come here into
3:38:22the histogram 2 Tab and I have the four
3:38:26Columns of interest that I want to use
3:38:28from the data Tab and I already have the
3:38:30filters applied but if you want to you
3:38:32can come in here and actually select to
3:38:34clear these filters and I'll just select
3:38:36it here from that Home tab then from
3:38:39there I'm going to go through and select
3:38:41data analyst roles that are full-time
3:38:44only that are in the United States and
3:38:48then finally I don't want any of these
3:38:49blank values here so I'm going to
3:38:52uncheck this value here for blanks now
3:38:54we'll say filtering this data did take
3:38:57some time to actually do so don't be
3:38:59alarmed if this taken more than 10 or 15
3:39:01seconds all right so back in let's
3:39:03actually make a histogram with this data
3:39:06we'll go into insert from here I'm going
3:39:08to insert in a histogram now once again
3:39:10this distribution is so the last one
3:39:13skewed right and we have a heavy amount
3:39:14of outline s right here even out this
3:39:17one value around 370,000 I don't think
3:39:20this provides a lot of value instead I
3:39:22want to actually focus more into these
3:39:25this actual distribution and not
3:39:27actually on this portion out here that
3:39:28we have just outliers anyway I'm going
3:39:30to come in here into our filters up here
3:39:32insert a number filter and that it's
3:39:35less than 300,000 click okay all right
3:39:39this is looking a lot more readable
3:39:42which we can actually see now the x-axis
3:39:45now each one of these bars right here or
3:39:48what what you would see in like a column
3:39:49chart are called the bins and they're
3:39:52all equally space but we can control the
3:39:55width of each one of those bins that
3:39:57they Encompass specifically I can double
3:39:59click on the chart to bring up that pane
3:40:01to the right selecting the x axis I can
3:40:04then go into access options and then
3:40:07once again access options we can go into
3:40:09something right now we're noticing that
3:40:10the bins are automatically determined we
3:40:13can actually change this binwidth I'm
3:40:15going to change this something to like
3:40:1715,000 notice that it is bigger in this
3:40:20case the bins are bigger than they were
3:40:21previously you can feel free to test
3:40:24different options if you will I feel if
3:40:26you go too small in the case let's say
3:40:28we went down to 1,000 it just gets too
3:40:30noisy and also you can't necessarily see
3:40:32the distribution as well so really you
3:40:35just have to play around with it until
3:40:36you get to what you want to find as far
3:40:38as the access goes this is a little bit
3:40:41this is sensory overload for me way too
3:40:43many zeros in here so I'm going to move
3:40:45this selecting the xaxis we can see that
3:40:48has format access now I can go under
3:40:50number and once again we can go in our
3:40:52custom type none of the ones that I've
3:40:54previously done are here sometimes it
3:40:56pops up sometimes it doesn't we're going
3:40:58to go ahead and just put in we want the
3:41:00dollar sign zero and then formatted with
3:41:03the K value basically removing all those
3:41:06uh thousands zeros and I'm going to go
3:41:08ahead and click add all right this is a
3:41:11lot more readable to actually see what
3:41:14those different ranges are
3:41:15and from there I'm going to change the
3:41:17title of how much do data analysts in
3:41:20the United States make probably also
3:41:22best practice here to add a title on the
3:41:26Y AIS for count of jobs and B now we
3:41:30have this final visualization show on
3:41:31our histogram we can see that a lot of
3:41:33the salaries are more around the range
3:41:36of 85,000 to 100,000 which 70,000 85,000
3:41:41is coming up next so this show is really
3:41:43visually great and at where I can expect
3:41:47to have a salary as a starting data
3:41:52analyst but now what if we want to
3:41:55analyze multiple different job titles
3:41:58which we're going eventually get to is
3:42:00this box plot here where we're plotting
3:42:02it for all the different job tiles we'll
3:42:04be able to actually compare different
3:42:06values across each other but before we
3:42:08get to that we need to First understand
3:42:10how to read a box plot also sometimes I
3:42:12call it a box plot but it's also known
3:42:14as a box and whiskers chart anyway I
3:42:17made this visualization here you don't
3:42:18have to do it there's a bunch of
3:42:19customization along with it the main
3:42:21purpose of this is to demonstrate or
3:42:23help understand how to read a box and
3:42:27whiskers chart so I took our data that
3:42:30we previously were analyzing for data
3:42:32analyst in the United States it was a
3:42:34full-time role along with all the salary
3:42:35data and then I use like we previously
3:42:38did calculating things like the Min
3:42:41first quartile median average third
3:42:44quartile and Max just ignore this
3:42:46portion right here it was used to make
3:42:48build this visualization right here
3:42:50anyway I tried as best as possible to
3:42:52line up this histogram where we have the
3:42:54x-axis going from 25,000 to 285,000 with
3:42:59the box and whiskers chart I may below
3:43:01it from 25,000 to 285,000 so the Box
3:43:04itself signifies what that nerds call
3:43:07the inter quartile range basically all
3:43:10the values between q1 or quartile 1 and
3:43:14cortile 3 had a typo there got to fix
3:43:16that anyway that's why it was so
3:43:17important that previously we calculated
3:43:19that first quartile and third quartile
3:43:21and if you remember from that there
3:43:23quartiles so 50% of the data Falls
3:43:26within this box and if we look up we
3:43:28were to draw imaginary lines into our
3:43:30histogram we can see that about 50% of
3:43:33the data does fall within this the next
3:43:36up inside of here is a line that is for
3:43:39the median in this case our median is
3:43:41990,000 and then we have our average of
3:43:4490
3:43:455,000 which as we discussed previously
3:43:48the average is going to be higher here
3:43:50because we have things all the way out
3:43:52here called outliers basically dragging
3:43:55that average higher and outliers are
3:43:57signified by these dots outside of the
3:44:01whiskers themselves these whiskers are
3:44:03the lines and the lines themselves
3:44:05extend to the minimum and the maximum
3:44:09and these are just relative mins and
3:44:11Maxes they're not necessarily the true
3:44:14men and Max anyway so that's a box and
3:44:17whisker chart and frankly by themselves
3:44:20I don't think they're really great but
3:44:22when you pair them with other
3:44:24categorical values I find them super
3:44:27interesting so let's actually build this
3:44:29visualization so you can come over to
3:44:31this box plot2 Tab and I have our data
3:44:35inside of it none of it is filtered it
3:44:38has all the different job titles and all
3:44:40their Associated salaries for this I'm
3:44:42going to select column M and then also
3:44:44holding control I'm going to select
3:44:46column A then from there go in and
3:44:48insert and go to recommended and from
3:44:50there look at the box and whiskers chart
3:44:53which looks like it's already pulling it
3:44:55up for us so let's pop this bad boy in
3:44:57now one drawback of these box and
3:44:59whisker charts in Excel is unlike that
3:45:02last box plot that I made I custom made
3:45:05this in order to make it appear in this
3:45:07horizontal fashion you can actually do
3:45:10that you can only have the option to
3:45:11have them vertical up and down anyway
3:45:14this is pretty close of what we want to
3:45:16get the main problem I'm noticing right
3:45:18now is we have outliers up to 1.2
3:45:22million and it's really with the data
3:45:24around 100 150,000 it's really hard to
3:45:28actually look into those boxes so I'm
3:45:30going to change this yvalue scale double
3:45:33clicking on the Y AIS I'm going to
3:45:36change the maximum to 300,000
3:45:39additionally since we're here I'm going
3:45:40to change that number formatting to use
3:45:43that 0k value then also I'm finding the
3:45:46color is a little hard to actually see
3:45:49these x's in here so under series option
3:45:53selecting fill in line fill I'm going to
3:45:56change this color to more of a lighter
3:45:59blue okay and that's definitely easier
3:46:02to read I'm going to add a vertical
3:46:04access of salary USD I'm also going to
3:46:07bold it all to make it a little bit more
3:46:08readable and then from there change that
3:46:10chart title to what are the top paying
3:46:13jobs in data science all right getting
3:46:15into actually analyzing this and getting
3:46:18insights from it now one drawback out of
3:46:21this is there's not an easy way to sort
3:46:25these values right here right now I'd
3:46:27normally put them high to low I'd
3:46:28probably put them high to low based on
3:46:30median salary but they've been put into
3:46:34this graph based on the order that they
3:46:36first appear over here in column A and
3:46:40that's when they pop up so that's the
3:46:41order so technically I could go through
3:46:43and sort this column
3:46:45alphabetically but that's going to take
3:46:47a little bit too much time if you want
3:46:48to do that feel free to try that out
3:46:50anyway it looks like roles like machine
3:46:52learning engineers and also software
3:46:55Engineers have a pretty large inter
3:46:58cortile range or that where that 50% of
3:47:00that data Falls so there's a basically a
3:47:02wide range of data or salaries you could
3:47:04find with that whereas data nerds data
3:47:07scientists data analysts and data
3:47:08Engineers have a tighter band also as
3:47:11expected those data analysts and
3:47:12business analysts have some of the
3:47:14lowest median salaries where something
3:47:16like the data engineers and the senior
3:47:18roles have even higher median salaries
3:47:21overall this is pretty great at going in
3:47:23comparing values I would probably work
3:47:25with this more to fine tune it to only
3:47:28have a couple of job titles in it and
3:47:30for that we can use something like
3:47:32slicers which will be covering in an
3:47:34upcoming chapter well the next chapter
3:47:37when we get into Advanced Techniques in
3:47:38Excel so we'll be able to customize this
3:47:40further once you have that knowledge all
3:47:42right you now have some practice
3:47:43problems to go through and get more
3:47:45familiar with those histograms and all
3:47:47scope box and whisker charts in the next
3:47:49lesson which is a quick one we're going
3:47:51to be moving into spark lines which is
3:47:53the final lesson in this chart overview
3:47:56with that I'll see you in the next
3:48:01one moving into this last lesson on
3:48:03charts focusing on spark line spark
3:48:06lines are basically ways to insert mini
3:48:10charts into a cell that summarizes data
3:48:14that's next to it if your data is coming
3:48:17in a horizontal form similar to this
3:48:19table you probably have the possibility
3:48:22of considering inserting a spark line
3:48:24we're going to going through how to make
3:48:25them but also customizing it all right
3:48:27for this we're going to be using the
3:48:28spark lines workbook for this we have
3:48:31like usual our data Tab and then our
3:48:33original tab that calculates data off it
3:48:35and for this data set we're just looking
3:48:37at what are the counts of the different
3:48:39job titles based on month so this is
3:48:41basically horizontally oriented this is
3:48:43great for a spar
3:48:47line so how we're going to do this well
3:48:49we'll go ahead and select the data only
3:48:51so C4 to n10 then come up into the
3:48:54insert tab then right here we have this
3:48:57section on spark lines we can insert a
3:49:00line column or a win loss we'll just
3:49:02start with column to start with and it
3:49:04fills in for the data range C4 to 10 but
3:49:07it wants us to choose where you want the
3:49:09spark lines to be placed so the location
3:49:11range and click this Arrow here and then
3:49:13from there actually drag it next to it
3:49:16all close this Arrow back and click okay
3:49:20anyway I wanted to demonstrate that bar
3:49:21chart because it's not really that great
3:49:23for here remember anytime we're doing
3:49:25continuous data in this case we're doing
3:49:28that monthly data I'm going to want to
3:49:29use something like a line chart instead
3:49:31so I can easily change it by coming up
3:49:33here selecting all of our different data
3:49:35selecting that spark Line tab and then
3:49:37just changing it to I can change
3:49:39something like win loss which no really
3:49:41data from this line chart that's what we
3:49:43really want from this now getting into
3:49:45the customization of this I really
3:49:47personally I'm like blue so we're going
3:49:49to stick with the blue color but we
3:49:51could change the color if we want to and
3:49:53the other thing we change is the marker
3:49:54color right now we don't have any
3:49:55markers on it we can actually change
3:49:57which markers are right here in the show
3:49:59selection right here so I can select the
3:50:02high points right now it's going to
3:50:03highlight all of them red uh low point
3:50:06also red negative points there's no
3:50:08negative point you also do the first
3:50:11point which I don't really find much
3:50:13value in that or last point and then
3:50:15actual finally the markers itself you
3:50:17just put every single one of them with a
3:50:18marker I really like this High Point and
3:50:21this low point and we can customize this
3:50:25the high points I would really want to
3:50:26call out to be a green color right now
3:50:30this green that's sort of hard to see so
3:50:33I'm going to change it to something a
3:50:34little bit darker and Bam we can see
3:50:36that one a little better the red for the
3:50:37low point I'm going to keep it as is and
3:50:39the last thing is all this data has
3:50:41Bally a grid around it I'm just going to
3:50:43add that in real quick by selecting all
3:50:45the cells come up into home into the
3:50:48borders I'm going to put in all borders
3:50:50around it then it looks like I have a
3:50:51double line right here for this lower
3:50:53one so I'll insert this bottom double
3:50:56border and then finally I'm going to put
3:51:00a thick border around this all bam we
3:51:03have our final visualization there now I
3:51:06can go through and see things like okay
3:51:10with that analyst and other analyst we
3:51:12saw spikes in January but things like
3:51:14thata Engineers we didn't see a spike
3:51:17however all the job titles ran to a
3:51:19similar problem where apparently they
3:51:20ran out of budget and the least amount
3:51:23of jobs were posted in November and
3:51:25December so this's a pretty cool feature
3:51:26to show some quick snapshots about the
3:51:29data you're looking at right you now
3:51:31have some practice problems to go
3:51:32through and basically practice making
3:51:34some of these spark lines we're going to
3:51:36next be jumping in the next chapter it's
3:51:38our final chapter of the basic section
3:51:41and it's going to be focusing on
3:51:43Advanced features inside spreadsheets
3:51:45such as tables formatting and how to
3:51:47collaborate with others it's our last
3:51:49section before we build our first
3:51:51project so with that I'll see you in the
3:51:53next chapter on Advanced
3:51:58spreadsheets then nerds welcome to this
3:52:01last chapter in the basic section
3:52:04focusing on Advanced features and
3:52:06spreadsheets there's a last chapter
3:52:07we're going to be covering before we get
3:52:08into our first project and this chapter
3:52:11is broken into three different lessons
3:52:13this one right here is going to be on
3:52:15tables how to use tables how to use
3:52:17things like slicers and how to
3:52:18manipulate them second lesson is on
3:52:20formatting not just on making cells look
3:52:23pretty but developing conditional
3:52:25formatting rules in order to highlight
3:52:28CES according to well a certain rule
3:52:31pretty interesting feature within Excel
3:52:33and the third lesson is on collaboration
3:52:35for a project we're going to be making a
3:52:37dashboard and so we need to enact
3:52:40certain measures in order to protect it
3:52:42and prevent people from going in and
3:52:44messing it up and so we're going to go
3:52:46over a lot of features in order to set
3:52:47it up properly anyway back to this
3:52:49lesson what are we going to be doing for
3:52:50it well first we're going to start out
3:52:52by using a smaller subset of our data
3:52:56set basically 15 rows and creating your
3:52:58first table we're going to be
3:53:00manipulating it using custom formulas
3:53:02that we really haven't seen before along
3:53:04with using some other ones that we have
3:53:06seen before in order to calculate totals
3:53:08subtotals and Aggregates by the end of
3:53:10this lesson we're going to be building a
3:53:12mini dashboard to analyze that histogram
3:53:15that we talked about in our previous
3:53:17lessons specifically we're going to add
3:53:19slicers to it in order to be able to
3:53:21filter down and look at a subset of data
3:53:24that we're most interested about and
3:53:26that's all could be done without the
3:53:28help of tables for this lesson we're
3:53:30going to be using the tables workbook in
3:53:33chapter
3:53:364 for this you're going to start in the
3:53:38tables intro original sheet and then the
3:53:40final one's going to be what we're going
3:53:41to eventually get to all these are going
3:53:43to be labeled similarly with the
3:53:44original and final and we're all going
3:53:46to be working with the original it
3:53:47should look like the final when you get
3:53:48done with this so let's dive into
3:53:49creating our first table first thing you
3:53:52have to do is make sure that we're
3:53:53selected somewhere in here we don't
3:53:54necessarily need to select the full
3:53:55table but just somewhere in here from
3:53:57there we'll go into the insert Tab and
3:53:59we'll insert a table also notice that we
3:54:02can use the shortcut control t for this
3:54:05so I'm going to do that instead and for
3:54:07this it automatically pinpoints the
3:54:10rightmost cell and the bottom most cell
3:54:12and we need to make sure we have this
3:54:13check mark enabled of my table has
3:54:15headers because we have well call them
3:54:17headers and Bam we just made our first
3:54:19table this lesson's over but seriously
3:54:21let's actually get into exploring this
3:54:22table design tab that now appears
3:54:24anytime you're selected to the table if
3:54:26I click off of it it disappears anyway
3:54:28we're going to first look at the table
3:54:30name and i' like to have a table name
3:54:32that's easy to reference so I'm going to
3:54:34just name it something like jobs it's
3:54:36going to come into handy naming it
3:54:38something simple whenever we're making
3:54:40formulas later for this now we'll get to
3:54:42this section in a little bit on tool and
3:54:44external table data but I want to move
3:54:47over to the style options you can play
3:54:49around with some of these options here
3:54:50where you can highlight the First Column
3:54:52or you can highlight the last column has
3:54:54a lot of different formatting options
3:54:56with it but what I really like is this
3:54:58color formatting if I'm not really
3:55:00liking the color that it's given to me
3:55:02just come over here select a new one so
3:55:04we'll get back to table design in a bit
3:55:06but what's really the benefit of this
3:55:09table well one thing is you can easily
3:55:11add data to a table and it will will
3:55:14autofill let me show you let's say I
3:55:16wanted to add a new column with a solid
3:55:19year average copy whenever I enter this
3:55:21new column name and press enter it
3:55:23automatically fills this in I can the
3:55:26skills are sort of covering this up
3:55:27right now sorry about that and I can
3:55:29make this a little bit bigger but you
3:55:31can see we have salary or average copy
3:55:33now included within this table and I can
3:55:36verify that it's included also in this
3:55:37table by if I want to go to resize table
3:55:39it will say that now it goes to
3:55:42k16 now for this I just want to copy the
3:55:45results of the salary year average
3:55:47column over here in h so what I'm going
3:55:49to do is press equal to and I'm just
3:55:51going to select the cell over here of H2
3:55:55now this is what I was talking about
3:55:56whenever I said tables have their own
3:55:58unique formulas what it's going and
3:56:00doing here is it's referencing the
3:56:01salary or average column which is this
3:56:04portion right here and then it's also
3:56:06using this at symbol to basically refer
3:56:08to this is the same point in the row of
3:56:11H2 that is a K2 anyway when I go ahead
3:56:14and press enter Watch What Happens we
3:56:17actually fill in all the different
3:56:18values of this so if I were to actually
3:56:20double click into this one down here we
3:56:22still have that same syntax of we're
3:56:25selecting the Sal your average column
3:56:27and we're using that at value value to
3:56:29get the one that corresponds in that
3:56:31same row now let's dive deeper into
3:56:33these different formulas we can use for
3:56:35this table so I'm going to come over
3:56:36here into column n and for this remember
3:56:39we named our table jobs so I'm just
3:56:42going to type in jobs and I have two
3:56:45tables in here one called job one jobs
3:56:47you only have one popping in here anyway
3:56:49it automatically pops up so I'm going to
3:56:51select jobs and now whenever I do this
3:56:55I'm going to press enter it's using our
3:56:57modern dynamic arrays basically to fill
3:57:00in all the data that we have over here
3:57:05inside of our table so pretty unique in
3:57:07how we can reference this now what
3:57:09happens if we wanted to also include the
3:57:11column headers up at the top well I can
3:57:12type in jobs and then from there I'm
3:57:15going to add a square bracket and we
3:57:18have a few options popping up right now
3:57:20it looks like it's just a column titles
3:57:22but if we scroll down we have these
3:57:25values here with hashtags in it
3:57:27specifically I want with the column
3:57:28headers so I'm going to put hashtag
3:57:31headers I'm going to put a close bracket
3:57:33on this and then press enter and now we
3:57:36have the column headers across the top
3:57:38now that's a little bit too much work
3:57:39having to do two different formulas for
3:57:41this if instead I wanted to do job and
3:57:44then square bracket and see the options
3:57:47available I can see I have an all a data
3:57:49only a headers and a totals row totals
3:57:52row we're going to get to a little bit
3:57:53so we'll do the all for now and if I go
3:57:55ahead and press enter bam we now have
3:57:58our data with our column headers and
3:58:01also the data itself but what happens if
3:58:04you want to just access certain columns
3:58:06well I thought you never asked that well
3:58:08once again I can type in something like
3:58:10jobs but the square bracket and then we
3:58:12have a list of different columns
3:58:14available let's do the salary year
3:58:16average and do a close bracket once
3:58:18again this is going to provide the data
3:58:20values only if we wanted to include the
3:58:23specific header for this I once again
3:58:25need to put in jobs and this time I'm
3:58:27going need to specify not only the
3:58:30headers so I need to put this in its own
3:58:33square brackets but I'm also going to
3:58:35have to do a comma put another square
3:58:37brackets and put salary year average
3:58:41within its own brackets so it's almost
3:58:43like a list of items if you're familiar
3:58:45with python this would be like a list
3:58:46anyway we have the headers in Brackets
3:58:49and we have salary year average in
3:58:50Brackets pressing enter we get salary
3:58:53your average up at the top now honestly
3:58:55an easier way to do this all is to well
3:58:57use that all command or hashtag all but
3:59:00it has to be put within its own square
3:59:02brackets then from there a comma and
3:59:04then we want to say hey the subset only
3:59:06that we're providing for this is salary
3:59:09year average close that bracket and then
3:59:11close the entire brackets for jobs now
3:59:14from there when we run it we get the Sal
3:59:16year average along with all the column
3:59:18values at any time if you forget that
3:59:20it's not that big of a deal as you can
3:59:23just go through and put an equal sign
3:59:25and like we did previously I could just
3:59:27highlight well not that um our salary
3:59:30your average column and look it
3:59:32automatically populates with that same
3:59:34formula above here and when I press
3:59:36enter boom it pops up there so don't
3:59:38think you have to memorize these
3:59:39formulas that I just went over but what
3:59:41do all these formulas actually provide
3:59:43any value value for well let's look at a
3:59:45use case let's say I wanted to identify
3:59:48jobs that whenever we looked at the
3:59:51skills we could find out if they
3:59:53contained the skill of Excel or not so
3:59:56I'm going to create this new column over
3:59:57here and call it Excel and for this
4:00:01we're going to be using the search
4:00:03function which we need to provide what
4:00:06text we want to actually find
4:00:07conveniently I put it in the column
4:00:08header so I'll go ahead and just select
4:00:10it and automatically populates the
4:00:12formula for this then from that we need
4:00:14to go to the next parameter of within
4:00:16text we're trying to look at that job
4:00:18skills column it puts that at symbol at
4:00:21the front of job skills to basically
4:00:23signify look at that row then from there
4:00:26I'm going to go ahead and close the
4:00:27parentheses and press enter so for that
4:00:29search function it provides the N
4:00:31numerical location of excel in here
4:00:35Excel is 36 characters deep into this so
4:00:38I'm just going to modify this cuz I
4:00:39don't really care about the number of
4:00:41that I'm going to say I'm going to use
4:00:43the is number function which checks if
4:00:46it's a number and then returns true or
4:00:49false in this case we have True Values
4:00:51so we know that for these columns if
4:00:54they contain Excel or not they'll have
4:00:56true so that's how I find myself using
4:00:58these different formulas and
4:00:59understanding how to actually manipulate
4:01:01them anyway let's get into our next step
4:01:03let's say we wanted to include some sort
4:01:05of totals Row in order to maybe
4:01:07calculate median salary how many job
4:01:09postings there were Etc so we'll go into
4:01:12this table design Tab and I'm going
4:01:13going to select the total row and now
4:01:17down here in row 17 we have total
4:01:19written down here along with a bunch of
4:01:22well blank values except for all the way
4:01:23to the right looks like it puts us the
4:01:26number of 15 which is the total of these
4:01:29now going over to that salary year
4:01:31average column I can basically select
4:01:34this totals row right here and you
4:01:36notice a drop down appears right here
4:01:38from here we can select some basic
4:01:40statistics average count min max
4:01:43variance go ahead and select average
4:01:45that's the average of this column right
4:01:46here so pretty neat I'd go through and
4:01:48if I wanted to do other columns as well
4:01:50that now you can also go into here and
4:01:52select more functions and then like we
4:01:54said we want to calculate Median on this
4:01:56salary we could go ahead and select this
4:01:59function of median but I'm actually
4:02:01going to recommend another approach you
4:02:04see if we double click inside of here we
4:02:06actually see that this totals column is
4:02:10using a function specifically the
4:02:12subtotal function function so let's
4:02:15actually build this out from scratch
4:02:16without selecting it luckily we have the
4:02:18salary your average copy column over
4:02:19here so I'm going to go in and I'm going
4:02:20to type in subtotal and it returns a
4:02:24subtotal in a list or database first is
4:02:27the function number what do we want it
4:02:29to actually do and this has even more
4:02:32values available to it that you can
4:02:35actually select from and perform on this
4:02:39so in this case let's say I wanted to
4:02:41find out what the max value is I would
4:02:43plug this in it would be 104 and then
4:02:45for the reference for this well we're
4:02:47just going to select this salary year
4:02:49average copy column it automatically
4:02:51transformed into this special syntax and
4:02:55then add a closing parenthesis and press
4:02:56enter and so now we have the max salary
4:02:59which looking at this it's true but if
4:03:01we go back into this and actually
4:03:02inspect what values are available in
4:03:05this function number we can see that
4:03:08median is not available in here so what
4:03:11are we going to do well there's another
4:03:14function we're not going to use median
4:03:16but that I recommend instead of using
4:03:18sub total and for this one we're going
4:03:20to use the aggregate function and this
4:03:23returns an aggregate in a list or
4:03:25database it's similarly designed where
4:03:28it has a function number but with this
4:03:30one we have a lot more options including
4:03:34things like CTO and stuff like that
4:03:35anyway it has median available as number
4:03:3812 now the second parameter on options
4:03:42allows us to select a host of options uh
4:03:46no pun intended for allowing us how we
4:03:48want to actually perform this aggregate
4:03:50basically do we want to maybe ignore
4:03:53hidden rows or do we want to ignore
4:03:55error values in my case I don't really
4:03:58want to ignore anything so I'm just
4:03:59going to do number four and then finally
4:04:02we need to insert the array or the
4:04:03column itself in this case we want
4:04:05salary year average closing the
4:04:07parentheses on this and pressing enter
4:04:09we get our median value of 94,000
4:04:16now depending how fast your computer is
4:04:18you're going to run into some
4:04:19limitations here I have in the table
4:04:22limits original tab which is the next
4:04:24one we're going to be working with in
4:04:25this uh portion of the lesson it has
4:04:28around well 32,000 which is in the data
4:04:30set anyway we're going to run into some
4:04:33limitations as I'm going to show I'm
4:04:34going to encourage you to just watch
4:04:36along uh me do this and then from there
4:04:39basically decide if you think you have a
4:04:41strong enough computer or not to
4:04:42continue on to do this
4:04:44um but if you have a pretty uh basically
4:04:46slow computer I wouldn't necessarily
4:04:47follow along with this anyway I'm going
4:04:49to convert this into table by selecting
4:04:50any portion in here pressing contrl T it
4:04:53selected all the different values and
4:04:55that table has CS so now we've converted
4:04:57this into a table and one of the
4:04:59benefits we haven't really discussed yet
4:05:01is the ability to actually filter data
4:05:04because it automatically provides this
4:05:05filter up at the top now I'm going to go
4:05:08ahead and filter this down based on a
4:05:10data analyst job title and when I go
4:05:14through and actually select this to just
4:05:15select it at analyst and press okay it
4:05:18runs pretty quickly but I have run into
4:05:22problems in the past especially working
4:05:24with smaller computers where it takes a
4:05:26while to do this I'm working with about
4:05:2924 GB of RAM on this virtual machine so
4:05:34if you're something at like8 or even 4
4:05:37I'm going to highly recommend that you
4:05:39may not perform this exercise
4:05:44so moving to this last exercise of this
4:05:46lesson I've gone ahead and condensed
4:05:48down this data set you can go into
4:05:50histogram original and our previous data
4:05:53set I basically shorn it down to these
4:05:55four columns and limited to only
4:05:59positions that have a salary year
4:06:02average value listed basically if
4:06:04there's blanks I remove those rows so
4:06:06it's about 208,000 rows anyway this is
4:06:08what we're going to be manipulating for
4:06:09this this shouldn't lock up your
4:06:11computer if you have a basically a
4:06:14computer with less RAM and we're going
4:06:16to convert this into a table first
4:06:18pressing control T I select all the
4:06:20values on here and press okay so now we
4:06:23have a title now also in this sheet you
4:06:26may have noticed hopefully that it's
4:06:27been on the screen I have this histogram
4:06:29here which is basically aggregating the
4:06:31data from this Delta column on salary
4:06:33year average anyway we're going to be
4:06:37manipulating this further we want to
4:06:39basically make this into a dashboard so
4:06:41we can go through and maybe filter for
4:06:42different job title different job
4:06:44schedule types or different job
4:06:46countries and it can be mildly
4:06:48inconvenient to come up here and
4:06:50actually select this arrow and then go
4:06:51through and select the values want
4:06:53that's why slicers are great so with our
4:06:56table selected I'm going to go into
4:06:57table design and then from there under
4:07:00Tools I'm going go to insert slicer
4:07:03we're going to be entering in both a job
4:07:04tile short job schedule type and a job
4:07:07country slicer so all three are here now
4:07:11I'm going to go ahead and position them
4:07:12make them look a lot neater all right
4:07:14got them cleaned up and then from there
4:07:16I can go ahead and actually select the
4:07:18slider sir and if you notice this slicer
4:07:20tab pops up conveniently labeled this
4:07:22slicer has a caption on it or a title as
4:07:25well and I can just rename it basically
4:07:27to a better visually appealing title in
4:07:30this case I want it to call job title
4:07:32and then it updates here for job title
4:07:34I'm going to do the same for the other
4:07:35two updating it to schedule type and
4:07:38then also Country Now by default this
4:07:41slicer and all the slicers have all the
4:07:43value selected so if I wanted to to go
4:07:45in to actually select a value I could do
4:07:48something like well we want to look at
4:07:49data analyst I just select data analyst
4:07:51it's going to clear all those other ones
4:07:53and then only select that analyst as you
4:07:55notice it took a second for it to
4:07:56actually load that's why with this
4:07:5820,000 rows of data even that's a little
4:08:00high for tables I recommend it around
4:08:0210,000 if you're using tables anyway we
4:08:05have it filtered down to data analyst I
4:08:07could also do it down to
4:08:09fulltime along with filtering it for U
4:08:13basically
4:08:14uh I want to do United States if you
4:08:15notice these values are gray out that
4:08:16means there's no country basically
4:08:18available with the current selections
4:08:19that I have of data analyst in full-time
4:08:22so that's what that means there but I
4:08:23can go into that for United States
4:08:25selecting it and Bam we now have our
4:08:29final basically visualization but what
4:08:31happens if I want to maybe look at
4:08:34multiple different values what if I
4:08:36maybe want to look at both data analyst
4:08:37and business analyst well in that case
4:08:40you want to select this box up here and
4:08:42it allows multi I select and so I enable
4:08:45it and now I can go through and select
4:08:46something like business analyst and this
4:08:49provides both those values along with I
4:08:51wanted to look at full-time and also
4:08:52part-time I could enable the multi
4:08:55select on this schedule type and select
4:08:57part-time and Bam now we have multiple
4:09:00values selected for this along with the
4:09:02United States and this makes the
4:09:04dashboards that you're building a lot
4:09:05more interactive and a little bit fun to
4:09:08play around and to visualize the
4:09:09different data all right we have some
4:09:11practice problems for you now to go
4:09:13through and dive into not only creating
4:09:15tables manipulating them but also adding
4:09:18and playing with slicers as well with
4:09:20that we'll see you in the next lesson
4:09:21we're going to be jumping into
4:09:23formatting specifically conditional
4:09:25formatting so see you
4:09:30there in this lesson we're going to be
4:09:32focusing on formatting and not just self
4:09:35formatting where we're going through and
4:09:36adding borders and colors but also
4:09:39conditional formatting where a cell's
4:09:42basically formatting highlighting will
4:09:44update dynamically based on a value in
4:09:47the first example we're going to focus
4:09:49on Cell formatting specifically we're
4:09:51going to go back to that table that
4:09:52we've worked with previously that does a
4:09:55count of data science jobs over the
4:09:57month anyway we're going to go through
4:09:58and actually format it using all the
4:10:00different functions we can in order to
4:10:02make it look pretty like I made it from
4:10:04there we're going to move into our first
4:10:05conditional formatting example where
4:10:07we're going to look at basically
4:10:08highlighting based on a job title those
4:10:11that are basically high and those that
4:10:13are low highlighting them appropriately
4:10:15green or red and then in our final
4:10:17example we're going to move on besides
4:10:18using color scales to also using things
4:10:21like datab bars and also icon sets to
4:10:24make it look a lot more Dynamic we're
4:10:26also going to go over best practices on
4:10:28what not to do cuz sometimes you can go
4:10:30overboard in how much you're actually
4:10:32coloring a table and you can make it a
4:10:34little distracting and and ultimately
4:10:36not meet your goal for this lesson we'll
4:10:38be working with our formatting notebook
4:10:41in chapter 4 as usual all the data is
4:10:44located in the little data Tab and we'll
4:10:46be starting with the underscore original
4:10:49of each of these sheets and then it
4:10:51we'll get to in this case format
4:10:52original we'll have what it looks like
4:10:54format
4:10:57final for the cell formatting we're
4:10:59going to be using this format original
4:11:00sheet and we're going to be focused on
4:11:02this Home tab here so I'm actually going
4:11:04to leave it expanded and for this we're
4:11:06going to make this to where well what
4:11:09this table looks like by going through
4:11:10and actually formatting using all the
4:11:12different features in here so the first
4:11:13thing we need to do is highlight it all
4:11:15and actually remove the formatting so
4:11:17with it all selected I can go to editing
4:11:20and then clear and I can either clear
4:11:22all which is what I don't want to do I
4:11:23want to do clear format and Bam now we
4:11:27have an ugly table that doesn't really
4:11:29make a lot of sense now previously we
4:11:31were mess with tables so I could
4:11:32highlight from B3 to 010 and make this
4:11:35into a table by coming up here to format
4:11:38as table basically selecting the color
4:11:40that I want saying that it has headers
4:11:43and allowing it to update there's
4:11:45definitely an option um but I'm not
4:11:47necessarily a fan of this so I'm going
4:11:49to clear this by pressing contrl Z Now
4:11:51an underused feature of formatting is
4:11:53this cell Styles tab right here so I'm
4:11:56going to go ahead and select the months
4:11:58up here basically the titles and for
4:12:00cell Styles they actually have a lot of
4:12:03pretty unique formatting you can see
4:12:05happening in the background so I'm going
4:12:07to try out in this case I'm going to try
4:12:09out heading two which is pretty neat
4:12:11because it makes it bold slight bigger
4:12:13and it puts a little line underneath it
4:12:16I could do something also where I
4:12:17highlight all the rows over here and
4:12:19then make this into maybe heading three
4:12:22and then all these values in here are
4:12:24calculations so technically I could just
4:12:27highlight this all and for the cell
4:12:29Styles I could come up to the top here
4:12:31and select hey this is a calculation and
4:12:34this not a bad looking table uh but not
4:12:36necessarily all I want to do so I'm
4:12:37going to just remove this all instead
4:12:40I'm going to start with my months I'm
4:12:41going to make them bold and also add a
4:12:44light gray background I'm going to do
4:12:46the same thing over here for the values
4:12:48in my rows and then from here we're
4:12:49going to get the actual column grid
4:12:51lines put in I'm going to only select C3
4:12:54all the way down to o10 I'm going to
4:12:55show you why and I'm going to add an all
4:12:59borders so this is NE it add it adds all
4:13:01borders to it what I'm going to also add
4:13:04this which will add a little bit of
4:13:05flare to it is a thick outside border so
4:13:08now we got a thick outside border around
4:13:10all of this and I'm going to do the same
4:13:13with this one of an all borders and then
4:13:16a thick outside border now it did remove
4:13:20that thick outside border that I had on
4:13:21this line between B and C so I'm
4:13:24actually going to go ahead and put that
4:13:25back in by just clicking it next thing I
4:13:27want to do is format these with a comma
4:13:30so I'm going to come up here and well
4:13:32add a comma and then unfortunately it
4:13:35adds this space in here and makes this
4:13:37table bigger than what you can see now
4:13:39I'm going to first remove the decimal
4:13:41places and then in order order to fix
4:13:43this I'm going to highlight all the
4:13:45different columns through here to
4:13:46January and just double click on one of
4:13:48them to make them slightly smaller
4:13:51anyway it's still not fitting completely
4:13:53on here and I want this to fit within
4:13:55the view here so I'm just going to
4:13:57select this all and I'm actually going
4:13:59to make these values slightly smaller
4:14:02and I'm not liking the positioning of
4:14:04these it looks like it's lower now that
4:14:06I made this smaller so I'm going to
4:14:07actually Center this this do a middle
4:14:09align basically move it up slightly all
4:14:12right my OCD is no looking good all
4:14:14right now this is looking good now the
4:14:15last thing we want to do is add a title
4:14:18to this basically describe what is this
4:14:20table that we're looking at and I want
4:14:22to insert this in up on the top row but
4:14:25I basically want it centered over this
4:14:26table so what I can do is highlight from
4:14:28b11 and from there select up here for
4:14:33merge and also I want to Center because
4:14:35that's I want my text Center during this
4:14:37and from there I put in hey this is the
4:14:38data science job count tracker and for
4:14:41the cell style I'll make this heading
4:14:45one now let's get into conditionally
4:14:48formatting this table and specifically I
4:14:52want to say if I'm looking at data
4:14:53analyst I want to be able to look across
4:14:55here and see which ones are the highs
4:14:57and the lows right now I have this grid
4:14:58lines and I can see that based on the
4:15:00green and red or the highs and lows but
4:15:02I want to actually be able to see this
4:15:03in this table right here and so
4:15:04underneath the Home tab we have this
4:15:06conditional formatting available we're
4:15:09going to focus on these three right here
4:15:10first and that is datab bars and you can
4:15:13see if I put it in it's basically
4:15:14looking like a you know like a bar chart
4:15:17color scales allows us to do well
4:15:19different color formatting with it and
4:15:21then an icon set basically allows us to
4:15:23put in a nice looking icon and we're
4:15:25going to stick simple for now we're
4:15:27going to do color scales right now I
4:15:29have C4 through N4 selected I'm going to
4:15:32go ahead and select this green to Red
4:15:35which is not bad if we're looking this
4:15:37right this is doing exactly what I want
4:15:38I want August which is the highest to be
4:15:40highlighted green to attract my eyes to
4:15:42it and then I want the red to be
4:15:43November and December cuz a Lis I want
4:15:45to attract attention to it but we want
4:15:46to highlight the entire table here so if
4:15:50I were to actually select the entire
4:15:52table if you will from C4 all the way
4:15:54down to n10 go into conditional
4:15:56formatting color scales and do the same
4:15:59thing you're going to notice it
4:16:01basically does these bands but it does
4:16:04this entire
4:16:06table all formatted together and this is
4:16:09not what we necessarily want of course
4:16:11the total road is going to be the
4:16:13highest I want to look through that row
4:16:15and actually see where I should be
4:16:16actually looking so anytime we need a
4:16:18clear mess with any rules we come into
4:16:19conditional formatting and go to clear
4:16:21rules you have clear from selected cells
4:16:24or entire sheet we're just going to do
4:16:27the entire sheet then we're going to go
4:16:28back to where we were before of
4:16:30selecting just the data analyst values
4:16:32going into conditional formatting color
4:16:34scales and I'm going to go to this green
4:16:36white red I actually want to try to
4:16:38limit as many colors as I do two is
4:16:41enough so I'm going to go green white
4:16:42red red and I really like this one
4:16:44better now I don't need to necessarily
4:16:46go through once again of selecting
4:16:47senior data analyst doing this again
4:16:49what I would do instead is I'm going to
4:16:51select data analyst here and then come
4:16:54into this home menu up here and you
4:16:56notice this paintbrush this is a format
4:16:59painter in the instructions it basically
4:17:01says select the content with with the
4:17:03format you like click format painter and
4:17:05then select something else to
4:17:06automatically apply the formatting so
4:17:08from here I can just paint my formatting
4:17:11on unfortunately this doesn't have a
4:17:13shortcut so I have to go do go back up
4:17:15every single time it removes their
4:17:17marching ants reselect the format
4:17:19painter and go through and select it but
4:17:22now we have this formatted how I want it
4:17:24where I can look at a certain Row in
4:17:26this case I look at data analyst see
4:17:28what some of the highest are and Senior
4:17:29data Engineers I can see how they
4:17:31contrast to the other job titles
4:17:33additionally which going be jump into a
4:17:34little bit more later is we can go into
4:17:37manage rules and we can see the current
4:17:40conditional formatting appli
4:17:43right now I have show matting formatting
4:17:45rules for current selection I'm selected
4:17:46the top cell right up here so there's no
4:17:49conditional formatting if I were to
4:17:51change this to just this worksheet I can
4:17:54then if I expand this down I can see how
4:17:57this applies this this type of
4:17:59formatting of the red white green
4:18:01applies to each of the different cells
4:18:03and if I needed to actually control what
4:18:05cells are actually selected I could do
4:18:08that I could have also gone through
4:18:10instead of done that copy formatting and
4:18:12pasting I could done a duplicate Rule
4:18:14and modifying the code as well but I
4:18:16decided to do my way instead anyway this
4:18:18is where you need to go if anytime you
4:18:20need a manage conditional formatting we
4:18:22cck
4:18:25okay let's crank this up a notch and get
4:18:27into using some more advanced
4:18:29functionality with conditional
4:18:30formatting here we have a new table you
4:18:32haven't seen before basically it has all
4:18:34the different job titles the counts of
4:18:36those jobs aggregated from our data
4:18:38sheet the median salary what is their
4:18:41work from home percentage or likelihood
4:18:44based on the jobs and then finally I
4:18:47have this job rank right here which
4:18:49basically uses these cells that are
4:18:51hidden right here that if we actually
4:18:53expand it out goes through and
4:18:56normalizes the values so in this case
4:18:59the job count normalize it between zero
4:19:01and one so this job count is 90 is the
4:19:04highest so it gets a value of one where
4:19:06it's the lowest gets a value of zero
4:19:07anyway I did this for all the different
4:19:09values and then from there provide a
4:19:11certain waiting factor of like 0453 and
4:19:140.15 in order to wait it appropriately
4:19:17this is all my bias and how I wanted to
4:19:19actually do it so feel free to adjust it
4:19:21to what you want anyway we have this
4:19:23final job rank in order to assess based
4:19:26on these three values and this is
4:19:28commonly done especially in like kpis
4:19:30and stuff like that so we're going to be
4:19:31making like icons for this column so
4:19:33let's get into formatting our first
4:19:35column we're going to do job count first
4:19:37and for this one I want to have data bar
4:19:40so I'm going to come down into condition
4:19:42formatting into data bars and we'll add
4:19:46these data bars right here I like the
4:19:49bars in this case because we're dealing
4:19:51with a count and we can really see
4:19:53especially data analysts scientist
4:19:55Engineers they really make up the
4:19:56majority of the data here so it really
4:19:58draws your attention to it next up is a
4:20:01median salary we're going to do similar
4:20:03to last time maintain a color scale
4:20:06we're just going to do this first one
4:20:07right here where green is the highest
4:20:09salary and red is the lowest and then
4:20:11one more we're going to do that work for
4:20:12home we're also going to do it in a
4:20:14color scale but for this one let's
4:20:17actually do a different color go into
4:20:19more rules and in this case we have this
4:20:21new formatting rule window right here I
4:20:24have two colors just say I want to do
4:20:26one color I'm going to do white from the
4:20:29lowest value and then we'll do like
4:20:31purple for the highest value anyway this
4:20:34is all basically to show a point this is
4:20:37becoming
4:20:38entirely entirely too much visually
4:20:41distracting if you're if you were to
4:20:43give this to somebody else or a
4:20:44stakeholder where are they supposed to
4:20:46look and actually organize their
4:20:48thoughts on where they should
4:20:50potentially pursue a job right now I'm
4:20:52thoroughly confused at looking at this
4:20:54so let's clean this up a bit and for
4:20:57this I want to make it to where I like
4:20:59maintaining a solid coloro across so
4:21:02that way you know like hey if this color
4:21:05is darker or there's more of this color
4:21:07I should be looking there so in this
4:21:09case we'll make this job count we're
4:21:10going to just clean it up slight
4:21:12slightly for the data bars B going to
4:21:14make this like gradient appearance cuz
4:21:17then I feel we can see the numbers
4:21:18better and it's not too visually
4:21:19distracting for the median salary I
4:21:22really my goal of this is to find jobs
4:21:25that are look say greater than 100,000
4:21:27so let's actually just make highlighting
4:21:29that highlights those jobs that are
4:21:31greater than this value in this case I'm
4:21:33going to come to conditional formatting
4:21:35and enter a new rule this new formatting
4:21:39rule popup comes up against once again
4:21:41and we have a select a rule type this
4:21:44allows us to do things like format all
4:21:47cells based on the value format only top
4:21:49or bottom rank values format only values
4:21:51that are above or below average I
4:21:54personally like this one of use a
4:21:55formula to determine which cells to
4:21:57format and in this case I want to say
4:22:00I'm going to collect this formula thing
4:22:02right here I want to look at you can
4:22:04just select the first item in the item
4:22:06selected so I'm select D3 it's going to
4:22:09go through and actually do all of these
4:22:10don't worry we'll see and for that we
4:22:12want to highlight those that are greater
4:22:14than
4:22:15100,000 and press enter and then right
4:22:18now it doesn't have any format set so
4:22:20I'm going to change this to format and
4:22:22we can control a whole host of things
4:22:24such as the fill border font and the
4:22:27number formatting itself but we're going
4:22:29to stick with that blue theme I'm going
4:22:31to just come down in here and I'm going
4:22:33just select this blue color right here
4:22:35and click okay and then okay again now
4:22:38you notice my formatting is not
4:22:40appearing that's because we have
4:22:43multiple formatting applied to a cell
4:22:45which you can do so in order to fix this
4:22:47we need to come into manage rules and as
4:22:51we see we have both of these applied to
4:22:54it so I actually need to select this one
4:22:55and I need to delete this Rule and click
4:22:59apply and then okay now we're running
4:23:02into our second issue and I slightly
4:23:05misled you earlier when I said that D3
4:23:07works if we go back into manage our
4:23:10rules and we see our formula right here
4:23:12I'm going to double click it we don't
4:23:14need to actually provide an absolute
4:23:16reference to a D3 because it's actually
4:23:18going to evaluate all those cells based
4:23:20on D3 instead we want it to be D3
4:23:23without the dollar sign so it's not an
4:23:24absolute reference and therefore
4:23:26whenever I click okay and okay again bam
4:23:30now it knows appropriately to check the
4:23:33actual cell that it's looking at within
4:23:36the range on whether to highlight it or
4:23:38not moving on to the work from home
4:23:40we're going to keep this similar in that
4:23:42not going to be purple though we're
4:23:44going to change this to Blue instead so
4:23:46going into manage rules we have the
4:23:49actual color right here selected I'm
4:23:51going to just go in and change this to
4:23:53this color that we used previously and
4:23:55click okay and then okay as well so that
4:23:58way it applies it all right the last
4:24:00thing is this the job rank itself and
4:24:03for this we're going to be using icon
4:24:05set
4:24:06specifically I like this one over here
4:24:09on ratings but this becomes a little bit
4:24:12over helming when where we have this
4:24:13rating and also the number next to it so
4:24:16we can actually remove this number in
4:24:18the column we go back into manage rules
4:24:22we can double click on that icon set
4:24:24Rule and we can even further customize
4:24:27when these stars are appearing but I'm
4:24:29going to just go ahead and get to this
4:24:31portion where it says show icon only
4:24:33this allows us to only show the value so
4:24:35going into applying this bam it's now
4:24:38showing the icon I want that icon
4:24:40centered both vertically and also
4:24:43horizontally so bam now whenever I look
4:24:45at this I can see especially since it's
4:24:48all one color my eyes really gravitate
4:24:51to well data scientists and data
4:24:54Engineers based on this full star rating
4:24:57and more of the blue being in this
4:24:59region and that's what I would hope
4:25:01people would go to or gravitate to as
4:25:03well when they're looking at it one
4:25:05quick note in this condition conditional
4:25:06format we didn't cover this highlight
4:25:08cell rules where you highlight greater
4:25:10than or less than or you do a top uh
4:25:12bottom rule where you can highlight the
4:25:14top 10% or top 10 you can also adjust
4:25:16that number anyway I find that myself
4:25:19more using custom rules instead by
4:25:22coming in here into new rule and then
4:25:25actually fine-tuning what I want to do
4:25:28so with the practice problems I'd really
4:25:30dive into actually relying on using
4:25:32these type of options instead and so as
4:25:35I desly hinted to you have some practice
4:25:38problems now to go through and really
4:25:40practice how to do formatting and also
4:25:42more specifically conditional formatting
4:25:44in the next lesson we're going to be
4:25:45move into collaboration and covering how
4:25:48to actually protect your workbooks and
4:25:50your worksheets so that way whenever you
4:25:52share these with co-workers or friends
4:25:55they don't go through and actually mess
4:25:56them up all right with that I'll see you
4:25:58in the next
4:26:02one welcome to this last lesson in
4:26:05spreadsheets advance for we jump into
4:26:06our project and this lesson itself is on
4:26:09collaboration which sounds sort of
4:26:11cheesy but in order to demonstrate what
4:26:13we're actually going to be learning in
4:26:14this lesson we need to actually jump
4:26:16fast forward a little bit and jump into
4:26:18our project so I'm going to open up the
4:26:20salary dashboard which is located under
4:26:23project One dashboard so here's the
4:26:25dashboard that we're going to build in
4:26:27it they have three boxes that you can go
4:26:30through and select this is going to be
4:26:32using data validation which we're going
4:26:34to be learning about in this lesson but
4:26:36it allows you to basically standardize
4:26:38the inputs that we want somebody to
4:26:40actually select in in order to get the
4:26:43results and it prevents them from
4:26:44putting in values that maybe don't exist
4:26:47and then breaking our dashboard so for
4:26:49each of these job titles country and
4:26:51types we have an Associated
4:26:53visualization for each showing the
4:26:55salary by job title the salary by region
4:26:58and then also salary by job type finally
4:27:01at the bottom I have some I call them
4:27:03kpi cards basically outlining certain
4:27:07characteristics or certain indications
4:27:08of the median salary what is the top job
4:27:11platform and then what what is a account
4:27:13of jobs but I can come in here and
4:27:15select something like maybe I wanted to
4:27:16look at business analyst and it's going
4:27:19to filter Down based on this telling me
4:27:22what their median salary is that
4:27:23LinkedIn is probably the best place to
4:27:24go to for this what are the different
4:27:26types of rollers availables and what's
4:27:28available in the job database so the
4:27:30other feature we're going to be going
4:27:31through besides this data validation
4:27:33process that we can do right here is
4:27:35actually protecting your sheets which
4:27:37you can find this here underneath review
4:27:39under protect but anyway if you try to
4:27:43move these cells around you're not able
4:27:46to at all so we're going to be able to
4:27:48design this dashboard in a way that
4:27:51other co-workers won't be able to
4:27:53destroy it additionally if you notice
4:27:55down here at the bottom there's only one
4:27:58sheet in here there's actually other
4:28:00Sheets if I go to unhide here there's
4:28:03other sheets I'll just unhide one of
4:28:05them we'll just unhide data there's
4:28:07other sheets inside of here but if
4:28:09they're not applicable to my co-workers
4:28:11or stakeholders I don't need to have
4:28:12them so I can hide them so that's the
4:28:15another feature we're go on over in
4:28:19this all right nothing be yaen let's
4:28:21actually get into this lesson for this
4:28:22we're going to be using the
4:28:23collaboration workbook in chapter 4 now
4:28:27we're going to be building out these
4:28:29three sheets as we go along and as a
4:28:32sneak peek in this first example we're
4:28:34going to be building out this little
4:28:36portion right here this is going be
4:28:37basically preparing us for our project
4:28:39so a lot of this work is going to be put
4:28:40to good use anyway we're going to be
4:28:42building the simple one right here I'm
4:28:44going zoom in where we have based on the
4:28:46job title we can go through and select
4:28:48it so senior. engineer it's going to pop
4:28:50up with our median salary so that's what
4:28:52we're going to be building with this and
4:28:54specifically we're going to be using
4:28:55this feature of data validation so I'm
4:28:58going to create a new sheet to start
4:28:59with because I don't want to start with
4:29:01the answer right there I'm going just
4:29:02call it calculator I'm going to put in
4:29:05job title here and then median salary
4:29:09below I'm also going to bold these by
4:29:11pressing B and then these are where next
4:29:14to it in column C is where we're
4:29:15actually going to use the actual control
4:29:17of this now we need to get a list of job
4:29:20titles to put in this so I'm going to
4:29:22create a new sheet and call it
4:29:25validation and basically what I going to
4:29:26do with this is create a sheet of all of
4:29:31the different job titles available
4:29:34specifically I'm going to say this is
4:29:35going to be from the column job title
4:29:38short and we're going to be using in
4:29:39order to get the unique values of it
4:29:41well the unique function we need to
4:29:44provide it an array so I'm going come
4:29:46back over here down to column A2 use
4:29:49control shift select all the way down
4:29:52close the parenthesis press enter okay
4:29:55so now we have all of our different
4:29:56values I'm going expand this out I'm
4:29:58also going to zoom in a little bit now
4:30:00whenever I do this drop- down menu I
4:30:03want it in some sort of order
4:30:05specifically I wanted in probably what
4:30:06is the highest count value I wanted it
4:30:09appearing at the top and those that are
4:30:10less likely down at the bottom so what
4:30:12I'm going to do is actually just copy
4:30:13this value right here because this is
4:30:14what we actually want to use what we
4:30:16want to do is a count ifs we want to
4:30:19count based on a condition for the
4:30:21criteria range we're going to be
4:30:23providing that job title short column
4:30:26from our table and then for the criteria
4:30:29we're going to be selecting right next
4:30:30to it A2 B there we'll just autofill it
4:30:33all the way down and then finally we
4:30:36want to now sort it by this so I'll use
4:30:37job title short sorted from there we'll
4:30:41use the sort function to then sort this
4:30:45by the second column position in
4:30:48descending order so bam this is more
4:30:51like I want I want those data analysts
4:30:53that scientist engineers at the top and
4:30:55the senior roles and so on cloud
4:30:56Engineers car Bel so we now have this
4:30:59list available that we want to use for
4:31:01data validation we speak of I'm going to
4:31:03go back to the calculator tab that I
4:31:05made and for this we're going to go to
4:31:07the data tab specifically under data
4:31:10tools they have this this selection
4:31:12available where where data validation
4:31:15actually is and now this is going to
4:31:18allow us to well customize it right now
4:31:21the data validation for this cell is any
4:31:25value I can place any value into it I
4:31:27could limit it to a whole number I could
4:31:30limit it to decimals a list a date a
4:31:32time a bunch of things we're going to
4:31:34limit it to basically a list of values
4:31:38and we need to basically so provide a
4:31:40source for this so for the source we're
4:31:43going to go in and select the validation
4:31:46tab that we just made and I'm going to
4:31:48select all the different jobs right here
4:31:50and then press enter from here I'm going
4:31:52to accept this and press okay now as you
4:31:56can see we have this little drop down
4:31:58right next to it and I have different
4:32:01selections actually available of data
4:32:03engineer if I were to go into here
4:32:05because I have this uh set to data
4:32:07validation if I was going to put in
4:32:09something like data nerd which isn't
4:32:11available and press enter it says this
4:32:13value doesn't match the data validation
4:32:15uh restriction defined for this cell
4:32:17therefore I have to go in and retry so
4:32:18so only values within there are going to
4:32:20be able to work in this so now let's
4:32:22actually get into calculating that
4:32:24median salary and for this we're going
4:32:27to create a new sheet similar to this
4:32:28median salary sheet we're going to call
4:32:30this one salary wrong spot need to
4:32:32actually enter it down here and call
4:32:34this one salary throw this all the way
4:32:36over first I need the names of job title
4:32:40short and all that kind of good stuff so
4:32:42what I'll do is I'll come over to our
4:32:43validation Tab and I've selected equal
4:32:46to already I'm going to select these
4:32:48cells right here press enter so now
4:32:50they're all appearing here now I'm going
4:32:53to calculate the median salary for all
4:32:56these jobs I know our calculator or
4:32:59dashboard has uh only one value that is
4:33:02calculating a time but in our dashboard
4:33:05we're going to build we're actually
4:33:06going to build a graph with all these
4:33:07median salaries so we just need to
4:33:09calculate them now all the median
4:33:11salaries and then basically calculate
4:33:13using data validation and also an X look
4:33:16up what the median salary is going to be
4:33:18here so for this we're going to be using
4:33:21the median function and specifically
4:33:23we're going to be using that if inside
4:33:24of it because median if isn't available
4:33:27we first want to check does the job
4:33:29title here of data analyst meet our
4:33:32condition of the job title short so I'm
4:33:34going to type in the table itself of
4:33:37jobs and then the column of job title
4:33:41short close bracket and set an equal
4:33:44sign equal to A2 then I'm going to close
4:33:48the parentheses on this and actually we
4:33:49need to wrap all this in parentheses
4:33:51because we have to do multiple different
4:33:53conditions we're going to do some array
4:33:55multiplication the other thing we have
4:33:56to check is that the values are not
4:33:59blank or not equal to zero so once again
4:34:02I'll put in jobs again and we're going
4:34:04to be using that salary year average
4:34:07column and we want to make sure that it
4:34:09doesn't equal to zero and so that's the
4:34:12condition we're checking for and so now
4:34:15what do we want to return if true well
4:34:16we want to return the salary so we'll do
4:34:19jobs and then salary year average I'll
4:34:23then close the brackets on that then we
4:34:25need to close one parentheses I can see
4:34:27a red parentheses still and then a final
4:34:29black parentheses NOS I'm good press
4:34:31enter looks like I got it right on the
4:34:33first try let's actually drag this down
4:34:36boom this is pretty nice so now we have
4:34:38all the median salaries for these
4:34:40different job titles I'm also going to
4:34:42take this a step further of actually
4:34:43sorting this by the med CER because I
4:34:44know I'm going to be actually
4:34:45visualizing this in the Project's lesson
4:34:48so we'll go ahead and sort this as well
4:34:51sorting it on the second index in
4:34:53descending order so now we need to
4:34:56provide the value in this case data
4:34:57Engineers there is selected we need to
4:35:00provide based on this value the median
4:35:03salary and I want to just calculate it
4:35:05over here just in case I need to go back
4:35:07to it so for this I want basically
4:35:10125,000 to here right here in G2 so I'm
4:35:14going to provide an X lookup and the
4:35:17first thing is this lookup value right
4:35:19we're going to look up the data engineer
4:35:22in this now I'm not going to use a cell
4:35:25reference of going over here of
4:35:28selecting this cell of data engineer
4:35:30which is calculator C2 I'm actually
4:35:32going to escape out of this we're going
4:35:34to stop this right here I want to go
4:35:36back to this I actually instead because
4:35:38I'm going to be referencing these cells
4:35:40specifically well this what s right here
4:35:43a lot I'm going to just rename this from
4:35:47C2 to title so right now I can see that
4:35:50it is named title so going back over to
4:35:54that salary tab again now we can perform
4:35:57our X lookup and for the lookup value
4:36:00we're trying to look up the title for
4:36:03the lookup array we're looking up
4:36:04through this job titles right here and
4:36:06then for a return array the actual
4:36:08salary values so now we're getting that
4:36:11data engineer value of
4:36:13125,000 similarly I also want to name
4:36:16this cell as well I'm going to name this
4:36:18one median salary pressing enter boom
4:36:22locks it in so now when I come back over
4:36:24to my calculator tab I can just put in
4:36:27here equal to median salary I'm also
4:36:31going to go through and format this to
4:36:33make this look
4:36:37better so just playing around with this
4:36:39I can see that I can put in something
4:36:41like senior data analyst and then a job
4:36:43the associated Med and seller is going
4:36:45to come up with it but let's say now I
4:36:46want to give this to a coworker right
4:36:49how can I prevent them from going in and
4:36:51potentially you know entering in this
4:36:53cell and then breaking it well we can
4:36:56come up here to review and in this case
4:36:59we're going to select this of protect
4:37:01sheet now the first thing you can do you
4:37:03can set a password to unprotect sheet
4:37:06I'm not going to put a password but say
4:37:07you wanted to put one you could and then
4:37:09we have these options for for what you
4:37:12can actually protect whether that's
4:37:13select lock cells or select unlock cells
4:37:16to protect we're just going to leave
4:37:17both of these checked for the time being
4:37:19click okay and now while one we can see
4:37:23that underneath protect here it now says
4:37:25instead of protect sheet it says
4:37:26unprotect sheet whenever I go through
4:37:28this and say I want to change it any
4:37:30value whatsoever I can't change it so
4:37:33it's good because the numbers can't
4:37:35change or the median tile can't change
4:37:37but now I can't change B job title which
4:37:40is a little bit of a pain so
4:37:41unfortunately Excel doesn't necessarily
4:37:44make this the easiest I'm going to start
4:37:46over again and just click unprotect
4:37:48sheet and what we want to do is we're
4:37:51going to select all the cells in here so
4:37:54with all the cells selected I'm going to
4:37:55press control and unselect C2 then right
4:38:00clicking it I'm going to go into format
4:38:03cells now under this protection tab
4:38:07right here we're going to notice we have
4:38:09options for locked and hidden we want to
4:38:11actually be able to lock all the cells
4:38:15except for C2 we don't want to hide any
4:38:17so we're not going to adjust that right
4:38:18now but now we're going to have the
4:38:20ability to adjust whether it's locked or
4:38:22not this doesn't actually change
4:38:24anything right now so if I go into here
4:38:26yes I locked those certain cells but if
4:38:28I were to type into here it's still
4:38:29going to allow it to be changed so now
4:38:33what I can do is go into protect sheet
4:38:35and previously we had both of these
4:38:36selected of Select lock cells and select
4:38:38unlock cells and in this case because we
4:38:42locked all the cells except for C2 we
4:38:45only want to allow people to select the
4:38:47unlocked cell of C2 so I'm going to
4:38:50uncheck this click okay and now I can't
4:38:54click anywhere else except for where
4:38:57I've set up that data validation in this
4:38:59cell and I can still change it and it
4:39:01will manipulate the value now we could
4:39:03also go through and protect the workbook
4:39:05itself I don't necessarily manipulate
4:39:08with this as much instead what would I
4:39:10would want to do in in this case is
4:39:12actually hide all these other sheets
4:39:15with the exception of this calculator
4:39:17and so I can do this by right clicking a
4:39:20tab and selecting hide so I'm going to
4:39:22go through and actually hide all of them
4:39:24so now we have everything as shown by
4:39:25this tab down here of calculator we have
4:39:28every tab hidden except for that and if
4:39:30I wanted it to
4:39:32reappear or get a sheet to reappear I
4:39:34would just right click it click unhide
4:39:36and then it's going to allow me to
4:39:37select which option I can unhide and and
4:39:41if I do want to make it to where a user
4:39:43can't go in and necessarily unhide
4:39:45sheets well I can go in here and select
4:39:48protect workbook once again I can enter
4:39:51a password if I wanted to I'm going to
4:39:53just set this up but now when I come
4:39:55down here to rightclick it there's no
4:39:57option to hide or unhide a sheet so the
4:40:01entire workbook is now protected so I'm
4:40:04not going to lie that was definitely an
4:40:06advanced intro into Data validation and
4:40:08also protecting your workbooks but I
4:40:10promise it's going to just come into
4:40:11great use for whenever we're building
4:40:13this project which will we get to next
4:40:15now we do have some practice problems
4:40:17for you go through and just test out all
4:40:18these different features and with that
4:40:20we'll be jumping in the next lesson and
4:40:23actually building this data science
4:40:25salary dashboard with that I'll see you
4:40:27in that
4:40:31one all right let's now dive in and
4:40:34build our first project with Excel which
4:40:37is this data science salary dashboard
4:40:40this project is going to combine
4:40:42everything that we've used and learned
4:40:44up to this point from formulas and
4:40:46functions to charts and then even to
4:40:48data validation we're going to start
4:40:50first by looking at the dashboard itself
4:40:53you can just go to the project One
4:40:54dashboard folder and Open salary
4:40:57dashboard workbook now in this right now
4:40:59you're only going to see one sheet and
4:41:01as you try to click around you're not
4:41:03going be able to do anything so as a
4:41:04refresher if you want to actually dive
4:41:07in and see what's going on behind the
4:41:09scenes you'll need to First if you want
4:41:11to actually touch any of these points
4:41:13actually go into the review Tab and
4:41:16click unprotect sheet then you'll be
4:41:18able to investigate how I name certain
4:41:20cells and whatnot additionally if you
4:41:23want to investigate any of the workbooks
4:41:25that I worked on you'll need to go into
4:41:27unhide and select the appropriate
4:41:30workbook that you want to well unhide so
4:41:33for this we're going to be building it
4:41:34out section by section specifically
4:41:36we're going to start up at the top
4:41:37building these data validation drop-down
4:41:39menus then from from there we'll go into
4:41:43building the different graphs associated
4:41:45with it and then finally we'll end up
4:41:47with these kpi cards now powering each
4:41:50one of these major topics I've built
4:41:53individual seats so for things like jobs
4:41:56I have all the jobs along with any key
4:41:59information to then build the
4:42:01visualizations in it so here is the
4:42:03basically the table that I made in order
4:42:05to show the graphic right here similarly
4:42:09for Country I have all the different
4:42:10countries and then they're Associated
4:42:12Med and salaries and I use that to not
4:42:14only make the drop down but also make
4:42:16the graph same thing for type and then
4:42:18finally for platform anyway that's just
4:42:21a quick overview to make sure that
4:42:23you're under familiar with how we're
4:42:24going to be working through this but
4:42:25let's actually dive into
4:42:29it for this I recommend picking up where
4:42:32we left off in the last lesson on
4:42:35collaboration did a lot of work for that
4:42:37so we're going to use this workbook
4:42:39first thing I'm going to do once this is
4:42:40open I'm going to go in and actually
4:42:42save it as this final dashboard and I
4:42:44recommend that during this you're saving
4:42:45this pretty frequently so we don't lose
4:42:47progress first thing I'm going to do is
4:42:48start moving this around I basically
4:42:51know where I want to get these different
4:42:52titles of these drop downs and then
4:42:54where I want to put the drop downs we're
4:42:56not going to be using meeting salary for
4:42:57a little bit so I'm just going to take
4:42:59that control xit and place it down at
4:43:01the bottom then take the job title put
4:43:04it in C3 and then move the data
4:43:06validation to right below that we'll fix
4:43:10all the format add in when we get later
4:43:12on it okay so we have the job title now
4:43:14the next thing we need to jump into is
4:43:16country and we'll be putting that right
4:43:18under this portion right here for this
4:43:21I'm going to create a new sheet and call
4:43:23this country with all these sheets I
4:43:25want to have them pretty much similar to
4:43:28what the title is above it so in this
4:43:32case here where we had median salary
4:43:34it's actually the titles um you have
4:43:38named it in the previous one salary so
4:43:40let's go ahead and just name this title
4:43:43anyway going back to that country tab
4:43:45that's where similar to the title tab if
4:43:47you see we first grab the names of the
4:43:50job titles from there and then calculate
4:43:52the median salaries for each we're going
4:43:54to be doing something similar in the
4:43:56country tab with first putting in the
4:43:59country names and then from there
4:44:01putting in that median salary but I want
4:44:04to keep a similar format as in this
4:44:06title case remember we actually pulled
4:44:09this from the data valid ation tab which
4:44:12we're pulling here so I want to keep
4:44:14this consistent anytime we're creating
4:44:17anything for those drop downs we're
4:44:19going to make it here in this data
4:44:20validation tab so I'm going to create a
4:44:22column here called job country and then
4:44:25in this I want to get the unique values
4:44:28from our data set specifically that jobs
4:44:31table it's still named that jobs table
4:44:33and of that column job country go ahead
4:44:38and close the brackets and then close
4:44:40parentheses and now we have all of these
4:44:42different countries not sure why but
4:44:43this is bolded I'm going to go ahead and
4:44:44remove that anyway I want this in a
4:44:46sorted format I'm not going to
4:44:48necessarily sort it like count like we
4:44:50did here with the job tiles I'm just
4:44:52going to sort it in alphabetical order
4:44:54so I'm going to use the sort function
4:44:57and I'm just going to identify that we
4:44:59wanted to use
4:45:00G2 hashtag and Bam now we have all of
4:45:04this also name this appropriately of job
4:45:07country sorted so now we have our list
4:45:11we can go back into here and actually
4:45:14put in the country for the data
4:45:17validation portion we do that by going
4:45:19to the data tab selecting data
4:45:22validation and the values we want to
4:45:25provide a list to this and for the
4:45:27source we go back to that data Val
4:45:29station tab close this out and we
4:45:32basically want to select all these
4:45:33values here so I'll just do control
4:45:35shift down pressing enter we now have
4:45:38everything all the criteria for this I'm
4:45:40going to go and click okay and I get
4:45:42this error message and there's a problem
4:45:43with this formula for some reason I
4:45:45guess when I move back it added this
4:45:47extra sheet in here I'm not too sure
4:45:50this extra data I can't even select in
4:45:52here anyway just make sure it's only one
4:45:54sheet there it's going to work fine
4:45:56country is now in here I can s something
4:45:58like Argentina next value that we're
4:46:00going to be looking at is the job type
4:46:03so part-time full-time whatnot with this
4:46:05although we're not going to use it yet
4:46:06I'm going to create a new sheet and call
4:46:08it type and also move that to the end
4:46:12but now we want to get the unique values
4:46:14of job schedule type so I'm put in the
4:46:16column here of job schedule type and
4:46:20then from there we want to get the once
4:46:22again unique values for this we're using
4:46:24the jobs table specifically that job
4:46:27schedule type column and Bam now you
4:46:30will notice from this one this one it's
4:46:33a little bit this needs some data clean
4:46:34up with it there's a lot of values in
4:46:37here like it sometimes it has combined
4:46:39values like full-time part-time and
4:46:40internship and and whatnot we really I'm
4:46:43actually going to expand this colum out
4:46:44we really just want the single values
4:46:46from this so something like fulltime
4:46:49contractor part-time internship and then
4:46:52also temp work so the first thing I'm
4:46:54noticing about the thing ones we want to
4:46:56remove is that they contain the word and
4:46:59so we'll first identify those that con
4:47:01turn and we do this using the search
4:47:04function which is a text function to
4:47:06find text specifically we're looking for
4:47:08that keyword of and with intext we want
4:47:11to just look through the whole array so
4:47:13we'll put in J2
4:47:15hashtag and I got a little error message
4:47:18I need to make sure I use double quotes
4:47:19for the text itself and running this now
4:47:22I have basically number values for where
4:47:27the and is located at and it looks like
4:47:30yeah it looks like we're good on
4:47:31everything with the exception of the
4:47:33zero which we'll get in a little bit
4:47:34okay so we need to convert this into
4:47:36basically Boolean values because we're
4:47:38going to end end up using this to to
4:47:41pull out that we want using a filter
4:47:43function so we're going to wrap this in
4:47:46the is number and we're going to get
4:47:49false or true and whatnot anyway all
4:47:52right so now we have false or true the
4:47:53last thing we need to do is use well not
4:47:56the last thing second last thing we're
4:47:57going to use the filter function and in
4:48:00this we provided the array so in this
4:48:03case it's going to be J2 hashtag and
4:48:07then for what we want to include is this
4:48:10other array that we just did so I'm
4:48:12going go ahead and close this and see
4:48:14what we get returned back and we're
4:48:16returning now only the values that have
4:48:20and in it we actually wanted to do
4:48:22opposite of that right we want the
4:48:24values that don't have an and so in
4:48:26order to do that we're going to fix this
4:48:27entire statement right here for the
4:48:30include portion we're going to wrap it
4:48:32in a giant knot to turn everything
4:48:35around add an extra parenthesis on the
4:48:37end bam now we have full-time contractor
4:48:40part time we got the zero in there
4:48:42internship and temp work we just need to
4:48:44remove this zero out of it so we just
4:48:47need to modify once again this right
4:48:50here this portion of this include we're
4:48:53going to do some array multiplication
4:48:55basically once again looking through and
4:48:57making sure no values equal to zero so
4:49:00I'm going to do a multiplication do an
4:49:03opening closing parenthesis and
4:49:05basically we're just checking whether J2
4:49:08hashtag is not equ equal to Zer let's go
4:49:12ahead and enter this boom now we have it
4:49:15down to the values that we want for this
4:49:19I'm going to name this appropriately job
4:49:22schedule type sorted also for some
4:49:26reason this is in this column we're
4:49:29going to move it over looks like we're
4:49:30buing one spacing anyway now we need to
4:49:33go back to our basic calculator Tab and
4:49:36we need to enter data validation in this
4:49:39portion to make sure can select the
4:49:41right type so going select data
4:49:43validation once again allow values of
4:49:46list and then for the actual Source
4:49:48itself we'll go to that data validation
4:49:50tab select all these values in here
4:49:53press enter and enter okay so now we
4:49:57have the type in here so all of our data
4:50:00validation portions are now
4:50:05built next thing up is moving into
4:50:08building the three different charts here
4:50:10we're actually going to start with the
4:50:11country chart because it's the easiest
4:50:14and a sneak peek of what data is
4:50:16actually needed for this I can go to the
4:50:18country tab inside my final salary
4:50:20dashboard and all we really need to do
4:50:22is for each country calculate the median
4:50:24salary and then throw it into a map
4:50:26graph so back to our Excel worksheet
4:50:28first thing we need to do is get those
4:50:30list of countries and remember we
4:50:31already have that so I'm put equal sign
4:50:34it's inside of our data validation here
4:50:36with these sorted values I want all
4:50:40these values here here so I'm going to
4:50:40do H2 hashtag press enter we have all
4:50:44them all so let's actually start
4:50:46developing the formula for building this
4:50:49out using only we're just going to
4:50:51calculate first the median salary for
4:50:54that country and then also remember in
4:50:56the past we've have to filter out any
4:50:58values that basically equal zero so for
4:51:00that if condition for The Logical test
4:51:03we're going to do we're going to have to
4:51:04do array multiplication and for our
4:51:07first array we're going to be checking
4:51:08for the job country right so we do that
4:51:11jobs table and specifically that job
4:51:15country column and we want to make sure
4:51:18that it's equal to basically A2 in this
4:51:21case the country right next to it
4:51:23additionally we want to check that
4:51:24there's 9 zero vales and so we're going
4:51:26to be checking the salary year average
4:51:27column and making sure that it's not
4:51:29equal to zero so now moving on to the
4:51:32value if true we basically want to use
4:51:35the salary year average column value
4:51:37false not applicable here go ahead and
4:51:39close this looks like we have a typo it
4:51:42went ahead and added that extra
4:51:44parenthesis and we have a median salary
4:51:46now and go ahead and copy that all the
4:51:47way down now this is great but remember
4:51:51in our if I go here back to to the basic
4:51:54calculator tab we also want to not only
4:51:56filter for a specific country but also
4:52:00we're going to need to filter for a job
4:52:03title and also for a job type so we need
4:52:07to include not necessarily the country
4:52:09because we're doing it for each country
4:52:10but we need to include the job title and
4:52:13the type now in order to add that this
4:52:15formula is going to get a lot longer and
4:52:17it's now getting hard to read so I want
4:52:20to actually I want to one I want to
4:52:21operate in this formula bar if you press
4:52:23control shift U it expands it out and
4:52:26then from there you can actually change
4:52:28it to the desired length that you want
4:52:30so what I'm going to do now is actually
4:52:32break this into new lines I can press on
4:52:35a Mac you're going to press Alt Enter on
4:52:40the Mac I'm pressing option return
4:52:43anyway I've went ahead broken this into
4:52:44different lines I've also inserted some
4:52:47spaces in there to basically put in some
4:52:49indentation so I can read it better
4:52:50don't have to necessarily do that but
4:52:52now I feel like this is much readable
4:52:54for my eyes go ahead and execute this
4:52:57and Bam we have all the results and if I
4:52:59do a drag and drop all the way down all
4:53:02the other ones are updated as well so
4:53:04the first thing we need to add to this
4:53:05is to check for the job title itself so
4:53:09I'm going put a multiplication there go
4:53:12to the next line pressing Alt Enter and
4:53:15for this I want to check jobs
4:53:18specifically I want to check that job
4:53:20title short column and whether it's
4:53:23equal to basically title remember we
4:53:26created title so I'm going go ahead and
4:53:29press enter and it looks like we have a
4:53:32typo because I forgot to insert a
4:53:35parentheses at the end press enter looks
4:53:38like I misspelled the actual table at
4:53:40itself my bad press enter again now I'm
4:53:43getting this name error right here and
4:53:45that's because of this title that we're
4:53:46using if we go back to that basic
4:53:48calculator and select that cell C4 right
4:53:51here it's named titlecore exe and I can
4:53:56inspect the different names assigned to
4:53:59cells by going to formulas Define names
4:54:03and then the name manager now I started
4:54:06directly with this workbook before we
4:54:08actually created all these variables
4:54:10here so what we'll do is this I'm going
4:54:12to go ahead and actually just delete
4:54:15this titlecore ex that was just an
4:54:18example that's why it says ex then from
4:54:21there I'm going to just rename it I'm
4:54:22going to select the cell itself of C4
4:54:25and I'm going to change it back to title
4:54:28okay now it's Title Here back to the
4:54:30country tab uh we have this updated for
4:54:32the title it's actually appearing now no
4:54:34name eror and I'll go ahead and drag it
4:54:36all the way down there's going to be a
4:54:37lot less values for this cuz we're
4:54:39further filtering this so I'm seeing
4:54:41some num erors that's as expected all
4:54:43right the last condition we need to now
4:54:45take into account is this type right
4:54:48here and we haven't named this cell
4:54:50already so I'm selecting K4 and I've
4:54:52come up here and I'm going to select
4:54:54type and now I've rename that as type so
4:54:59we can finish this formula off we wanted
4:55:01to I'm going to do a multiplication sign
4:55:03start a new line by pressing Alt Enter
4:55:07then do open and closeing parenthesis
4:55:09for this we want to check if the job
4:55:11schedule type column is equal to type
4:55:15okay I'm going to go ahead and press
4:55:16enter for this looks we have a value I
4:55:18expect a few more even filtered from
4:55:21here okay not a lot now one note on this
4:55:24this formula is perfectly fine for
4:55:27checking the job schedule tyght I'm
4:55:29going to make it slightly better and
4:55:31actually slightly more correct if I go
4:55:33over to that data validation tab I'm
4:55:36going to press uh control shift U to
4:55:37actually close that formul bar if you
4:55:39remember
4:55:40from our job schedule tites yeah we
4:55:42narrowed it down to this list but
4:55:44actually there were the true list is
4:55:47this so what we actually need to do is
4:55:51check if a value is in here so in our
4:55:56case we want to check whether the type
4:55:57is in here so if we select part-time we
4:56:00will also match on this job type here
4:56:03where it says full-time parttime or this
4:56:05one here where it says full-time
4:56:07part-time temp work and we can do that
4:56:09using the search function so we can find
4:56:14something like part time within text of
4:56:18right here and it's going to give us
4:56:19back a number and then if it's not there
4:56:22if I were to actually drag it down to
4:56:24something like third column it's not
4:56:25there it's going to get a a a value
4:56:27error so I'm going to come back into
4:56:28this and expand out the formula bar and
4:56:32I'm going to change this formula right
4:56:34here to basically get that condiction
4:56:37remember we want to use the search
4:56:38function we want to find the text of the
4:56:42type which is that variable that we have
4:56:44for the job type and we'll be searching
4:56:46the job schedule type column now
4:56:48remember this is going to return back a
4:56:50number of the position if it's there so
4:56:52we're going to need to wrap this all in
4:56:54a is number function and then put
4:56:57closing parentheses so I'm going to
4:57:00autofill this all the way down again and
4:57:03it doesn't look like any values at least
4:57:04in view actually changed underneath this
4:57:07formul bar for right now so I'm going to
4:57:08go ahead and hide it and then for this
4:57:11when we go to plot it we actually need
4:57:12to remove these numb values from here so
4:57:16in order to do this I'm going to I'll
4:57:18create this new one called job country
4:57:21filter and we're going to be using the
4:57:23well filter function and for this we
4:57:26need to include the array so everything
4:57:29from here downwards pressing control
4:57:32shift down to select that and then what
4:57:35do we want to actually include well we
4:57:37want to check to include anything in
4:57:40that b column so is a number we going to
4:57:43check those values are equal to a number
4:57:45so I entered in that b column then as
4:57:48well all right let's go ahead and run
4:57:50this and it looks like it has all of our
4:57:53values I don't like the order I'd rather
4:57:55it sorted this is just me preference I'd
4:57:57rather the numerical values be sorted so
4:58:00I'm going to wrap this all in a sort
4:58:02function and this is the array we're
4:58:04applying to it we want to sort it on the
4:58:08second index and for for this we wanted
4:58:10to put it in we'll say descending order
4:58:14and well Puerto Rico has some of the
4:58:16highest jobs may have to move there and
4:58:18okay we're going to get into applying
4:58:19this now I want to make sure that we
4:58:20have the maximum amount of values
4:58:23present there's a lot of countries
4:58:24missing that I know we available so I'm
4:58:26going to just select the most basic job
4:58:30possible to make sure that we have all
4:58:32the jobs that we can appear so so we'll
4:58:36just select data analyst United States
4:58:38fulltime okay now we can go about
4:58:40selecting column d and e and then
4:58:43inserting in our map now I don't want
4:58:46this here so I'm actually going to grab
4:58:48this map and then come over here and put
4:58:51it in I'm only going to do some minor
4:58:53cleanup right now I'm going to remove
4:58:54the chart title and also leged but we
4:58:58now have this chart map available for
4:59:00countries that shows the median salary
4:59:02one quick note you are going to have
4:59:04this sort of warning right here if I
4:59:06click on it and it says hey we plotted
4:59:0874% of the location from the data with
4:59:11high confidence basically some of the
4:59:13countries in there couldn't align
4:59:15properly in my opinion it picked out a
4:59:18lot of the major countries so I'm really
4:59:21fine with that I'm fine if I didn't
4:59:22identify all of them 74 is good enough
4:59:25back to the final dashboard so we made
4:59:27this country map right here now we need
4:59:28to make these other two one thing to
4:59:30call out with this which I don't think
4:59:31I've called out before if we notice
4:59:34whenever we select a job so in this case
4:59:37I'll select data scientist it makes that
4:59:39barall are a darker color blue the way
4:59:41your eyes go towards it and then you can
4:59:43compare it to the other ones so how did
4:59:46I do this well if I go to my jobs tab my
4:59:49final jobs tab what I'm doing here is I
4:59:52have all the median salaries which we
4:59:54calculated already in ours but I added
4:59:56this over here basically I have one
4:59:59column without we have data scientist
5:00:01selected right now so I have one column
5:00:04without the value appear in and then one
5:00:06value with it appearing in and then what
5:00:09we'll do from there is just some
5:00:11basically manipulation of the graph to
5:00:13make it to where in this case data
5:00:15scientist appears so going back to our
5:00:18worksheet of our fancy Dancy dashboard
5:00:20we have so far going to go to that title
5:00:23sheet remember we already did all this
5:00:26portion of the last section first thing
5:00:28we do is well we need to do some cleanup
5:00:30we need to get rid of this name error
5:00:32also we are going to create those extra
5:00:34columns right here for basically what
5:00:36job title selected but we need need to
5:00:40more importantly if I expand out the
5:00:43formula bar we need to update this
5:00:46median salary similar to what we do with
5:00:48job type to not only take into account
5:00:52the job title but also the country and
5:00:56the job schedule type so I'm all for not
5:00:58repeating our work I'm going to go back
5:01:00over to the country tab select the
5:01:01median salary and I'm going to basically
5:01:04just copy all that portion that's in
5:01:05there anyway I'm going to escape out of
5:01:07that come back into the job job title
5:01:10tab select B2 and I'll go ahead and just
5:01:14press uh Alt Enter insert all that in
5:01:18and then now I just want to clean this
5:01:20up we do want this country which we're
5:01:22going to have to
5:01:23fix but we don't need these middle two
5:01:27right here that we already basically
5:01:29have specifically with the job country
5:01:32though so remember this thing's
5:01:34calculating the median salary based on
5:01:37the job title selected in this col here
5:01:40and column A so this A2 is going to work
5:01:42here previously we were doing the same
5:01:44thing with country we don't need to do
5:01:46country anymore we need to actually put
5:01:48in a variable of country which we
5:01:52haven't created yet so I'm just going to
5:01:53enter country in it's going to give me
5:01:55an error this name error I'm going to
5:01:58come back over to the basic calculator
5:01:59tab select this and then rename G4 to
5:02:04Country press enter come back to the
5:02:07title tab we're no longer getting that
5:02:09name error looks like it's executing
5:02:12just right I'm going to go ahead and
5:02:14drag it all the way down and we do have
5:02:17an error in my formula I have this comma
5:02:20right here this is supposed to actually
5:02:23be an array right this whole thing is
5:02:26supposed to be um an array so now let's
5:02:29try it again press enter okay 990,000
5:02:32for data analyst in the United States I
5:02:34know that's true and now we're filling
5:02:37it in for all the rest okay so we have
5:02:39what we need I'm going close out the
5:02:41formula bar and remember we want to
5:02:44basically in one column if it has the
5:02:47word data analist we want to not include
5:02:48it and then another one we want to only
5:02:50include that one so we're going to use
5:02:52an if for this so if this value which
5:02:57we're going to go ahead and lock the
5:03:00column is not equal to the title then
5:03:05we're going to basically display those
5:03:06results which I'm going to lock the
5:03:08column for this otherwise I just wanted
5:03:11to display an A and not a value Okay g
5:03:14to go ahead and enter this and it is dat
5:03:17analyst so it's not going to appear
5:03:18there but it will appear all the rest of
5:03:19these and so I locked those columns so I
5:03:21can just drag this over and now with
5:03:24this other one I want to do the opposite
5:03:26basically if it's equal to title I want
5:03:28it to appear and then I'll drag and drop
5:03:30it all the way down so these are the
5:03:33values I want to plot so I'm going to
5:03:36select D2 to d11 then holding control
5:03:40also select these values right here go
5:03:43in and insert recommended charts and
5:03:46first one up is actually the one that I
5:03:48want so we'll go ahead and insert that
5:03:50so I'll take this chart and also move
5:03:53that right here into the basic
5:03:56calculator tab with this one once again
5:03:58I don't want a chart title and I don't
5:04:01want a legend the other thing are the
5:04:03values the horizontal values down here
5:04:06I'm going to go ahead and double click
5:04:07on that scroll down here all the way to
5:04:10number and we're going to do that custom
5:04:12formatting that we've done previously if
5:04:14it's not peering uh feel free to type
5:04:17the code in but we're going to use this
5:04:19to basically format it as with the
5:04:21dollar sign in the front and then also
5:04:23the k for the thousands place all right
5:04:26the last thing is you know I don't like
5:04:28to use a lot of different colors in this
5:04:30so making sure the graph is selected go
5:04:32to chart design and then into chart
5:04:34colors right now it's set under colorful
5:04:37which I think is awful default value I'm
5:04:39going to come down here and select not
5:04:41this monochromatic palette 4 five sorry
5:04:44the but the monochromatic palette 12 and
5:04:47that's because now data analyst will be
5:04:50the darkest blue the other ones will be
5:04:52light so that way my eyes go to that one
5:04:53instead so now what we just did with the
5:04:55job title we need to repeat it for job
5:04:58type so a lot of copy and pase in so
5:05:01we're going to move a lot faster with
5:05:02this one because we've done most of this
5:05:04before for this we're going to be
5:05:05entering in the type sheet and I'm going
5:05:07to go ahead and pull all those things in
5:05:09from data validation tab now we need to
5:05:11get the median salaries for that I'm
5:05:13just going to come back over to the
5:05:14title sheet come into here and actually
5:05:16just copy this en typable formula then
5:05:19expanding this out with control shift U
5:05:21pasting this in here now we need to just
5:05:23change this up slightly so for the job
5:05:25title we need to actually use the job
5:05:28title whereas conversely for the job
5:05:31type we no longer want to use type we
5:05:34want to use what's available in A2
5:05:37pressing enter we get our value for
5:05:39full-time 990,000 of data analyst that's
5:05:42correct and then drag it on down I'm
5:05:44going to go ahead and close this for of
5:05:45the bar and for this I'm going to use uh
5:05:47similar to what we did in that Country
5:05:49Sheet in where we not only filter the
5:05:52data to make sure we include is numbers
5:05:54but also we sorted it and that's because
5:05:56sometimes these values sometimes we may
5:05:58not have values and we go back to this
5:06:00type tab sometimes there may not be a
5:06:03certain job schedule type so I'm going
5:06:05to go ahead and paste this in now it is
5:06:08working I know there will always be five
5:06:10values so I'm going to actually change
5:06:12this to B6 here and also B6 here and
5:06:17press enter now I also realized I made a
5:06:20mistake earlier whenever I went to the
5:06:22title sheet this is only doing the sort
5:06:24function and we may have a condition
5:06:26where in certain countries they don't
5:06:28have all these different job titles
5:06:31available so we need to do its similar
5:06:33Hill here as well so I'm going to paste
5:06:35that formula into here and then adjust
5:06:37it because I know there's always 10 job
5:06:40titles so it's going to go down to 11 in
5:06:42this case and 11 here we go ahead and
5:06:47run that there's going to be no change
5:06:50the one issue though is in this case if
5:06:54I go back to that basic calculator it
5:06:56doesn't do it in the order that I want
5:06:59so going back to that title sheet I'm
5:07:00going to change that sorting value from
5:07:02a negative one to a one so that way it
5:07:04goes in basically ascending order and I
5:07:07need to do the same thing here here as
5:07:10well in the type sheet where it's also
5:07:12in ascending order cuz we're going to be
5:07:13making the same graph all right similar
5:07:16to last time I wanted to if the value is
5:07:19selected I want it to be highlighted so
5:07:21we need to make those same columns again
5:07:23so if this is not equal to the type I
5:07:26want the value to appear and it be na
5:07:29because right now fulltime is selected
5:07:31dragging it over and then adjusting it
5:07:33for equal instead and then dragging it
5:07:36down I do want it to appear if it's
5:07:38full-time now I'm going to select
5:07:40D2 D6 and then these values in f and g
5:07:47once again we're going to go to insert
5:07:48recommended charts I don't like these
5:07:50clustered columns I prefer a clustered
5:07:54bar chart so I'm going to take this and
5:07:57then put it in here make similar format
5:08:00and changes as well of removing the
5:08:01title and then also the legend updating
5:08:04the xaxis by going into numbers and
5:08:08changing the format to a custom format
5:08:11to using the K value instead and then
5:08:14finally the actual color Itself by going
5:08:17to that monoch chromatic the color
5:08:20palette 12 so bam now we have a lot of
5:08:24this made so I can go through now and
5:08:26select say data data scientist it will
5:08:29update for selecting data scientist and
5:08:32then you see all these other values
5:08:33update as well I can also select the
5:08:35different type um part-time in this case
5:08:38and then the values still remain the the
5:08:39same it just changes the bar that it's
5:08:41selected
5:08:44to all right the last major thing before
5:08:46we get into formatting we're going to
5:08:47make these three kpi cards one is for
5:08:50the median salary the next is for the
5:08:53top job platform and then finally on the
5:08:56job count itself for how many counts of
5:08:59jobs for all of these now one quick
5:09:01thing Excel doesn't necessarily have kpi
5:09:03cards like if you use something like
5:09:05powerbi or looker they provide cards to
5:09:08this we're going to do some sort of
5:09:10backdoor approach if you will to make
5:09:12this into a kpi card basically I'm going
5:09:13to insert in a text box and we're going
5:09:15to put a cell equal to it you'll see
5:09:18what we're going to do with it but the
5:09:19main point is these values this value
5:09:21itself is not as you can see it's a
5:09:24rectangle it's not in a Cell per se but
5:09:28it is calculated within the workbook
5:09:31anyway what we're going to be doing I
5:09:33don't need this down here this median
5:09:35salary what we did from the last lesson
5:09:38I'm gonna go ahead and delete this but
5:09:40the first we want to calculate is that
5:09:42median salary and we basically have it
5:09:46already and I'm going to calculate it
5:09:49right here in this column of I2 and for
5:09:51this we're just going to use a simple x
5:09:54lookup and the value we want to look up
5:09:57is based on the job title selected so
5:10:01title and the lookup array is this array
5:10:04right here and then the final return
5:10:06array is right next to it there's a
5:10:09missing value right now because Cloud
5:10:10Engineers is not available in the
5:10:12currenc are selected so make sure you're
5:10:14selecting the full values and we going
5:10:16to go ahead and close it but we have now
5:10:19the median salary so I'm going to
5:10:22actually rename this I2 cell to median
5:10:27salary and then going back into our
5:10:29basic calculator tab remember I'm not
5:10:31going to insert it into a sell in here
5:10:33but instead we go into insert and then
5:10:38illustrations and I'm just going to
5:10:40insert a simple old textt box I'll drag
5:10:42it right there now the thing is I don't
5:10:44want to type inside of here what I'm
5:10:46actually do is I'm going to select the
5:10:47Box itself so you no longer have that
5:10:49blinking cursor in there come up into
5:10:51the formula bar up here type in equal to
5:10:55median salary and Bam now if you notice
5:10:59it copied the formatting that we
5:11:01previously have right here as a cluster
5:11:03number looking at right there it copied
5:11:05the same formatting that we're using
5:11:07here in I2 so what I'm going to do is
5:11:10just go in here and change this
5:11:11formatting to a currency with zero
5:11:13decimal places and then once we have
5:11:16this value actually updated go back to
5:11:18basic calculator we can see boom looks a
5:11:20lot nicer we'll adjust the formatting as
5:11:22far as the size and stuff in a little
5:11:24bit after we calculate all the other
5:11:26ones the next one from our final
5:11:28dashboard is the top job platform so
5:11:31we've only calculated things associated
5:11:33with the job title the job country and
5:11:35the job type so we need to make a new
5:11:38sheet and we'll rename it platform and
5:11:42technically the column name is job via
5:11:45and for this we need to get the unique
5:11:48values of the job via column now for
5:11:54this one we're trying to get the top job
5:11:56platform so we're not necessarily doing
5:11:57that based on what is the top median
5:12:01salary on this I just want where are the
5:12:03most jobs actually located so we're
5:12:06going to be doing a count using control
5:12:08shift U to expand the we've been using
5:12:09this median with this if array in it
5:12:12we've already built this out already
5:12:15which this formula does so you could so
5:12:17we're going to use this I'm going to go
5:12:19ahead and copy it by pressing contrl C
5:12:21coming over to platform and then pasting
5:12:23it in with contrl v okay and instead of
5:12:26median we're going to use count and the
5:12:30only other thing we need to update on
5:12:32this is we stole it from the job country
5:12:34page is we need to update the job
5:12:36country to be well country and we need
5:12:40to check one more condition so we need
5:12:42to add to this array I'm going press uh
5:12:44Alt Enter to create a new line and we
5:12:47want to check that job via is equal to
5:12:51in this case A2 and we go ahead and
5:12:54press enter looks like 10 were available
5:12:56for Via script zip recruiter and then it
5:12:59calculates all the way down now remember
5:13:01our data set also has hourly data in
5:13:05there as well so technically if you
5:13:07wanted to which I'm going to I'm going
5:13:08to remove move this condition right here
5:13:10that we're checking that it's not equal
5:13:11to zero basically it's also going to
5:13:13include if there's a job that has an
5:13:15hourly salary included so I'm going to
5:13:18go ahead and backspace out of that press
5:13:20enter and then from there drag and drop
5:13:22it down and I can see we added a few
5:13:24more values because of this I'm close
5:13:27this formula bar control shift you all
5:13:29right so now I need to sort these values
5:13:32basically from high to low selecting all
5:13:35the values using control shift down the
5:13:38sword index we want to use the second
5:13:39index and we want to put this one in
5:13:42descending order cuz we want the highest
5:13:44one up at the top and for this it looks
5:13:46like snag a job is the highest anyway uh
5:13:51this is what we want this first one
5:13:53actually appearing in our kpi card but
5:13:56if you notice all of these have via in
5:13:58front of it so what I'm going to use is
5:14:00a text function of substitute which
5:14:03replaces existing test with a new text
5:14:06and for our text in D2
5:14:09the old text that I want to replace is
5:14:11via with a space and the new text is
5:14:14just a blank value so snag job is now up
5:14:18the top this is what I want to be known
5:14:21as we're going to rename this variable
5:14:23to platform then we do the same thing on
5:14:26our dashboard of inserting a text value
5:14:30and for this I'm going to select it and
5:14:32say that it's equal to platform all
5:14:35right so snag a job and for this one
5:14:38this one is well somewhat simple but in
5:14:41our data validation tab we were in the
5:14:45very beginning in the last lesson we
5:14:47were calculating the count and we were
5:14:50calculating a generic count of all of
5:14:53them so we need to once again modify
5:14:54this because we want the count based on
5:14:57our three conditions here so what I'm
5:15:00going to do is just basically steal it
5:15:01from what we did previously go into that
5:15:03B2 cell in the platform sheet go ahead
5:15:07and copy this all and then then in here
5:15:09I'm going to expand this formula out I'm
5:15:11going to go ahead and replace that in B2
5:15:14with this now a few modifications we can
5:15:16make to this we're no longer checking
5:15:18the job via column we're not trying to
5:15:21check that for the count that was
5:15:22specific to where we stole that from so
5:15:24I'm going to delete that and also this
5:15:25uh multiplication point and then this is
5:15:28checking all of the things selected of
5:15:30country title and type we're wanting to
5:15:32check the count of a certain title so
5:15:36instead of having title we'll put in a
5:15:39A2 pressing enter we have a lower value
5:15:42because we've the current filters are
5:15:44lower and then we'll fill it all the way
5:15:46down closing the formula bar out we now
5:15:49want to get the count for whatever is
5:15:52selected so I'm going to go to an empty
5:15:55column over here right here and we're
5:15:57going to be doing an X lookup again the
5:16:00lookup value is what is the title that
5:16:03we're using the lookup array is we'll
5:16:06use this one right here and then for as
5:16:09far as the return array right next to it
5:16:13pressing enter boom get a value of 537
5:16:17now just to be safe in case there aren't
5:16:20any results like say it was zero or
5:16:22something or not applicable it's going
5:16:23to be basically not applicable I do want
5:16:26to include if not found I'm going to
5:16:28enter in no results and I'm going to do
5:16:31the same thing underneath the title
5:16:33sheet for where we calculated the median
5:16:35salary put for no results
5:16:39so I'm going go ahead we want to get
5:16:41that count in there so we insert that
5:16:43illustration again for us we're going to
5:16:44insert a text box and that textbox is
5:16:47going to be equal to count which I don't
5:16:50think we actually named yet so I
5:16:52actually need to go back to escape out
5:16:55of this go back to the data validation
5:16:58tab rename this count and then from
5:17:02there with the text box selected I'm
5:17:03going put that equal to count now for
5:17:06each one of these text boxes I need to
5:17:08go through and actually
5:17:09as you can see the we have a text box
5:17:11for the value but I actually want to use
5:17:14a shape basically background to tell us
5:17:18what we're actually performing or
5:17:20calculation that this kpi is showing so
5:17:23I'm going come in here and to insert
5:17:25illustrations for shapes we're going to
5:17:27keep it actually we'll say a rectangle
5:17:30this time and then we'll go ahead and
5:17:32draw it now for the shape format itself
5:17:36I'm going to go to this one right here
5:17:38basically a blue around with white on
5:17:40the front and with these shapes you can
5:17:44still put in text in here so I can put
5:17:46in something like median salary and I
5:17:49can open up the Home tab and I can
5:17:52actually customize this further so I can
5:17:54make this bold I can put in the center I
5:17:57actually want Center top and I'm going
5:17:59to make this slightly bigger by 20 point
5:18:02also I'm noticing this box is a green
5:18:04outline I don't really like that I'd
5:18:07rather a blue outline so we have that
5:18:09now okay so how do we get that number if
5:18:11you notice the number is no long it's
5:18:13hidden behind here we can do a couple
5:18:15different ways but I'm just going to
5:18:16rightclick this object and then under
5:18:19shape format you can go to send
5:18:22backwards specifically I want to send
5:18:23all the way to the back now getting into
5:18:26the actual text box itself if you notice
5:18:29there's a little bit of a a box around
5:18:31it I don't really like that I'm also
5:18:33going to exp expand it all the way to
5:18:35the edges I'm going to format this one
5:18:37as well to be centered bold and then
5:18:41we're going to make the font much bigger
5:18:42on this and I'm going to once I like I
5:18:45talked about remove that shape outline
5:18:47right now it has a a light one I'm going
5:18:49to say no outline okay so now it looks
5:18:52like a kpi card copying this I'm going
5:18:55to then make two more and for each of
5:18:58these I'm going to send them back to the
5:18:59back name appropriately to top job
5:19:02platform and job count for this I'm
5:19:04going to just copy this text box here
5:19:07that has the median salary in it and I
5:19:10just want to copy the formatting to the
5:19:11other ones as well so we can
5:19:12conveniently use this paintbrush this
5:19:14format prer and I'll select this one it
5:19:17disappeared I have to reselect it and
5:19:20I'll also select this one if you notice
5:19:23the names are cutting off so it's really
5:19:24important that you extend it all the way
5:19:27over same thing with the job count as
5:19:33well now we're getting into the format
5:19:37portion of actually just doing some
5:19:39final touches on here I don't like grid
5:19:41lines so under view tab I'm going to
5:19:43select remove grid lines for each of
5:19:45these charts I don't really like those
5:19:47outlines I want it just to sort of blend
5:19:49in to make it look like it's there so
5:19:51for the shape outline I'm going to
5:19:52change each of them to no outline up in
5:19:54our data validation point I want to make
5:19:56the spacing right I'm also going to make
5:19:58these titles slightly bigger for the
5:20:01dropdowns themselves I want them to
5:20:03basically pop out so I'm going to change
5:20:06this formatting I'm going to go to the
5:20:07cell Styles and I really like this one
5:20:09of input because it sort of calls your
5:20:10eyes to what you need to go to I'm going
5:20:12to make this G column slightly bigger
5:20:15and then shift the type over some the
5:20:18other thing I want to do is add a title
5:20:20up here at the top for what this
5:20:21dashboard actually does so I'm going to
5:20:23select cells B1 through L1 I'm going to
5:20:26do merge and center and I'm going to
5:20:28change this to data science salary
5:20:30calculator along with going to the cell
5:20:32style we'll do heading one for right now
5:20:34I want that to still be slightly bigger
5:20:37okay now we're going to to start moving
5:20:39stuff around but I want to get in it's
5:20:41like its final form that I'm going to
5:20:43give to colleagues and co-workers and
5:20:45I'm going to give it with the Home tab
5:20:48closed and also with if I view this can
5:20:52remove headings so it moved the column
5:20:55headers the A and the B and then the row
5:20:57numbers as well so it looks like
5:20:59everything's upda correctly one minor
5:21:01thing this job count I want to make sure
5:21:03after I select it fulltime I saw that
5:21:05the formatting of the thousands with the
5:21:07Comm is not there so going back into
5:21:09that data validation tab I'm going to
5:21:11select this go to home make it a comma
5:21:14and remove all the decimal places okay
5:21:17looking good all right now we need to
5:21:18get this set up to give to colleagues I
5:21:21don't want them to have all these other
5:21:23tabs or all these other sheets so I'm
5:21:25going to go through and actually just
5:21:26hide the ones that aren't applicable for
5:21:28them Additionally the sheet of basic
5:21:30calculator doesn't really make sense
5:21:32anymore cuz that was for that first
5:21:34lesson I'm going to actually name this
5:21:35to salary calculator now call could
5:21:39still potentially go in and they could
5:21:41mess up these formulas and so we need to
5:21:44now protect our worksheet and we only
5:21:47want them to be able to manipulate these
5:21:49three cells so we're going to be going
5:21:52through protecting the sheet but we need
5:21:54to actually recall that we have to pick
5:21:56what cells that we want to lock right we
5:21:59need to select all the cells and I
5:22:01preemptively told you to hide the
5:22:03headings you need to go back into view
5:22:04and show the headings again cuz we need
5:22:05to be able to select this triangle in
5:22:07the upper left hand order to select all
5:22:10the different cells and then from there
5:22:12holding control unselect these three
5:22:15cells and then from there we're going to
5:22:17right click in there go to format cells
5:22:20under protection and we want to make in
5:22:22that case that they are locked or
5:22:24basically we are going to be able to
5:22:25lock them conversely we need to escape
5:22:28out of this and now select the three
5:22:31cells that we want to unlock right click
5:22:34go to format cells and for these we want
5:22:36to make sure that they are not checked
5:22:38for this so basically unlocked whenever
5:22:40we go ahead and protect the sheet so now
5:22:43whenever I go into review go to protect
5:22:46sheet I want to be able to select unlock
5:22:49cells once again if you want to enter a
5:22:51password you can I'm going to click okay
5:22:53so now I can't click anywhere else
5:22:57except for where we have our data
5:23:00validation so I can go through and
5:23:01select things like data scientist and
5:23:04turkey now I'm just going to add that
5:23:05last final touch of removing the
5:23:08headings
5:23:09bam we have our dashboard now I promise
5:23:12last last thing before we go I'm
5:23:15noticing and you're probably noticing as
5:23:17well if you're going through and
5:23:18manipulating these values in this case
5:23:20let's go from data analyst from previous
5:23:22selected data scientists this me talking
5:23:24in real time I want to show it takes how
5:23:26long it takes to load and it takes
5:23:28forever to load why is it doing this
5:23:31this is not good for stakeholders
5:23:33they're going to get annoyed if it takes
5:23:35this long I'm going go ahead and unhide
5:23:37some of our sheet repats specifically
5:23:40that platform one now these formulas
5:23:43that we're using um the array formulas
5:23:47to calculate these values it's F so in
5:23:51this platforms one we have like oh my
5:23:53gosh in this case we have close to 200
5:23:56oh no it's like slowing down even going
5:23:58through this we're executing this
5:24:01hundreds of times in here whereas if I
5:24:03compare it to something like the title
5:24:06sheet we're only running this you know n
5:24:1010 times which I feel isn't that big but
5:24:13if we're running this formula hundreds
5:24:15of times it's going to slow down this
5:24:17sheet so I have a quick fix for this and
5:24:21it involves we're not going to
5:24:23especially for this sheet here platform
5:24:24sheets we're not going to use this um
5:24:27array multiplication order to calculate
5:24:29this instead we're going to use a count
5:24:32ifs the first thing we're going to do is
5:24:35check that the Java is equal to the
5:24:39criteria one of A2 so basically job
5:24:42platform is what it is says it is from
5:24:44there we'll check the job title short
5:24:46column to make sure it makes up with
5:24:47title we'll check the job country is
5:24:50equal to Country and then finally we're
5:24:52going to check that the job schedule
5:24:54type is equal to type and then we're
5:24:56going to go ahead and execute this and
5:24:58then we're going to autofill it all the
5:24:59way down notice that 1490 it's actually
5:25:01going to go down slightly to
5:25:041426 and that's because we've now
5:25:07changed this condition inside of this
5:25:09count ifs specifically if I go back to
5:25:11that title sheet you remember whenever
5:25:13we match for this we did a really
5:25:16indepth search so if any job schedule
5:25:19type contain those keywords we match to
5:25:21it now we're only matching it if it
5:25:23exactly matches but since this job
5:25:26platform is just providing it's not
5:25:28providing a numerical value it's
5:25:30providing what is the Top Value I don't
5:25:31think the Top Value is going to change
5:25:34that much so I don't think we're being
5:25:36inaccurate about this if we change this
5:25:39formula anyway going back to the actual
5:25:42dashboard itself now whenever I change
5:25:43this from data analyst to data scientist
5:25:46it is much faster so now I'm go ahead
5:25:49and hide those sheets and we are done so
5:25:53that was a heck of a lot of work so in
5:25:56the next lesson we're going to be
5:25:57getting into how you can actually go
5:25:58through and share this dashboard
5:26:01specifically for those that have a
5:26:02Microsoft description you can use
5:26:03something like Microsoft online because
5:26:06it has all these features that we have
5:26:07within here and host it there for others
5:26:10to use additionally we're going to get
5:26:12into my recommended method of sharing
5:26:14any your projects and that's via linked
5:26:16in now just a heads up we will be
5:26:19getting into git and GitHub after
5:26:22project 2 at the very end of this course
5:26:26and during that portion we'll talk about
5:26:28how to share not only project 2 but also
5:26:30this project here but that's more
5:26:32complicated and I really want to focus
5:26:34on Excel so with that we're going to be
5:26:36shifting in the next lesson to quickly
5:26:38share it and then moving into the
5:26:39advanced chapter all right with that
5:26:41I'll see you in the next
5:26:42[Music]
5:26:46one first up congratulations on
5:26:49completing your first project in Excel
5:26:52and building this salary dashboard been
5:26:55nothing short of your hard work and you
5:26:58shouldn't let that hard work go
5:26:59unnoticed so in this lesson we're going
5:27:01to be going over different methods you
5:27:03could go about actually sharing this
5:27:05project to your social network and to
5:27:07others to help out in the job search or
5:27:10future employment now if you were just
5:27:12learning these skills for fun you had no
5:27:14intent getting a new job or increasing
5:27:17your pay in your current job then you
5:27:19can feel free to skip this and go to the
5:27:21next chapter on pivot
5:27:25tables so there's a few different ways
5:27:27you can go about sharing your work that
5:27:29you did we're not going to go dive into
5:27:31deep any of these we're going to look at
5:27:32these more at a high level before
5:27:34jumping into one of the options first up
5:27:36is a portfolio website here I have luk
5:27:38bru.com and if I wanted to I could come
5:27:41inside of here and edit it and include
5:27:43my project here along with what I did
5:27:46for others to see another option even if
5:27:48you don't have a big following on
5:27:50YouTube is you could actually go in and
5:27:52record and describe what you did within
5:27:55your dashboard and host it somewhere
5:27:57like YouTube now for both those options
5:27:59you may be like Luke how do I actually
5:28:01actually share my Excel file that
5:28:03actually went through well that's where
5:28:05we run into a little bit of issues as as
5:28:08yes we created this Excel file right
5:28:10here but how do you actually go about
5:28:13sharing it with others to see your work
5:28:16well one option for this is actually
5:28:18hosting your file online via something
5:28:21like one drive which if you're paying
5:28:23for a subscription of Microsoft service
5:28:27you have access to one drive and you can
5:28:29host your dashboard online all I need to
5:28:31do is navigate to One drive. live.com go
5:28:35to this add new and files upload from
5:28:37there select my file that I actually
5:28:39want to upload online and then we can go
5:28:42to it and our file is actually uploaded
5:28:45here which we can actually go through
5:28:48and select something like data
5:28:49scientists and it will actually
5:28:51calculate based on the changes we make
5:28:53to it now one note the country chart
5:28:56inside of excel online doesn't work but
5:28:59I have a fix for it and mainly it's to
5:29:01just remove it you go into the review
5:29:03tab under protection and go to manage
5:29:06protection and then you turn off sheet
5:29:08protection then from there you can
5:29:10delete it next all you need to do is
5:29:13just take those charts and actually
5:29:15extend them over so way they take up
5:29:17that extra space and then once you're
5:29:20complete with that turn back on the
5:29:21sheet protection and now you can go
5:29:24about actually sharing this so here I'm
5:29:26coming into share and you can add an
5:29:29email if you want or if you just want to
5:29:31share it in general with a link you can
5:29:33come down here and fine-tune the control
5:29:36of a link to provide in this case I'm
5:29:38selecting that I'm going to share with
5:29:40anyone they can edit it you could make
5:29:43it view but then they can't change the
5:29:44dropdowns so I recommend that you still
5:29:46leave it on edit you could set an
5:29:48expiration and even password and then
5:29:50from there click apply and now you have
5:29:53a link to your dashboard that works even
5:29:57if you don't have a Microsoft account so
5:29:59here I am in incognito mode within my
5:30:01browser so I'm not signed in at all and
5:30:03I can actually go in and access this
5:30:06dashboard and go through and select
5:30:09something and it updates in real time
5:30:11and because I got that sheet protection
5:30:13on they can't go through and change
5:30:14anything except for these dropdowns
5:30:16don't believe me you can check out my
5:30:18project via the link below but what
5:30:20happens if we want to not only maybe
5:30:22share our file but also write up what we
5:30:26did the work we did with this and all
5:30:28the different skills that we used well
5:30:31that's the case of using something like
5:30:34GitHub GitHub provides a location to
5:30:37store Excel files like shown here along
5:30:40with giving you the ability to go
5:30:41through and perform a write up detailing
5:30:43all the different work that you did now
5:30:45if you wanted to see this you could just
5:30:47navigate over to my project where you
5:30:49download all these files from on GitHub
5:30:52navigate into that project
5:30:53one-board and in here has our Excel file
5:30:56and also this read me which then appears
5:30:59actually underneath here and details all
5:31:01the different work that we did for this
5:31:03now getting this project onto GitHub if
5:31:06you're not familiar with GitHub up is
5:31:09fairly complex we're actually going to
5:31:11be saving this for after project 2 and
5:31:14in that case navigating back to the
5:31:16project itself we'll not only be
5:31:18uploading project one we'll also be
5:31:20uploading project two as well so after
5:31:23we finish the last chapter chapter 8 on
5:31:24power pivot we'll be getting into all of
5:31:26this and you'll be learning more about
5:31:28git GitHub and how to manage a
5:31:33projects now from what I found working
5:31:35in data science it's that the best way
5:31:38to share your work and your project and
5:31:40potentially collaborate with others is
5:31:42use something like LinkedIn a social
5:31:44media platform for networking in order
5:31:46to share your project specifically here
5:31:48I am on my profile right here and if we
5:31:51scroll on down they have a section in
5:31:53your profile to basically show all your
5:31:56different projects that you've worked on
5:31:58and contributed to and adding a project
5:32:00is super simple I got to do is click
5:32:02this plus icon include a description in
5:32:05my case I was trying to help out job
5:32:07Seekers inves salaries for their desired
5:32:09jobs put in a few skills up to five of
5:32:12Microsoft Excel data analysis or Excel
5:32:14dashboards now for media they do have
5:32:16options to add a link or media in the
5:32:19case of the media it doesn't support
5:32:21Excel files and then if you try to
5:32:23insert your one Drive Link I ran into
5:32:26errors so I find the best way to
5:32:27actually just share the link is to post
5:32:29it inside of the description from there
5:32:32specify when you start and stopped on
5:32:34this project anybody that contributed to
5:32:37it this or anything that is associated
5:32:39with and then from there click save the
5:32:42other option that I recommend is
5:32:44actually just going in and making a post
5:32:47here I just write up a short little
5:32:49description of what you did with your
5:32:50project and then if you want include
5:32:53something like an image or even
5:32:55something like a gif which shows an
5:32:56overview of the project and then
5:32:58probably the most important thing is
5:33:00actually sharing that link to your one
5:33:02drive online you can also Post in the
5:33:03comments and not include in the
5:33:04description it's really up to you anyway
5:33:06go through there and then post
5:33:08so bam that's how you share your project
5:33:11as a reminder we will be going into
5:33:14greater detail into how to share both
5:33:16this project and also the second project
5:33:19on GitHub using git and also use things
5:33:21like markdown in order to write about
5:33:24your project but that'll be included
5:33:26after we go through all of the different
5:33:27Excel content just wanted to have a
5:33:29quick way of you going through and
5:33:31actually sharing what you've done so far
5:33:33cuz I know you're probably excited and
5:33:34proud of it all right in the next videos
5:33:36we're going to be shifting gear into the
5:33:38advanced chapters getting starting off
5:33:41first with pivot tables with that I'll
5:33:44see you in
5:33:48there all right welcome to the advanced
5:33:52chapter and because we're get into the
5:33:54advanced section you know it's time for
5:33:56a new
5:33:57flannel and with this Advanced chapter
5:34:00we're going to be focusing on a few core
5:34:03topics that I think is going to make
5:34:05your life a lot easier specifically
5:34:07we're f focus on things like pivot
5:34:08tables power query and also power pivot
5:34:12all of these are great at automating my
5:34:15Excel workflows to make it a lot easier
5:34:18to do repetitive analytics that my boss
5:34:21may come to me back and back again for
5:34:23instead of with something like a formula
5:34:25where I have to go through and make and
5:34:27copy and paste that formula all over
5:34:29again and rerun that whole analysis
5:34:32these Advanced chapters are going to
5:34:33make your life a lot easier anyway in
5:34:35this chapter we're going to be focused
5:34:36on pivot tables this lesson specifically
5:34:39will be getting an intro into pivot
5:34:41tables how to make them how to
5:34:43manipulate them how to even read them in
5:34:45the next lesson we'll be going into
5:34:47advanced pivot tables looking at things
5:34:50like grouping and even aggregating such
5:34:52as getting percentages of grand totals
5:34:55and whatnot and then the final lesson in
5:34:57this chapter is on pivot charts which
5:34:59allows us to basically take what we have
5:35:01in our pivot tables and convert it into
5:35:04a usable chart hence the name pivot
5:35:07chart all right so let's actually get
5:35:09into it and understanding why these
5:35:12pivot tables are so
5:35:16important so in the basics chapter we
5:35:19made this table right here which uses
5:35:23hardcoded values for the different job
5:35:25titles along with the different months
5:35:28and then from there uses formulas
5:35:30specifically some product along with
5:35:32some array calculations in order to
5:35:35calculate how many job counts per month
5:35:38this is cool and all but what happens if
5:35:41we wanted to add another job title so
5:35:44say we have like some like business
5:35:45analyst or we have software developer
5:35:48we'd have to actually manipulate and
5:35:49upgrade all these different formulas
5:35:51that we have here well here's that same
5:35:53table but in a pivot table and by its
5:35:58name that's what they're great at
5:35:59they're great at pivoting and thus
5:36:01aggregating data based on certain values
5:36:04and whatnot so what is if we want to add
5:36:07more job title this well I can just come
5:36:09in here similar to how we manipulate a
5:36:11table select this filter dropdown and
5:36:13then go from there and select things
5:36:15like oh I want to include something like
5:36:16a business analyst and then the data
5:36:19automatically updates for this no
5:36:21readjusting formulas makes it super
5:36:23simple I can even take this table a step
5:36:25further and if I wanted to I can
5:36:28actually filter by the job country in
5:36:30this case I'm filtering by the United
5:36:32States and we now have these values
5:36:35makes it super simple anyway we're
5:36:36getting ahead of ourselves we actually
5:36:37need to get into creating our first
5:36:39pivot
5:36:42table all right so for the advanced
5:36:44chapters it's going to be a little bit
5:36:46different for what files you're going to
5:36:47use for this the final results of this
5:36:50lesson will be in the lesson title of
5:36:53pivot table intro but what I want you to
5:36:55do whenever you're going through or
5:36:57following me along in this lesson is
5:36:59actually revert back to the previous
5:37:01file of the last lesson in this case or
5:37:04the first lesson so we don't have one so
5:37:06I have this one called zero of just
5:37:07pivot tables that's the one you want to
5:37:09start with so in this case pivot tables
5:37:12itself just has the data tab of the data
5:37:14we want to work with and this sheet of
5:37:16the table that we've been familiar with
5:37:17in Basics chapter which by the end of
5:37:19this we're going to make a pivot table
5:37:21out of and when out of I mean actually
5:37:23of the core data itself anyway for the
5:37:27actual pivot table intro this will have
5:37:29also those similar tabs but then also
5:37:32the lesson itself will have all the
5:37:34different work that we've actually done
5:37:36to complete what we need to do so feel
5:37:38free to just have both of these up
5:37:41during a lesson so that way you can
5:37:42consult back and forth in case you get
5:37:44lost all right so let's get into our
5:37:46first pivot table we're going to be
5:37:47using the data that we previous been
5:37:48using of all the salary data for those
5:37:51job titles anyway if I go into the
5:37:53insert tab up here in the top left hand
5:37:56corner I have pivot tables but I also
5:37:57have recommended pivot tables if I don't
5:38:00have an analysis in mind I could come
5:38:02into recommended pivot tables a Pan's
5:38:04going to appear on the right hand side
5:38:06and notice here that it actually
5:38:08selected the data range I know that's
5:38:11the data range and it goes through and
5:38:13provides some recommended different
5:38:15pivot tables that you could put into
5:38:18here whether you put it into a new sheet
5:38:20or an existing sheet but I know what
5:38:23analysis I want to do specifically I
5:38:25want to do a count of the different job
5:38:29titles so data engineer I want to find
5:38:31the accounts of this senior data analyst
5:38:33and so on right now it's not providing
5:38:35any of that I don't typically find that
5:38:36any time with recommended pivot tables
5:38:38that it provides me what I want so I
5:38:39don't find myself using that often
5:38:41instead I go directly into pivot tables
5:38:44right here and then we have three
5:38:47options but we're really going to focus
5:38:48for this lesson and this chapter is from
5:38:51table or range I'm selected inside of A4
5:38:55right now but it automatically knows
5:38:58that this is the data range all the way
5:39:00down to the bottom the other thing it
5:39:01says is choose where you want the pivot
5:39:03tail to place you can either do a new
5:39:04worksheet or you can do inside the
5:39:07existing worksheet but you have to
5:39:08specify a location we don't want that I
5:39:11typically like it in a new worksheet to
5:39:13keep my analysis in one standard
5:39:15location the last thing it asked is
5:39:17whether you want to analyze multiple
5:39:19tables specifically add this to the data
5:39:22model we're going to be going into Data
5:39:24models very heavily in the power pivot
5:39:28chapter or chapter eight or last chapter
5:39:30this is a super powerful feature when
5:39:32you have multiple tables you need to
5:39:33combine it we're not doing it in this
5:39:35lesson or in this chapter so we're going
5:39:37to leave it unchecked so now I'm in this
5:39:39new sheet that I'm going to rename to
5:39:42job count and I'm also going to move it
5:39:45over here to the end anyway this pivot
5:39:48table this pivot table 2 that is calling
5:39:50it is there's nothing in it right now
5:39:52and you notice there's a few things that
5:39:54popped up first is the pivot table
5:39:55analyze tab which is available with this
5:39:58and also the design tab we'll be going
5:40:01into these in some upcoming examples
5:40:03that we're going to get into we're
5:40:05however going to be focusing on for this
5:40:07example example on the job count I'm
5:40:08going to close this out on this pivot
5:40:10tabl Fields pane right here now the
5:40:14layout of this you may see it's somewhat
5:40:16different is we have the columns over
5:40:19here on the left so if you remember the
5:40:21job tile short column job tile column
5:40:22job location and then these fields on
5:40:26the right hand side are things for like
5:40:29filters row columns or values so I can
5:40:31take the job title short column put into
5:40:33something like the rows and get
5:40:35basically all the values in the rows now
5:40:37your layout may be a little bit
5:40:38different if you come up and select the
5:40:40tools icon right here you may be under
5:40:43this Field section and area section
5:40:46stacked which has the feels down here on
5:40:48the bottom I personally don't really
5:40:51like this because look how short my
5:40:53column titles are so I like having them
5:40:57like this instead anyway I think we
5:40:59understand this columns area right here
5:41:01but I don't think we understand these
5:41:02filters rows columns and values so let's
5:41:05explore this by calculating the counts
5:41:08of these different job titles now
5:41:10anytime I add something to the rows or
5:41:12any of these columns I can either remove
5:41:14it by grabbing it and pulling it off
5:41:16notice they have the x mark on it or
5:41:18similarly I can also just come in here
5:41:21and click the uncheck Mark box that's
5:41:23more applicable if especially for having
5:41:26it in multiple different panes and want
5:41:28to move it completely makes it simple
5:41:30besides rows we also have columns and so
5:41:32instead of the job titles being in rows
5:41:35they're in the different columns I don't
5:41:37really like this too much I typically
5:41:38find myself using rows so we're trying
5:41:40to calculate what is the count of these
5:41:43job title shorts so I'm just going to
5:41:44take that job title short again and put
5:41:46it into the values and it automatically
5:41:50Aggregates this by counts of that but
5:41:54what happens if I don't want to do that
5:41:55count aggregation well one way is to
5:41:58come back into that values right here
5:42:00and I'm going to just click it not right
5:42:02click it just normal click it and then
5:42:04go into value field settings and this
5:42:07pop-up is going to come up first up is
5:42:09the name of the column itself I actually
5:42:11don't like this for of a name I'm just
5:42:12going to rename this to job count under
5:42:15here under the summarized values by tab
5:42:17you can select a lot of different
5:42:20aggregation methods we're going to stay
5:42:22with count you can also change how you
5:42:25show value as basically if we wanted to
5:42:27do a percentage of some total or not
5:42:29we're going to be jumping that in the
5:42:30advaned lesson so stand by for that the
5:42:32last thing to note with this is the
5:42:34number format so I can come in here and
5:42:36actually select in our case we have
5:42:39thousand values so I like to use a th
5:42:41separator along with zero decimal places
5:42:44and then clicking okay to apply this all
5:42:47it updates the formatting and the name
5:42:49so we've going over rows columns and
5:42:51values what happens if we want to then
5:42:53filter let's say for only United States
5:42:56jobs well I could drag something like
5:42:59the job country column into filters and
5:43:02right now it's selecting all you have
5:43:04you see this pan come up right here and
5:43:06from there here I can actually go
5:43:08through and select something like the
5:43:10United States click okay and now the
5:43:13values as you can see they reduced and
5:43:15are only United States value other type
5:43:17of filterings I can do I can filter the
5:43:19row itself so if I wanted to I could
5:43:22select the different job titles that I
5:43:24want to appear in this and click apply I
5:43:27could also do something where let's say
5:43:29I wanted only job title so we're going
5:43:30to do a label filter and jobs that
5:43:33contain the word data so I could just
5:43:36type in here
5:43:37data and whenever I filter it I get all
5:43:40the different jobs that contain data
5:43:41similarly I could also filter by this
5:43:44job count here and that's by the values
5:43:47filter so I'm going to remove this label
5:43:49filters to start with and we can go back
5:43:52in here in the values filter and we
5:43:54could do something like hey we want to
5:43:55get jobs that are only greater than
5:43:59let's see here Cloud Engineers 33 I
5:44:01don't want to see that anymore I get to
5:44:03greater than 100 and it filters down but
5:44:06we're not going to use any filters right
5:44:07now so I'm going to one clear this
5:44:10filter for the table and then also
5:44:13remove this filter from filtering for
5:44:16the United
5:44:19States so let's get into taking this
5:44:22analysis of step further and we're going
5:44:23to want to now analyze the average
5:44:27salary of these different job titles
5:44:29while we're going through this we're
5:44:31also going to be exploring the pivot
5:44:32table analyze tab so a quick tour of
5:44:35this tab first up over here on the left
5:44:37is Pivot tables if I wanted to I could
5:44:40go through and rename this i' probably
5:44:42name this typically something similar to
5:44:44what is my sheet name itself this case I
5:44:47named it job count additionally inside
5:44:49of here we have options which allows us
5:44:52to do a lot of detailed control of how
5:44:55we're building our pivot tables it's a
5:44:56very Advanced feature I don't find
5:44:58myself going into it quite often unless
5:45:00I need to fine-tune the functionality of
5:45:02it active field so that tells us
5:45:04basically what's the active field
5:45:06grouping is something we're going to go
5:45:07into in the next lesson we actually go
5:45:09and Performing groups of different job
5:45:11titles slicers and timelines we're going
5:45:13to be going into the last lesson on
5:45:15pivot charts in order to basically use
5:45:17these slicers and timelines to filter
5:45:19data section is used to control our data
5:45:21so I can click something like refresh or
5:45:24refresh all it's going to refresh the
5:45:26data that we have so in this case
5:45:28remember business analyst is around
5:45:301,1 so if I go back to our data itself
5:45:34and I find this entry on business
5:45:36analyst and then and let's say that
5:45:37that's not correct and I delete that out
5:45:39of there whenever I come back to this
5:45:42table itself it still says
5:45:461,1 what I have to do is well we've
5:45:49updated the data so I have to well
5:45:51refresh it now that I refreshed it it's
5:45:54down to 1,000 I actually don't want to
5:45:56remove that entry so I'm going to just
5:45:58press contrl Z and bring that right back
5:46:02and then also click refresh to make sure
5:46:03it's up to date if I want to change the
5:46:05data source or maybe the range I could
5:46:07go into something like this of change
5:46:08data source actions allow us to clear
5:46:12select and even move a pivot table for
5:46:14calculations they have things like
5:46:16calculated fields and items but we're
5:46:18going to get into measures and I feel
5:46:20they're way more powerful so we're not
5:46:21going to cover this much the last thing
5:46:23to cover with this is over here on the
5:46:25right hand side is the show sometimes
5:46:28whenever you're navigating you'll click
5:46:30into your pivot table and that pivot
5:46:31table Fields pane won't pop up you can
5:46:33also pan it on and off by clicking this
5:46:36field list and if you didn't want
5:46:38something like row labels at the top you
5:46:40could just remove the field headers as
5:46:42well so getting into that actual
5:46:43analysis we want to analyze the salary
5:46:46year average what is the average value
5:46:50now I can't see all the different values
5:46:51selected in here so I'm going to
5:46:52actually going to go ahead and close
5:46:54this paint up here to have a bigger view
5:46:55anyway what it did was it did a sum of
5:46:58the salary year average we don't really
5:47:02want that we want to go to average and
5:47:05I'll change this column name to to
5:47:07average yearly salary now if you've been
5:47:10following along since the basic chapter
5:47:12you probably know that I prefer me
5:47:14performing a median for this salary data
5:47:17over an average but if you actually go
5:47:20through this there's no median value for
5:47:23this that doesn't mean you can't do
5:47:25median in pivot tables you actually can
5:47:27you can actually do even more advanced
5:47:28stuff which we're going to get to in
5:47:31chapter 8 and power pivot but for now
5:47:33we're just going to stick to only
5:47:35performing average for this I'm going to
5:47:37click okay so the formatting on this is
5:47:39all jacked up and we could go into that
5:47:42field settings and adjust that or I can
5:47:44actually go in as long as I have all the
5:47:46values selected here I can select hey I
5:47:49want to convert this to a currency and
5:47:52that I don't want any decimal places and
5:47:54it's going to format all the values and
5:47:57I feel this is a little bit easier
5:47:58because now actually if you go back and
5:48:01in exploring the value field settings
5:48:03inside of number format it actually
5:48:05applied this custom formatting for me so
5:48:08it knows to apply that since I applied
5:48:10it to all the values that were visible
5:48:12now since this is so easy I could also
5:48:14do something like get the average of the
5:48:16hourly salary once again it's doing the
5:48:19sum of that and I don't want that I want
5:48:22the average itself and I can change that
5:48:25column Name by just going in here and
5:48:27typing in average hourly salary
5:48:31inspecting the value field setting it
5:48:33also updates inside of here and I'm
5:48:35going to go ahead and adjust the
5:48:36formatting as well changes to a currency
5:48:38with two decimal
5:48:42places so let's get into actually
5:48:44cleaning how this table looks up and we
5:48:46can go and do this by going into the
5:48:49design tab now I'm going to start over
5:48:51here on the right in pivot table Styles
5:48:52and we can actually change what it may
5:48:55look like in this case I sort of like
5:48:57this one right here the simplistic look
5:49:00I can also change things like column
5:49:02headers which I like the formatting on
5:49:04it or whether I want banded rows or
5:49:07banded columns in my case I kind of like
5:49:09the banded rows we'll go with that last
5:49:11portion is around the layout if you
5:49:13notice down here we have this grand
5:49:15total over here this is a grand total
5:49:18based on well the column values it's
5:49:21adding up all the values in the column
5:49:22so this is on for the column so if I
5:49:24wanted to turn it off for rows and
5:49:26columns I could come up here and
5:49:27actually do that I kind of like this so
5:49:29we're going to leave it on I could also
5:49:30turn on on for the rows and columns but
5:49:34in this case because we're doing
5:49:35different aggregation method so a count
5:49:37here and an average here it's not
5:49:40necessarily going to do anything over
5:49:42here for the row grand total whereas for
5:49:45something like the columns gram total
5:49:48that knows that hey for a job count I
5:49:50probably need the total count for the
5:49:52average I probably need an average and
5:49:54that's what it does for both of these
5:49:56there's some additional ones up here on
5:49:57adjusting the report layout adjusting
5:49:59for blank rolls and then also subtitles
5:50:01we'll be exploring that as we go along
5:50:02as we build out more complex pivot
5:50:04tables
5:50:07so let's now get into that final
5:50:09analysis and we're going to be creating
5:50:11basically this pivot table that we did
5:50:13previously with formulas and functions
5:50:17so what we'll need to do or think of
5:50:18right we're going to need the job title
5:50:20short in the rows and we're going to
5:50:23need the month the job posted months in
5:50:27the columns and then we'll need to
5:50:28aggregate this by count for the values
5:50:31now I can navigate back to the data Tab
5:50:33and once again go to insert pivot table
5:50:36if you notice here it says from table or
5:50:38range so that's the really good thing if
5:50:41we actually convert this to a table
5:50:43we'll now be able to once we do this
5:50:45press okay and rename this to something
5:50:48like jobs now we can really be anywhere
5:50:51in this workbook in this case I created
5:50:53a new sheet I go hey insert from table
5:50:57arrange specifically I want to do a
5:50:58table of jobs and we want to do this
5:51:01existing worksheet in A1 and all the
5:51:05values from that jobs table are now here
5:51:08so we know we need the job title short
5:51:10along the rows but then we need the job
5:51:13posted month across the top which right
5:51:17now we have a date we could put the date
5:51:19into the columns but we get this air
5:51:22Message hey you cannot place a field
5:51:23that has more than well 16,000 different
5:51:26values for it so we're not going to do
5:51:28that also before we forget I'm going to
5:51:30rename the sheet to monthly count anyway
5:51:32we need a monthly value here so what
5:51:36going to have to do is good thing about
5:51:38the table itself is now that we've
5:51:41created this as a table I know next to
5:51:43this job posted date colum I want to
5:51:45insert in a column called job posted
5:51:48month and for this we'll just use that
5:51:51text function that we already know using
5:51:53the value of job posted date and then
5:51:56for the format we know we want three
5:51:59lowercase M to get the month itself it's
5:52:02going to fill all the way down okay so
5:52:03now we have job posted month going back
5:52:05to our pivot table itself remember we're
5:52:08not going to see job posted month in
5:52:11here until we actually go back into
5:52:14pivot table to analyze and click
5:52:17refresh now job posted month is inside
5:52:21of here and conveniently it's also in
5:52:23the correct order now this thing is
5:52:25completely blank right now we need to
5:52:27actually add what values we want so I'm
5:52:28going to drag job title short into
5:52:31values and it's going to do a count
5:52:33notice here we do have column value Val
5:52:37which go up and down and then the row
5:52:38values itself so we can see what the
5:52:41count of business analyst is around 101
5:52:44I'm not really a fan of these things
5:52:46that say row and column labels I'm going
5:52:48so I'm going to toggle off field headers
5:52:49to make this look a little bit better
5:52:51and I'm also going to change the name of
5:52:53this to monthly job count so bam this is
5:52:56looking good and we compare it to our
5:52:58basically non-pa table just to make sure
5:53:00that our values are correct we can see
5:53:01we have 982 for data analyst come over
5:53:04over to data analyst we have 982 all the
5:53:07last thing we want to do is actually
5:53:09filter this down and better sort our
5:53:11values specifically I'm curious about
5:53:13roles in the United States so I'm going
5:53:15to drag that job country over here and
5:53:18select United States from here to apply
5:53:20to it additionally I care about the most
5:53:22important jobs at the top and the least
5:53:24important at the bottom mainly by this
5:53:26grand total right here and so what I can
5:53:28do is I can sort it by the grand total
5:53:30but if you notice I remove that that
5:53:32filter button right here whenever I
5:53:34actually remove the field headers so I
5:53:36can also go in Instead rightclick This
5:53:40Grand the value inside of grand total
5:53:42and I can say sort from in our case
5:53:45largest to smallest so I feel like that
5:53:48makes it a lot more convenient alsoo
5:53:50sort additionally I'm noticing the
5:53:51formatting isn't correct for this I'm
5:53:54going to put in that comma separator and
5:53:55then remove the two decimal places
5:53:58similarly not only did we sort by the
5:53:59grand total let's say I only wanted
5:54:01maybe the top six of these right here I
5:54:04could rightclick any of these job titles
5:54:06right here and then go into filter in
5:54:09this case I'm going to go top 10 instead
5:54:12I'm going to select top six press okay
5:54:15now that we have this all sorted I can
5:54:17once again go into that design tab
5:54:19change the grand totals we're going to
5:54:20turn it on for columns only and Bam now
5:54:25we have basically the same pivot table
5:54:28that we had before with our values or
5:54:30using formulas but instead now with
5:54:32pivot tables and this is a lot more
5:54:35customizable all right all right it's
5:54:36your turn now to get your hands dirty
5:54:38with some practice problems and
5:54:39exploring how to make some different
5:54:41pivot tables in the next lesson we're
5:54:43going to go deeper with pivot tables
5:54:45looking at things like grouping
5:54:47hierarchy and how we can show different
5:54:49values as with that I'll see you in the
5:54:51next
5:54:55one so let's get into some Advanced
5:54:58pivot table features and for this lesson
5:55:01and actually for everything in advanced
5:55:03chapter we're going to be sticking with
5:55:05that salary data set of over 30,000 rows
5:55:09in order to actually analyze for this so
5:55:11I'm not going to be calling it out
5:55:13really any further into other lessons or
5:55:16chapters the first thing we're going to
5:55:17focus on is hierarchy which allows us to
5:55:20look at things like we want to aggregate
5:55:23not only the job title itself but also
5:55:25by the country so what job titles are
5:55:27within a country and then look at
5:55:29specific values there for say like the
5:55:31salary next we're going to move into
5:55:33grouping focusing first on automatic
5:55:35grouping basically using that job posted
5:55:37date column to automatically aggregate
5:55:40by year month and whatnot and from there
5:55:43we'll then shift into some manual
5:55:45grouping we'll be able to create groups
5:55:48of different job titles and basically
5:55:50break out whether we want to look at
5:55:52maybe senior roles such as senior data
5:55:54analyst senior data engineers and
5:55:55compare them to just normal data nerd
5:55:58roles such as data analyst or data
5:55:59Engineers with this we're also going to
5:56:01dive deep into understanding a deeper
5:56:04method to analyze maybe percentages of
5:56:07totals or percentages of grand totals
5:56:09when analyzing these type of groups for
5:56:12this you can continue working with that
5:56:14workbook you were working with on the
5:56:16last lesson if you've did everything you
5:56:17did there or you can just open the pivot
5:56:21table intro for this lesson once again
5:56:25as a reminder the solution is going to
5:56:27be in pivot table Advanced we don't want
5:56:30to open that just yet because it could
5:56:31mess up what we're doing here if I could
5:56:34it is so we have four different sheets
5:56:35that we cre created with this I only
5:56:37really care about the data tab right now
5:56:38so I'm actually going to select all
5:56:40these other ones by holding control and
5:56:42then right clicking it to hi
5:56:47them so let's actually look what a
5:56:50hierarchy actually creates I'm going to
5:56:52go in and insert a pivot table from
5:56:54table arrange remember we're using that
5:56:56table of jobs you should have named the
5:56:59table that in order for this to work and
5:57:00we're going to insert it in a new
5:57:01worksheet I'm going to move this over
5:57:03and I'm also going to create uh call
5:57:05this sheet hierarchy so for this we want
5:57:08to look at the salaries for job titles
5:57:11in a certain country so we're going to
5:57:14start by dragging that job country over
5:57:16to Rose and right now there's no
5:57:19hierarchy but if I drag job title short
5:57:23into the rows as well when we close this
5:57:25tab up here we can see that now we have
5:57:27two values in here and how we have
5:57:30values underneath here we've now created
5:57:33a hierarchy so Albania is basically the
5:57:36parent or the top of this and then we
5:57:39have data analyst data scientist senior
5:57:41data scientist notice there's only three
5:57:42values here and that's because an
5:57:44Albania sort of a smaller country they
5:57:46only have three types of jobs there at
5:57:48least in the data set now we want to
5:57:49look at salary for this so I'm going to
5:57:51drag the salary your average into the
5:57:53values it's going to do a sum once again
5:57:56going into value field settings I'm
5:57:58going to change this to average rename
5:58:00the title to salary year average and
5:58:02then changing the number format to
5:58:04currency with zero decimal places
5:58:07pressing okay for all this bam now I'm
5:58:09also curious by this how many jobs we
5:58:13actually have with a salary value this
5:58:16just sort of an add-on so I'm going to
5:58:17drag that salary year average over going
5:58:20into the value field settings I'm going
5:58:21to do a count of this and we'll call
5:58:25this job count click okay so now we get
5:58:28more of a relative idea of how many jobs
5:58:30are so in Albania we have well only five
5:58:32job postings so now I want to get into
5:58:35actually seeing what countries have the
5:58:38highest pay now as a refresher you can
5:58:41come in here and select the dropdown and
5:58:43we could either select how we're going
5:58:45to filter the row labels or filter the
5:58:48value labels but remember we want to
5:58:51sort them and right now this is only the
5:58:52or option to sort a toz or Za to a for
5:58:55those row labels instead I can just
5:58:58click make sure I'm clicking the Sal
5:59:00your average because that's where I care
5:59:01about I can rightclick it and from there
5:59:03go to sort in this case sort large just
5:59:06the smallest and what it did is it
5:59:08sorted the values well within each of
5:59:11these it still kept this kept the
5:59:13countries in alphabetic order instead
5:59:15what I can do is Select this cell for
5:59:17the countries because I want to sort the
5:59:19country's highest to lowest and then I
5:59:22can sort largest to smallest as well now
5:59:25this is pretty neat because now we can
5:59:26see things like Belarus Russia Bahamas I
5:59:29got to go down there have some of the
5:59:31highest salaries by country and then
5:59:32what those are based on the different
5:59:34job titles there now some sometimes I
5:59:36find reading this somewhat difficult in
5:59:39this manner that it's laid out here I'm
5:59:40going to show you how you can actually
5:59:41change this so going back into the
5:59:43design tab remember we had this reports
5:59:46layout that we sort of breezed over last
5:59:49right now it's in this show in compact
5:59:51form we can actually change this to
5:59:53something like show in outline form and
5:59:55it will basically shift this over and
5:59:58have this hierarchy basically in two
6:00:00separate columns it also makes it nice
6:00:02that you can actually a little bit
6:00:03easier to sort with another method is
6:00:06show in tabular form so now it basically
6:00:09crunches it up and I actually like this
6:00:11one even better and it's still breaking
6:00:13out the job country and job title short
6:00:15into two different columns but now it's
6:00:17actually aggregated to less line so I
6:00:19can actually see more data on here now
6:00:21this is definitely a form that I'd like
6:00:23if I want to hand over on boss and even
6:00:24if I wanted to convert this even further
6:00:26to what is this repeat all item labels
6:00:29so now I could if I wanted to actually
6:00:31copy and paste this into its own table
6:00:34and analyze further at least now I have
6:00:36like Bahamas with the software data
6:00:38engineer not software data engineer I
6:00:40mean software engineer or senior data
6:00:43engineer anyway you may have noticed
6:00:44there are some blank values in here and
6:00:47that's because it has an Associated
6:00:49hourly salary but not yearly what i'
6:00:53need to do is actually apply a value
6:00:55filter because it's a value so I come in
6:00:58here and click to drop down go to Value
6:01:00F filters and then maybe put something
6:01:02like greater than we'll put zero and now
6:01:06those values will
6:01:10disappear next analysis we're going to
6:01:12do is a count by the job month but we're
6:01:16not going to use the this job posted
6:01:18month column that we created in the last
6:01:20lesson instead we're going to use
6:01:22automatic grouping for this so we'll go
6:01:25ahead insert in a pivot table we'll
6:01:28insert into a new sheet and we'll call
6:01:31this group automatic I'll go ahead and
6:01:34move that to the very end okay so what
6:01:36I'm going to do is I'm going to take the
6:01:37job posted date remember it's a bunch of
6:01:40dates and I'm going to throw it into the
6:01:42rows and this is going to get into some
6:01:44aggregation it's going to take a little
6:01:45bit to load my computer's not even
6:01:47loaded yet but it's about 15 seconds
6:01:49later and it is now available if you
6:01:52notice now we have this hierarchy of
6:01:56this grouping and I can now dive into in
6:02:00this case January and then one Jan here
6:02:04and then diving in further we can dive
6:02:06into specific times of job postings
6:02:09going to go ahead and close this up if
6:02:11we actually investigate over here inside
6:02:14of here we can see that after I dragged
6:02:16that job posted date over it basically
6:02:18created a month days and then the date
6:02:22itself which is actually a date time but
6:02:25anyway three different values its own
6:02:28hierarchy with this automatic grouping
6:02:31and so now I can go in and do something
6:02:34like drag the job title short into here
6:02:36to get the job count I'm going to change
6:02:38this
6:02:39to job count also go in and actually
6:02:43adjust the formatting but now whenever
6:02:47actually go into each one of these
6:02:48hierarchies and look in we can see how
6:02:50many job postings were having on a daily
6:02:53bra basis and how many were happening at
6:02:56a certain date time so now let's say I
6:02:58wanted to dive deeper to understanding
6:03:01maybe why July had such a high number
6:03:04compared to all the other months one I
6:03:07could double click it or I can just
6:03:08rightclick it and go to show details
6:03:12this is going to show well the details
6:03:15and if we actually go over to the job
6:03:17posted date column it's going to have
6:03:20all the values for July inside of here
6:03:24so this is a pretty unique way to get
6:03:27into diving deep and showing the details
6:03:30of what is the data being used to
6:03:33perform these aggregations and also
6:03:35double check your
6:03:38work now we're going to get into manual
6:03:40grouping specifically we're going to
6:03:42create this where we actually go through
6:03:45and aggregate based on the job titles
6:03:49itself assigning it into well a group so
6:03:51put data analyst scientists and data
6:03:53Engineers into Data nerds senior RS into
6:03:55senior data nerds and then these guys
6:03:57into other data nerds so we're going to
6:03:59create a pivot table for this go in and
6:04:02select okay using the jobs table and
6:04:04we're going to be grouping the job title
6:04:06short so I'll drag that into the rows
6:04:08for the time being and we'll just start
6:04:11by grouping just the data nerds so I'm
6:04:14going to just select one of these and
6:04:16then hold down control and then also
6:04:17select data engineer and then also data
6:04:19scientist then I'm going to rightclick
6:04:21it and select group the other way I
6:04:24could also do this is go into pivot
6:04:26table analyze and select group selection
6:04:30the next one on want to group are senior
6:04:32roles so I'm going to just select all
6:04:33the different senior roles conveniently
6:04:35they're all right next to each other
6:04:37then I'm going right click it and go to
6:04:39group so now they're the own group the
6:04:41only thing left is getting the rest of
6:04:43these I'm actually going have to control
6:04:45these select these and then these as
6:04:47well and then from there we'll group
6:04:51that for group one I'm just going to
6:04:52select it come up to the formula bar and
6:04:55type in data nerds name group two to
6:04:58senior data nerds and then group three
6:05:01to other data nerds also going to zoom
6:05:04in a little bit to get a little bit
6:05:05closer
6:05:09now that we have all these grouped let's
6:05:10actually dive into performing a
6:05:13basically deeper anal analysis on this
6:05:16to look at how or what percentages these
6:05:19make up of all the job titles and also
6:05:23of their respective groups specifically
6:05:26we're going to be looking at on going
6:05:27job the job title short over here we're
6:05:29looking at the count and how the counts
6:05:32of those jobs are going to be of the
6:05:34percentages anyway I'm going change this
6:05:36to job count along with going through
6:05:39and updating the formatting to use a
6:05:41comma and no decimal places so for this
6:05:44I still wanted to use that basically
6:05:46count of the job title short so with
6:05:49these counts we're going to do the
6:05:50percentages so I'm still going to use
6:05:51that job title short column going to
6:05:53drag it into the values we did a count
6:05:57but now let's actually go in inside of
6:05:59the value field setting remember we got
6:06:02to that show value as and inside of here
6:06:06we can have different values percent of
6:06:09grand total percent of column total
6:06:11percent of row total we're just going to
6:06:12go percent of grand total to start press
6:06:15okay and Bam this is now showing us the
6:06:19percent of the grand total now I'm not
6:06:22liking how this is ordered right now I'm
6:06:24actually going to I'm going to sort this
6:06:26selecting one of the values inside of
6:06:28the job count from sort it from largest
6:06:32to smallest and then also I want to do
6:06:34the actual grand total itself sort
6:06:36largest to smallest anyway we updated
6:06:39this to percent of grand total we need
6:06:41to update the title to specify perent of
6:06:44grand total and so we can see that data
6:06:47nerds for their parent are taking up
6:06:50about 76% almost 34 of the jobs are that
6:06:54and individually we can see that data
6:06:56analysts are nearly 30% of that whereas
6:06:59we get down to the other data nerds
6:07:00they're only taking up a very small
6:07:02percentage now what happens if we want
6:07:04to see so what is data analyst of the
6:07:08actual parent or what is the cloud
6:07:09engineer of the parent other data nerds
6:07:13well I can drag that job title short
6:07:15into the values again it's aggregating
6:07:17by count but I can go in and this time
6:07:20I'm actually just going to rightclick it
6:07:23and we can have this show value as I'm
6:07:25going to use that instead we can do that
6:07:27percent of grand total but instead I'm
6:07:29going to come down here to percent of
6:07:32parent total and in our case it's asking
6:07:35us what is the parent now you didn't we
6:07:38haven't gone over this but it actually
6:07:40recreated that that grouping as job
6:07:42title short two so I'm going to click
6:07:44okay we don't want to do the job title
6:07:46short that's not the parent job child
6:07:49short to and that's you can see it
6:07:52actually down here job title short too
6:07:54it created inside the rows but anyway
6:07:56getting back to the parent now it's
6:07:58showing the percent that it takes to
6:08:01make the parent and then obviously the
6:08:02parent is at 100% so I'm going to rename
6:08:06this one percent of parent now we just
6:08:09looked at percent of grand total and
6:08:11percent of parent but the show value as
6:08:14has a lot of different other ones you
6:08:16can also do in here if I wanted to I can
6:08:18even do something like rank largest to
6:08:22smallest once again it's asking us do we
6:08:24want to rank part of the parent or part
6:08:26of the job tile short I want to rank
6:08:27part of job tile short and it will show
6:08:29its individual rankings underneath each
6:08:32from highest to lowest I'm going to go
6:08:34ahead and undo this I don't want to know
6:08:35necessarily keep that one more note
6:08:37before we go for those that purchase the
6:08:38course practice problems and also note I
6:08:41also go into calculated items and field
6:08:45and have its own little worksheet for
6:08:47you to follow along and try out
6:08:49calculated items and field I didn't
6:08:51necessarily include it in this lesson
6:08:53because I felt that it wasn't a very
6:08:56powerful feature I instead use like
6:08:58using measures instead which we going to
6:09:00cover in the power pivot chapter but if
6:09:02you're interested about it I have
6:09:04content on it in our notes and those
6:09:06calculated field and items is underneath
6:09:09that pivot table analyze tab in here on
6:09:12calculated field and items we're not
6:09:13going to be covering it outside of those
6:09:15notes that you can follow along and do
6:09:16your own self-study with it all right
6:09:18you have some practice problems now go
6:09:19through and get more and familiar with
6:09:22these Advanced features and pivot tables
6:09:24because in the next lesson we're going
6:09:25to be diving into actually making charts
6:09:28out of these pivot tables using pivot
6:09:29charts with that see you in the next one
6:09:36moving now into pivot charts so we did a
6:09:39lot of work already in analyzing things
6:09:42with pivot tables we're going to take it
6:09:44now to Next Level pivot charts
6:09:46specifically we're going to be looking
6:09:47at first what is the average salary by a
6:09:50job title next we'll be looking at which
6:09:53job has the highest percent of demand
6:09:55and then finally lastly we'll be looking
6:09:58at how basically how are jobs trending
6:10:01over time we're going to be building all
6:10:03these charts using pivot tables
6:10:05additionally we're going to include the
6:10:07features of slicers and also timelines
6:10:12based on what chart we're using in order
6:10:14to be able to filter down and more
6:10:16easily make our graphs more interactive
6:10:19as usual in the advanced chapters I want
6:10:20you to starting with the Excel workbook
6:10:24from the last lesson so pivot tables
6:10:26advance and if you want to see the
6:10:27examples or the final answer you could
6:10:29go to Pivot charts for this we're not
6:10:31going to be using the hierarchy or that
6:10:34show Det tail tab so I'm going to go
6:10:37ahead and hide
6:10:40those so let's create this first chart
6:10:43to analyze what is the top paying job in
6:10:45data science for this I'm going to just
6:10:47create a new pivot table for this using
6:10:50that jobs table and we're going to be
6:10:52aggregating by job title short in the
6:10:54rows and then the salary your average in
6:10:57the values and for this we want to
6:10:59summarize values we don't want to do the
6:11:01sum we're going to do the average I did
6:11:03this by right clicking it but we do to
6:11:05have these all formatted correctly in
6:11:08currency with no decimal places and I'll
6:11:10update the title as well to average
6:11:13yearly salary so in order to insert in
6:11:17this pivot chart we're going to go to
6:11:19the insert Tab and we're going to come
6:11:21here to Pivot chart there's only one
6:11:23option right now because we're selected
6:11:25on a pivot table and that's a pivot
6:11:27chart itself so I'll go ahead and insert
6:11:28it with this there's no recommended
6:11:30charts but I know I want a column chart
6:11:33so we're going to go with that and if
6:11:35charts aren't that different from
6:11:36regular charts I can come up here select
6:11:38this plus sign I can remove things like
6:11:41the legend I don't really need that and
6:11:43then I can change things like the title
6:11:45by just double clicking this to
6:11:47something like what is the top paying
6:11:49job in data science now you may notice
6:11:51these pivot charts are a little bit
6:11:52different as they have these field
6:11:54buttons on here that basically allow you
6:11:56to with the chart itself go in and
6:11:58filter it this is really convenient if
6:12:00them say this chart was in a different
6:12:01page anyway I want to have these salary
6:12:05sorted from highest to lowest so I can
6:12:08come into here and you know we can go
6:12:10sort A to Z or Z to A and you can change
6:12:14it around we want to actually sort from
6:12:16highest to lowest so I can come in here
6:12:18under more sort options and I can change
6:12:21this from the job title short column to
6:12:23that average yearly salary column and we
6:12:27want it to be descending and we'll click
6:12:29okay so bam now we have our salary
6:12:33oriented from high to low with our
6:12:35values if you don't like these field
6:12:38buttons right here you can come in and
6:12:40right click it and go hide all field
6:12:43buttons if you want but if you want to
6:12:45get them back you have to come back
6:12:46underneath the pivot chart analyze Tab
6:12:49and select field buttons and uncollect
6:12:52this hide
6:12:55all the next thing to analyze is which
6:12:58job has the highest percentage of demand
6:13:01we're going to use that percentage of
6:13:02grand total column before and we're
6:13:04going to be adding a little twist with
6:13:05this one as we're going to be also
6:13:07building in some slicers so we can slice
6:13:09the data for what we want so back inside
6:13:12the work should you should be working in
6:13:13so we want the percent of grand total
6:13:15only so I'm going to move out count
6:13:18percent of parent and also that rank
6:13:20count to only have what we want next
6:13:22move into getting a pivot chart Built
6:13:24For This and once again I'm going to be
6:13:26using that column chart I'll go ahead
6:13:28and insert that I'm going rename this to
6:13:30which job has the highest percentage
6:13:32once again I don't really care about
6:13:34that Legend now I want my basically
6:13:38target audience whoever I give this to
6:13:40to have control to be able to select
6:13:42which group they can filter for whether
6:13:46that's data nerds senior data nerds or
6:13:47other data nerds so in order to control
6:13:50that I'm going to first zoom out we're
6:13:52going to insert some slicers for this so
6:13:55if we come into the pivot chart analyze
6:13:57tab we can have with this chart selected
6:14:01I'm going to go into insert slicer and
6:14:05we're going to do it for remember that
6:14:06that group from last time is actually
6:14:08job title short 2 and also we're going
6:14:10to filter this one also by country I'm
6:14:13going to click okay they're going to pop
6:14:15up here on top of this I don't really
6:14:16like this I'm going to drag it over and
6:14:18I'm going to fix the formatting real
6:14:19quick so now with these slicers I can
6:14:23make it a lot easier for somebody using
6:14:25this to come in and say hey I only want
6:14:27to look at data nerds or I want to look
6:14:29at other data nerds and see what their
6:14:33appropriate percentage is when you click
6:14:35on a slicer you will notice that this
6:14:37slicer tab comes up there's some
6:14:39different formatting options the one
6:14:41thing that I Define myself do changing
6:14:43is the appropriate label or the slicer
6:14:46caption in this case I would rename this
6:14:49one to something like job group and then
6:14:51for the job country I would just rename
6:14:53this to Country and you can see they
6:14:56update appropriately here for it as a
6:14:58refresher right if you wanted to select
6:15:00multiple different op options I would
6:15:03select this multi select right here and
6:15:06then with that enabled I can then select
6:15:07data nerds and also senior data
6:15:12nerds the last visualization we're going
6:15:14to be building with this it's a line
6:15:16chart looking at how jobs are trending
6:15:18out of time using that previous pivot
6:15:20table we made on the job count for this
6:15:22one we're going to be using a timeline
6:15:24filter to be able to select down to
6:15:26maybe a certain quarter or month so back
6:15:29in the workbook that we're working in
6:15:30I'm in this group automatic sheet we
6:15:32want to create a pivot chart so I go to
6:15:34insert and into pivot chart and for this
6:15:37one we want a line so I'm going to go
6:15:40ahead and insert that I'm going to give
6:15:41this appropriate title of how are jobs
6:15:44trending over time additionally I'm
6:15:47going to remove that Legend and I want
6:15:49to add a trend line to it now you notice
6:15:52by this one the actual field values for
6:15:55this you have multiple different ones
6:15:57here remember it did that automatic
6:15:59grouping in the last lesson so you have
6:16:01not only months to filter by days and
6:16:03also that job posted date so a lot more
6:16:05values here now to add a timeline for
6:16:07this I'm going to go up to Pivot chart
6:16:09analyze and I'm going to go into insert
6:16:12timeline there's only one value that's
6:16:13going to be available for this job
6:16:15posted date and right now if I expand
6:16:18this all the way out we have all the
6:16:20different months that are available I'm
6:16:22going go ahead and close this up right
6:16:24below it so if I wanted to filter by a
6:16:27specific month I could be like hey I
6:16:29want from February to November in this
6:16:32case October I actually need to select
6:16:34February I'm holding down my key for
6:16:36this and then dragging to November
6:16:38anyway I can also change this with this
6:16:40filter not only months but also quarters
6:16:44and even something like years I prefer I
6:16:47typically analyze things in quarters so
6:16:49we're going to do it that manner and I'm
6:16:51also going to shift it up here to the
6:16:53right hand side similar to slicers if I
6:16:55have the timeline selected I can come up
6:16:58here and actually change the name in
6:17:00this case I'm going to change it to date
6:17:02I could also change thing like
6:17:04formatting or even things like color now
6:17:07one thing to note with this with what I
6:17:09have selected here it's only going to
6:17:11filter what I have the chart set up to
6:17:14or what I actually created the timeline
6:17:16while the pivot chart was selected so
6:17:17let's say I came into here and I wanted
6:17:19to look at in our case just data nerds
6:17:22and then also go into looking at the
6:17:25counts themselves this isn't necessarily
6:17:27going to update for that those slicers
6:17:30aren't connected to other charts but you
6:17:33can change it to do that so in this case
6:17:35I could select something like the pivot
6:17:37table itself going into pivot table
6:17:39analyze and then here under filters
6:17:42where you can create things like slicers
6:17:44and timelines which we did in the pivot
6:17:46chart anyway they have this thing called
6:17:47filter connections and I'm going to
6:17:50expand this out so we can actually see
6:17:52it and right now we're saying that well
6:17:54for pivot table 3 as we can see up here
6:17:56probably need to give these even better
6:17:58names only the date is actually
6:18:02connected to this if I wanted to connect
6:18:03the other ones such as country or job
6:18:05group I'd have to select them and press
6:18:08okay now I don't know if you noticed
6:18:10that but it actually adjusted these
6:18:12values actually decrease because I have
6:18:15less values selected here whereas if I
6:18:17actually select more all of these going
6:18:20on this is going to increase the values
6:18:24anyway that's sort of hard to see let's
6:18:25actually show this by with uh sheet one
6:18:29which actually should be something like
6:18:31top paying jobs and in this case I can
6:18:35go into pivot chart analyze into filter
6:18:39connections and this is going to show us
6:18:41based on pivot table 7 which is this one
6:18:44right here I should have renamed these
6:18:46there's no different slicers or
6:18:48timelines attached to it so I can
6:18:49actually select all of these and apply
6:18:52it to this one and now when I go to our
6:18:55grouping right here right so we had all
6:18:57of them selected if I want to just look
6:18:59at data nerds here so I can see the
6:19:01percentages of data analyst dat engineer
6:19:02and data scientist I can see what their
6:19:05salaries are for it and then also I can
6:19:08see their counts for those as well so
6:19:11this is definitely a useful feature if
6:19:12you're looking to link charts or
6:19:15specifically pivot tables that are not
6:19:17necessarily
6:19:18connected all right now it's your turn
6:19:20to get more familiar with using pivot
6:19:22charts we have some practice problems
6:19:24that you go through and actually
6:19:25understand more about how to use them
6:19:27with that in the next lesson we're going
6:19:29to be jumping into well the next chapter
6:19:32on Advanced Data analysis and using some
6:19:34pretty unique and pretty complicated
6:19:37features in order to analyze data so
6:19:39with that I'll see you in that
6:19:45one welcome to this chapter on Advanced
6:19:48Data analysis and this entire chapter is
6:19:51really focused on using addins which are
6:19:55basically programs that people have
6:19:56built to incorporate into Excel to do
6:19:59very unique and specific tasks because
6:20:03of that going from less lesson to lesson
6:20:05we're not going to necessarily be
6:20:07building on each other as we go through
6:20:09these lessons every lesson is going to
6:20:10be sort of its own unique sort of
6:20:12Learning Journey about a specific
6:20:14feature or features to start with this
6:20:17lesson we're looking at just enabling
6:20:19the add-ins and looking at some basic
6:20:21ones such as what if analysis and we're
6:20:24going to get more into it in a second
6:20:25but we're going to be focused on looking
6:20:27at if weed three different job offers
6:20:30which one should we actually take in the
6:20:32next lesson we're going to be continuing
6:20:34on with what analysis focusing on data
6:20:36tables and this shows us how values are
6:20:38going to be changing based on one or
6:20:41multiple variables and then finally the
6:20:43third lesson is on an addin called
6:20:46analysis tool pack that provides us
6:20:49access to a lot of different statistical
6:20:52analysis that we can just easily select
6:20:55what type of analysis want to perform
6:20:57and it does all the analysis for for us
6:20:59and provides it in a sheet pretty neat
6:21:02anyway getting into this lesson we're
6:21:04going to start by first enabling these
6:21:06add-ins so that way you have it and then
6:21:07from there we're going to move into our
6:21:09first somewhat simple example
6:21:11forecasting what's going to happen into
6:21:13the future specifically we're going to
6:21:15look in at our past job postings and try
6:21:17to predict what's going to happen in the
6:21:19future from there we're going to be
6:21:20moving into what if analysis and for
6:21:24this we're going to have a scenario
6:21:26where we have three job offers and we're
6:21:28trying to find what is the most optimal
6:21:30one we're going to use things like
6:21:32scenario manager to go through and
6:21:34automatically calculate what it should
6:21:36be for those three different job offers
6:21:39and then let's say we need to actually
6:21:40negotiate one of those job offers and we
6:21:43want to match another we can use solver
6:21:45or goalkeeper and both of these have
6:21:48both unique different features of them
6:21:50that we're going to dive into to allow
6:21:52us to adjust what we could potentially
6:21:54negotiate for better job offers one
6:21:57quick reminder on which versions of
6:21:59excel will support this chapter on
6:22:02Advanced Data analysis all of them will
6:22:04with the exception of Microsoft online
6:22:07it doesn't have the ability to add in
6:22:10these specific addins but you're on Mac
6:22:13or the windows version you're going to
6:22:14be completely fine so for this we're
6:22:16going to be working inside of the
6:22:18analysis addins workbook I know it said
6:22:21previously you need to work with the
6:22:22previous workbook from the previous
6:22:23lesson but this chapter in general
6:22:26doesn't build on anything it has
6:22:27everything you need within the workbook
6:22:30so you're going to be fine with this
6:22:31anyway we just need two sheets from this
6:22:33forecast original and what if analysis
6:22:35all the others are just the results that
6:22:37we're going to be getting and feel free
6:22:39to go through and select the sheets that
6:22:41we're not using so these four in this
6:22:42case and hide them so that way we only
6:22:46have the two sheets of forecast original
6:22:48and what if
6:22:52analysis so before we enable addins I
6:22:54think you need to know what are exactly
6:22:57Excel addins here I am in perplexity a
6:23:00and I asked the question and it goes
6:23:02into to specify What It Is by saying
6:23:04that basically interacts with Excel
6:23:06objects and data and it will add custom
6:23:09ribbon buttons or menu items and thus
6:23:12providing custom functions now this is a
6:23:14little technical but there are three
6:23:16different type of addins they have web
6:23:18Excel and com add-ins today we're going
6:23:20to be importing in Excel addins which
6:23:23are actually created using something
6:23:24like VBA anyway the most popular Excel
6:23:27add-ins are things like solver power
6:23:29pivot power query you don't necessarily
6:23:31have to add in unless it's not included
6:23:33and then also things like analysis tool
6:23:35pack which we're going to get to in that
6:23:36third lesson all right enough on the
6:23:38history lesson let's actually get into
6:23:39enabling your addins if you go to the
6:23:41data tab right now you'll probably see
6:23:43that you have this forecast section so
6:23:45you do have what if analysis available
6:23:47but you don't have anything ex else over
6:23:50here right now it's um well usually
6:23:52blank but we're going to add to it so
6:23:53I'm going to go into file and then from
6:23:55there it's hidden but under more I'm
6:23:57going to go to options on the menu on
6:23:59the left hand side I'm going to go into
6:24:01addins and this menu right here tells
6:24:03you what your active application addins
6:24:06are right now I have no active
6:24:07applications and then your inactive
6:24:10application addins so I do have access
6:24:12to all these different ones right here
6:24:14so I want to enable them specifically I
6:24:17want this analysis tool pack and then
6:24:19well the one we're going to use in this
6:24:20lesson solver so um on manage I have
6:24:24Excel addins that's the one that I want
6:24:25to actually use for this I'm going to
6:24:27click go and now we need to enable which
6:24:29ones we're going to use so analysis tool
6:24:31pack for the third lesson and solver for
6:24:33this one from there I'm going to click
6:24:35okay and now over here on the right hand
6:24:38side we have analysis popup data
6:24:40analysis which is the analysis tool pack
6:24:42and then solver is the solver
6:24:47added so let's actually get into
6:24:50forecasting specifically looking at what
6:24:52we expect job postings it to be next
6:24:55year and right here in the forecast
6:24:57original sheet I have date and then also
6:25:00the job count and this goes all the way
6:25:03for or this is all the data for 2023
6:25:07anyway this example is going to show the
6:25:08custom features that we really can do
6:25:11with some of these add-ins and also
6:25:12built-in features so I can select the
6:25:15date and job count column and then for
6:25:18this we're going to go into the forecast
6:25:20and specifically to forecast sheet in
6:25:23this it plots in blue what are our
6:25:26values that we currently have for
6:25:28basically 2023 and then from there it
6:25:31plots into the future using this orange
6:25:33I can toggle this between this a line
6:25:35chart and also a column chart but I'm
6:25:38not really finding the column chart that
6:25:39useful It's Time series data so I'm
6:25:41going to go back to that line chart the
6:25:43other major thing I control is the
6:25:44forecast end date so if I wanted to only
6:25:46do maybe two months I could change this
6:25:49instead to end in March additionally
6:25:52have hidden underneath this drop down of
6:25:54options the ability to go in and
6:25:57actually change other things like
6:25:59confidence interval and seasonality and
6:26:01things like that right now it's
6:26:03automatic set it up to basically detect
6:26:06automatically and seasonality is as you
6:26:08notice in this data it goes up and down
6:26:11up and down up and down it has a
6:26:12seasonality to it basically every single
6:26:15week there's more postings during the
6:26:16week and on the weekend there's less as
6:26:19expected so this seasonality is carried
6:26:23out into the predicted data as you can
6:26:25see here because it's still in the
6:26:27orange actually goes up and down anyway
6:26:30going to close this this is great I'm
6:26:31going to click create in this new sheet
6:26:34it automatically has this popup here
6:26:36that says this table contains a copy of
6:26:38your data with additional forecast of
6:26:40values at the end you can manually edit
6:26:42the forecasting formulas in the sheet or
6:26:43return to the original data to create a
6:26:45different forecast worksheet okay great
6:26:46got it I'm going to zoom out a little
6:26:48bit and what this table did is it still
6:26:50kept that date and job count column but
6:26:52it also built out three other columns to
6:26:55actually look at scroll all the way down
6:26:58what the forecasted would be a lower
6:27:01confidence band and then an upper
6:27:02confidence band and then looking at the
6:27:05actual chart that it provides we can see
6:27:08this where this darker orange color is
6:27:11what The Forecastle band is this is the
6:27:13upper band and then this is the lower
6:27:14band anyway that's pretty cool that I
6:27:16could generate this all by just clicking
6:27:19a single button of forecast
6:27:23sheet all right now we're going to move
6:27:25into wh if analysis and we click this wh
6:27:28if analysis we have three different
6:27:30things here we have scenario manager
6:27:32goal seeker and data table
6:27:34for this one we're going to start with
6:27:36scenario manager but let's first go over
6:27:38what the data is here in the sheet that
6:27:40we're trying to basically trying to
6:27:42calculate first let's focus on these
6:27:44columns B and C this is a if you will
6:27:47dashboard or calculator that I built so
6:27:49I can put into here a base salary a
6:27:52bonus rate and then an annual raise
6:27:54amount and it will calculate it so let's
6:27:56say our base salary is 12,000 I can put
6:27:59that into here assuming the same 10% and
6:28:011.5% it's going to automatically update
6:28:03for this over here on the right hand
6:28:05side in E through H over here we have
6:28:09three different job offers that we
6:28:12received and they consist of the base
6:28:14salary the bonus rate and the annual
6:28:17raise underneath here this fourth or
6:28:19fifth row if you will this is
6:28:21constraints that we're going to use
6:28:22later on I would just ignore this right
6:28:24now so what's going on down here in the
6:28:26result cell well what we're doing is
6:28:29we're
6:28:30calculating what the expected salary is
6:28:34for year zero all the way to year four
6:28:37and then from there we're actually
6:28:38getting a total so in this case this is
6:28:41summing up all these values right here
6:28:44so why am I doing four years why am I do
6:28:46a total left for these four years well
6:28:48the Bureau of Labor Statistics basically
6:28:51estimates that most people have the
6:28:53average tenure at a company of four
6:28:55years so the idea with this calculator
6:28:59that I've made is that we're able to
6:29:01calculate based on a job offer we re
6:29:04what would we expect if we were to stay
6:29:07at the basically average amount or
6:29:09median amount of time that a normal
6:29:11person stays at a job like just looking
6:29:13at what's the first year because
6:29:15sometimes things like bonuses and annual
6:29:18raise may actually push us into higher
6:29:21salaries even though the base salary is
6:29:24lower than another salary so it
6:29:26basically helps calculate this out and
6:29:27even the playing field for these three
6:29:30jobs that we're trying to calculate
6:29:31anyway you can go through if you want to
6:29:33and and understand what formulas are
6:29:35going on behind the scenes here but
6:29:37basically I'm just taking into account
6:29:39these three parameters right here and
6:29:41then every year basically starting with
6:29:43that previous years and then adjusting
6:29:46it for the annual raise and then giving
6:29:48it its appropriate bonus so as expected
6:29:51because there's an annual raise on each
6:29:52one of these the salaries are going up
6:29:54so with that what is going on here do I
6:29:57need to actually go through and actually
6:29:59put in every single one of those jobs so
6:30:02I'll put in job one and get the 566,000
6:30:05and then now do the second job and third
6:30:07job no I can use scenario manager for
6:30:10this so going into what if analysis I
6:30:12select scenario manager and we're going
6:30:14to add three different scenarios so I'm
6:30:17going to come up here and select add
6:30:20this scenario name we're going to call
6:30:21it job one next we're going to move into
6:30:25what we're going to use for the changing
6:30:26cells and I've labeled these basically
6:30:29or made these into an input format we're
6:30:32going to select these three right here
6:30:33so C3 through C5 we'll leave the comment
6:30:36as is protection as prevent changes and
6:30:38go to okay now it's going to ask us what
6:30:42values we want to use for each in this
6:30:44case I use 100,000 10% and 1.5 it's
6:30:47already filled in pre-filled in from
6:30:49there I'm going to click okay now we
6:30:51need to add job two for this I'm going
6:30:53to leave changing cells the same this
6:30:55one I'm going to change to 880,000
6:30:5815% and then change this bottom one to
6:31:021.2%
6:31:04then finally we need to add that job
6:31:05three one of the last steps we need to
6:31:07do is now go into summary right here and
6:31:10for this we need to figure out what we
6:31:12want to actually have it provide for us
6:31:15in our case we want the result cell of
6:31:17C9 through c14 to be provided from there
6:31:21we click okay and bam we're going to get
6:31:24this scenario summary sheet that goes
6:31:27through in details based on job one job
6:31:31two and job three for the value that we
6:31:34input into it and from there it's going
6:31:36to tell us what year zero is year 1 2 3
6:31:41all the way down to the total salary now
6:31:43one thing to note is you see these names
6:31:46of Base bonus raise year zero uh and
6:31:48then total salary if I go back to what
6:31:51if analysis I've actually gone through
6:31:53already for you and actually Nam this so
6:31:56in this case I'm selecting zero it's
6:31:58named year zero and total salary if I
6:32:01were to use things that were maybe not
6:32:04named it would just provide the cell so
6:32:06if we're using the values here it would
6:32:08just going to be provide F6 and in that
6:32:10case we would have saw F6 here also back
6:32:13in the scenario summary you may not have
6:32:14ever saw this before but Excel allows
6:32:17this sort of grouping if you will to
6:32:20basically manipulate the sheets and what
6:32:23values are hidden or potentially shown
6:32:26here anyway pretty unique feature that
6:32:28you may or may not have seen
6:32:32before all right moving on to goal
6:32:35Seeker let's say we have the scenario
6:32:38now where we got the job offer for job
6:32:42one in this case but we want to try to
6:32:45match that of job three specifically if
6:32:48I go back to that scenario summary sheet
6:32:49we can see that job one is at around
6:32:53566,000 but job three is at 640,000
6:32:56we'll say we have some Insider
6:32:58information that human resources told us
6:33:02hey we can't adjust the base or the
6:33:03bonus but we can adjust the raise what
6:33:07raise you get every year and so you
6:33:09could potentially ask for a higher Rays
6:33:12what Rays would you need to basically
6:33:14put into here to get equal to that job
6:33:17three so the first thing I'm going to do
6:33:18is go in and make sure that we have
6:33:20inside of our formula input in the job
6:33:22one actual statistics of it so 100,000
6:33:2610% and 1.5% for the annual raise now I
6:33:30could go through there so I type 1.7%
6:33:33and then 1.8% and just keep on going up
6:33:36until I actually find what it is or
6:33:39instead we can just actually use this
6:33:40goal seeker and for this we're going to
6:33:42be setting a cell specifically cell
6:33:45c14 to that 640,000 that we want to get
6:33:50to and we need to provide what cell
6:33:52we're going to actually change in this C
6:33:54case we're going to change cell
6:33:57C5 which is the annual raise no for this
6:34:00we can only change one option we're
6:34:02going to be able to change M multiple
6:34:03the next scenario but not in this one of
6:34:05goal Seeker so from there I'll go ahead
6:34:07and click okay and Bam automatically
6:34:11goes through I don't know if you saw
6:34:13that it Ste through it and it went up to
6:34:167.6% and that's what we'll need in order
6:34:18to get to that 640,000 and it even
6:34:21provides an old nice dialogue box saying
6:34:23that hey it did find a solution
6:34:25sometimes you may put a goal in that's
6:34:27not achievable and in this case it would
6:34:29it would tell
6:34:32you so
6:34:347.76% is a pretty high raise let's say
6:34:38we get further information from HR
6:34:40saying hey we can actually change not
6:34:43only the annual raise but also your
6:34:45bonus we still have the same scenario
6:34:47you can't change the base salary needs
6:34:49to stay at 100,000 for that first year
6:34:52so we have multiple parameters now that
6:34:54are changing this is when we're going to
6:34:56shift from using this goal Seeker now
6:34:59over to solver one thing before we start
6:35:02we need to actually reset these values
6:35:04in here I'm going to change this back to
6:35:081.5% both of these Step Up in value so
6:35:10you want to reset it before you go so
6:35:13opening up solver I'm going to set the
6:35:16objective as before that c14 of that
6:35:19total salary and we want to get it to a
6:35:22salary of 640,000 and we want to do this
6:35:26by like we said we can change two things
6:35:28in this case the bonus and the annual
6:35:30raise we can also add constraints which
6:35:33we'll do in a second after we just run
6:35:36through this one but I want to actually
6:35:37just go through and solve it first and
6:35:40the last thing we need to look at is
6:35:42select a solving method we're going to
6:35:44just leave it here I really like this
6:35:45grg nonlinear we'll leave it that for
6:35:48the time being and we'll go ahead and
6:35:49click solve now for this it says solver
6:35:52found a solution all constraints and
6:35:54optionality conditions are satisfied as
6:35:56we can see it increased the bonus and
6:35:58then also the annual raise and we got to
6:36:00that 640,000 inside of this popup box we
6:36:04can have it output certain reports so
6:36:06I'm going to just hold control and
6:36:08select multiple different reports along
6:36:11with clicking this for outline reports
6:36:13that's it's going to actually print to
6:36:14different sheets and from there click
6:36:18okay anyway the most important of these
6:36:20three different reports that it gave to
6:36:22us feels the answer report basically
6:36:24tells us hey what was the original
6:36:26values put in for the D bonus and raise
6:36:29and then what are the final values in
6:36:31order to get to that final value of 6
6:36:3340,000 they also have these two other
6:36:36reports one on sensitivity analysis and
6:36:39the other one evaluating the limits
6:36:40which we're going to get to um but these
6:36:42I don't find as important so now with
6:36:44this with solver we found that we can
6:36:46input more than one different input now
6:36:49we can also specify constraints if I
6:36:52come back up to solver and it says Hey
6:36:54in this dialogue box subject to the
6:36:58constraint right now the annual raise is
6:37:00sort of low still at 2.1% but that bonus
6:37:04skyrocketed it was previously at 10% and
6:37:07it went all the way up to 23% so we
6:37:09could actually put some constraints in
6:37:11by clicking add and we'll say hey the
6:37:13bonus we're not going to let that exceed
6:37:1715% we'll click add for that and then
6:37:20for the next one we don't want the
6:37:23annual raise to exceed we'll say 4% and
6:37:27we'll click okay remember I did name
6:37:30these cells so that's why it pops up
6:37:32automatically as B and raise makes it
6:37:34super easy whenever you name cells all
6:37:36right let's go ahead and click solve so
6:37:39look at this solver could not find a
6:37:42feasible solution with these constraints
6:37:45basically maxed out the bonus and maxed
6:37:48out that annual raise and we didn't get
6:37:49to that 640,000 so what I can do is I
6:37:52can return to the solver parameters
6:37:54dialogue click okay and in this case
6:37:57I'll change the bonus to we'll say 20%
6:38:01now and then for the raise we'll change
6:38:03this to 5% click okay and then try to
6:38:07solve again and we found a solution we
6:38:11have 17% and
6:38:144.4% and for this I'm going to Output
6:38:16the answers I'll click outline reports
6:38:19to export it click okay close this out
6:38:22and then go to the report we can see
6:38:25what our finally values are along with
6:38:28how we got to our 640,000 final value
6:38:32all right you got some practice problem
6:38:33problem to now go through and try these
6:38:36different features out of scenario
6:38:38manager and goal seeker and also solver
6:38:41and I think once you play around with
6:38:42them more you can find out which one is
6:38:44more applicable to which scenario with
6:38:47that I'll see you in the next section
6:38:49where we're going be going into deeper
6:38:51into what if analysis specifically on
6:38:53data tables one my favorite features of
6:38:55what if analysis with that see you
6:39:01there let's now get wrapped up on what
6:39:04if analysis by focusing on data tables
6:39:07we're going to be focusing on building
6:39:09one input and also two input data tables
6:39:12for the first one on one input we're
6:39:14going to be continuing on with that
6:39:15exercise from last lesson looking into
6:39:19that job offer one and seeing how we
6:39:21could change the annual rays in order to
6:39:25thus affect different salaries at our
6:39:284-year point and mainly the total salary
6:39:31at this point and from there we're going
6:39:32to shift into building two input data
6:39:34tables where we're not only analyzing
6:39:37that annual raise increase but also a
6:39:39change in the bonus rate to see what the
6:39:42different salaries are for that final
6:39:44total amount of those four years so for
6:39:47this lesson and also for this chapter
6:39:48we're going to be starting with the
6:39:49actual workbook of the name of the
6:39:51chapter in this case data tables and
6:39:54we're going to be working this original
6:39:55sheet but I want to jump into that one
6:39:57input to basically show you what we're
6:39:59going to be building
6:40:03we're going to be inputting into here
6:40:05the annual raise percentage we're going
6:40:07to put it in increments of. 5% and then
6:40:11along the top in the row we're going to
6:40:13be inputting the values from over here
6:40:17um and these values right here across
6:40:19the top and then the data table itself
6:40:22is going to fill this in with the
6:40:26expected result so in this case year
6:40:29three at 2% s uh 2% increase in arrays
6:40:33it's going to get around
6:40:35116,000 we also do for coloring at the
6:40:37end uh the data tables don't do that
6:40:39that's done with conditional formatting
6:40:41so here we are back in the original
6:40:42sheet first thing we need to do is get
6:40:44the annual Rays put up here remember we
6:40:48want to go in we'll say. 5% increment so
6:40:51I'll do zero
6:40:540.5% and then for the rest of these I'll
6:40:57just drag them down I end up messing up
6:40:59the formatting so I'm just clear the
6:41:01borders and then put a border back
6:41:04around the outside now for the salaries
6:41:07I want that to be for what year zero
6:41:10then year 1 I'll drag this on over for
6:41:13these and then we'll put a total so for
6:41:15this I want to enter in that year zero
6:41:17we're going to be doing this for all the
6:41:18different values right there I'm going
6:41:20go ahead and put it in if you notice it
6:41:22has this line through it and I actually
6:41:24click it and then from here whenever I
6:41:27look into it it provides the error of
6:41:28stale value you may or may not see this
6:41:32but I'm going I show you how to fix this
6:41:34if you are experienced this you can go
6:41:36into file and then into more under
6:41:39options and what happens is under
6:41:42formulas my workbook calculations went
6:41:45from basically automatically calculating
6:41:47to manually where under manually if I
6:41:50look at this little icon right here they
6:41:52can be manually calculated by pressing
6:41:55F9 or going to formulas calculate now
6:41:58anyway there's nothing wrong with having
6:41:59automatic calculations that's actually
6:42:01what I want all the time somehow my
6:42:02thing switched into this manual if yours
6:42:04does switch it back to automatic click
6:42:07okay bam we're good to go and we'll
6:42:09continue on now it's important for up
6:42:12here at the top that we have them equal
6:42:15to the formulas here because this is
6:42:18what's going to be ultimately getting
6:42:20changed and manipulated so I wouldn't
6:42:23want to go through and actually manually
6:42:24fill this in with a 110,000 it needs to
6:42:26be connected to the formula that
6:42:28actually is getting calculated so
6:42:30building our data table now I'm going to
6:42:32select this entire range right here E3
6:42:36all the way down to K12 go to the data
6:42:38Tab and select data table now this
6:42:41provides us two inputs a row input cell
6:42:44and a column input cell we're only doing
6:42:46a one input data table so we only need
6:42:48to fill in one of these specifically
6:42:51we're looking for the input either into
6:42:53the row or the input into the column in
6:42:55this case we're going to be subbing in
6:42:57this this column this e column right
6:42:59here we're going to be subbing it into
6:43:01the formula here and it wants to know
6:43:03what is the input cell for in this case
6:43:05the column so I'm going to go ahead and
6:43:07select it it's C5 I'm gonna go ahead and
6:43:10click okay and it's going to
6:43:13automatically fill it in now what's
6:43:15unique about this is I could also go in
6:43:17here if I wanted to and maybe change
6:43:19this to something like 10% and it will
6:43:23update this entire data table with that
6:43:26new value I'm actually going to change
6:43:27that back to 3% but pretty unique anyway
6:43:30if I wanted to I can come in also and
6:43:32I'll so go in and to conditional format
6:43:35it I'm only going to select Euro 0
6:43:37through four and I'm going to do a white
6:43:39to green and then for the total I'm
6:43:41going to do its own because it's almost
6:43:42in its own bracket here right it's a a
6:43:44sum of all those different values so I'm
6:43:46also going to do the same thing of the
6:43:49white to green and then you know me I
6:43:51don't really really like green so I'm
6:43:53going to go ahead and select this and
6:43:54I'm going to end up changing this by
6:43:55going into manage rules and conditional
6:43:57formatting selecting on this one
6:44:00adjusting the color to Blue and also
6:44:02selecting this one and changing this one
6:44:04to Blue as well click apply and then
6:44:07okay and
6:44:11Bam so with that example complete let's
6:44:13move into a two input data table and
6:44:15let's look at the final example for this
6:44:17for this we're going to have as we had
6:44:21before the annual rays in the column but
6:44:24this time we're going to have the bonus
6:44:27up on that top row and for this we're
6:44:30going to be calculating as we click here
6:44:32it's going to be calculating
6:44:33c14 which is the total salary we're not
6:44:36going to be calculating that 0 1 through
6:44:384 anymore and it's going to go through
6:44:40and calculate it for all of these
6:44:43different scenarios if you will all
6:44:45right to do this I'm going to go back to
6:44:47that original sheet I'm going to
6:44:49actually duplicate this by saying copy
6:44:52it create a copy and click okay okay so
6:44:56now we have original two so I'm going to
6:44:58name original to one input and then
6:45:02rename or two to two input now for this
6:45:05one I'm going to end up just clearing
6:45:07the contents from here I'll go to
6:45:09editing clear and I'll just select clear
6:45:12contents and now thinking about it I
6:45:14want to also clear any of the formatting
6:45:16that's in here cuz we're going to be
6:45:17doing something different with it I can
6:45:19go into clear rules I can go clear rules
6:45:22from entire sheet all right so we're
6:45:24have the Rays and the rows and now we
6:45:27need the actual bonus in the columns for
6:45:30this we'll go from 0% to
6:45:335% and I need to actually change this
6:45:36formatting to actually be a percentage
6:45:38and then drag this all the way through
6:45:40along with fixing this formatting so now
6:45:43a two input data table is a little bit
6:45:46different in that we need in the upper
6:45:48left hand corner what we actually want
6:45:50to change whereas the one input put we
6:45:52did across in in our case we did across
6:45:55the rows in this case we just want to
6:45:57have in the upper left hand corner there
6:45:59it is I sort of grayed it out you can
6:46:01make it a little bit darker if if you
6:46:03want to but I would just want to make it
6:46:05known that hey we're not necessarily
6:46:06using it so similarly we're actually
6:46:08going to get into creating it we're
6:46:09going to select the entire data table go
6:46:11to the data tab what if analysis data
6:46:13table for the row input cell so this row
6:46:17up here what are we wanting to
6:46:19substitute these values into well we
6:46:21want to sub it into the bonus and then
6:46:25similarly for the column input same as
6:46:27last time that's the annual raise so
6:46:29we're going to want to sub that into C5
6:46:32going go ahead and click okay so I'm
6:46:34going to dress this up a little bit I'm
6:46:36going to bold the header right here also
6:46:38I'm going to merge and center this all
6:46:40so we can put inside of here bonus and
6:46:43then finally I'm going to conditionally
6:46:45format it like we did last time using
6:46:47that white to green and then changing
6:46:50that green to a blue to get it more of
6:46:53what I want so bam now we have a two
6:46:56input table and we can see what it's
6:46:59going to be across all these things also
6:47:01with this if you remember from our last
6:47:03lesson right we were looking at finding
6:47:05what is the value we'd want to be to get
6:47:07around 640,000
6:47:10now we have a few different values we
6:47:13can actually look at for this and we can
6:47:16tell from this well we going to need to
6:47:18be above a bonus rate of 15% to even be
6:47:20considered to get up to 640,000 so
6:47:24sometimes I like this visually better
6:47:26than going in and doing something like
6:47:28goal Seeker or even things like solver
6:47:30because now I have multiple different
6:47:33variables I can look at and analyze and
6:47:35try to adjust on my own all right so you
6:47:38now have some practice problems to go
6:47:39through and get familiar with data
6:47:41tables I found when I first started with
6:47:44data tables got really confused on the
6:47:46row input and also the column input
6:47:49cells but really understanding how those
6:47:51are being applied into the original
6:47:53formula helps you figure that out all
6:47:55right with that I'll see in the next one
6:47:57where we're going going into the
6:47:58analysis tool pack and diving into a lot
6:48:01of different statistical analysis you
6:48:02can do with Excel so with that see you
6:48:08there all right this is the last lesson
6:48:10in this chapter on Advanced ad analysis
6:48:13and specifically we're going to be
6:48:14focusing on that analysis tool pack
6:48:16addin now this addin is packed full of
6:48:19features and I can make a whole tutorial
6:48:21just on this addin alone but we're only
6:48:23going to be focusing on four core things
6:48:25of it that it does that I use from time
6:48:27to time on our job posting salary data
6:48:30set of over 30,000 rows first we're
6:48:33going to look at how we can get
6:48:34descriptive statistics of something like
6:48:36a salary column so we don't have to go
6:48:38through and use formulas to get all the
6:48:39different statistics for it second we're
6:48:41going to investigate how to make
6:48:43histograms but these are a little bit
6:48:45with a Twist in that I feel like they're
6:48:47more customizable than the previous
6:48:49histograms we can make third we'll get
6:48:51into ranking and assigning a percentile
6:48:55for our salary data so we can understand
6:48:57where it actually ranks for percentiles
6:49:00and then finally we're going to be
6:49:01moving into looking at at a moving
6:49:03average if you remember our job posting
6:49:05data set had all the seasonality in it
6:49:07basically went up and down a lot
6:49:08depending on where it was posted during
6:49:10the week well we can remove those
6:49:12fluctuations by a moving average for
6:49:15this we're going to be working in the
6:49:16analysis tool pack workbook and all the
6:49:19answers in there so you can feel free to
6:49:21go ahead and actually select all the
6:49:24different sheets in here and go ahead
6:49:26and hide them so we only have the data
6:49:28tab in there and we'll be working with
6:49:30this
6:49:34so as a refresher this is the data
6:49:36analysis tool pack you should have gone
6:49:38through in that first lesson and
6:49:40actually enabled it by going into
6:49:43options into the addins itself and it
6:49:45should now be under the active addins if
6:49:48you didn't do that remember you all you
6:49:50have to do is just go into go into here
6:49:52and select it all right so let's open
6:49:54this bad boy up and if I click that
6:49:56analysis it's going to pop up here and
6:50:00this dialogue box allows us to select
6:50:03like I said from a variety of different
6:50:05tests that we can actually perform
6:50:07there's a lot of different statistical
6:50:09tests in here such as regression and
6:50:11sampling and then even things like
6:50:14correlation Co variance and whatnot so
6:50:16let's start with the one that I find
6:50:17myself using the most and that's
6:50:19descriptive statistics when I want to
6:50:20perform Eda or exploratory analysis this
6:50:22is the first thing I want to do now the
6:50:24thing about this is we need to provide a
6:50:26column that has numerical values in it
6:50:29so we could do the date column but what
6:50:31we're going to do is we're going to to
6:50:32provide the salary year average column
6:50:36go ahead and press enter for this we do
6:50:38have labels in the first row so I need
6:50:40to click this here for output options we
6:50:42want to go to a new worksheet so that's
6:50:45what we'll leave for this and with this
6:50:48we do want the summary statistics you
6:50:51can go in and also specify things like
6:50:53confidence level and the cith largest
6:50:56and kith smth but we're going to leave
6:50:57those default for the time being and
6:50:59click okay now it's popped up in this
6:51:01new sheet called cheap one and diving
6:51:04into it I'm actually going to expand
6:51:06this out and then format all these
6:51:08numbers real quick so that's much more
6:51:10readable so now we have all the key
6:51:12statistics from it we don't have to go
6:51:14through and calculate a formula for mean
6:51:16median mode standard deviation the
6:51:19minimum maximum sum
6:51:23whatnot all right next up is histogram
6:51:26and previously remember we could just
6:51:28select something like the M column go
6:51:29into insert here and actually insert a
6:51:32histogram now the one problem I have
6:51:35with this is the formatting of the rows
6:51:38or the X values down here it basically
6:51:41provides this range this is a lot of
6:51:43data right there and there's it's really
6:51:44hard to format this so let's look at an
6:51:47alternate option for this using the data
6:51:49analysis tool pack specifically we're to
6:51:52come in here to histogram for the input
6:51:54range once again I'm going to go ahead
6:51:56and just select that column M press
6:51:58enter it does have labels for bin range
6:52:01I'm going to leave m I'm not going to
6:52:03specify a width of the histogram or the
6:52:05bin I'm going to leave it just default
6:52:08for the output I'm going to leave it as
6:52:09the new worksheet ply I don't want
6:52:11either of these the parto or the
6:52:13cumulative percentage instead I just
6:52:15want the chart output of this press okay
6:52:18and here we have the histogram it's
6:52:20honestly not too special it's a little
6:52:22hard to read based on the size of these
6:52:25bins as you can see basically the
6:52:27difference between these is around it
6:52:30looks like they're doing basically an
6:52:31thousand increments so the increments
6:52:33are way too small we need to adjust the
6:52:35bin anyway the one good thing is along
6:52:38this xais it's only one value now so a
6:52:41lot easier to read so now let's go in
6:52:44and adjust that bin size so if I go back
6:52:47to data analysis into histogram and
6:52:49click okay for the bin range it wants me
6:52:51to actually put in a range or a
6:52:54selection so we need to actually
6:52:56pre-fill out what range or bins we want
6:52:59for this so I'm going to copy this
6:53:00header up here cuz we're going to keep
6:53:01the bin in frequency start a new sheet
6:53:05paste it in here and I want to go in
6:53:08we'll say 50,000 increments so 0
6:53:1150,000 and I want it to go to basically
6:53:16400,000 so now going into Data analysis
6:53:19again histogram opening it back up still
6:53:21has the input range selected correctly
6:53:23now for the bin range I'll select A2 to
6:53:27A10 select the output range to I
6:53:30basically want it to be inside of of
6:53:32this notebook so I'm going to select up
6:53:34here on D1 we'll just start there and we
6:53:37want a chart output on this page okay
6:53:39I'll click okay and I'm getting this
6:53:41error message that the input range must
6:53:42contain at least one data point right
6:53:44now this Elm is not referring back to
6:53:47the correct sheet it needs to look at so
6:53:49actually I'm going to select right here
6:53:51you can see it selected that other sheet
6:53:52I actually want to select the M column
6:53:54of the data tab now we'll press okay so
6:53:58now I love this because wanted output
6:54:01this I didn't apparently need to do this
6:54:03frequency thing I got confused anyway we
6:54:06can actually go in and format this to
6:54:09remove the legend and then update the
6:54:12axess title for salary and then we'll
6:54:15update this one for frequency anyway I
6:54:17really like this because now look at
6:54:19this control we were able to minimize it
6:54:23not to go past 40,000 and have all these
6:54:26outliers and everything else that has
6:54:28past 40,000 is put into this basically
6:54:31more value you anyway this is my
6:54:33preferred method for making histograms
6:54:35especially whenever I need to control
6:54:37that
6:54:40xais next up is Rank and percentile and
6:54:43with this one we're going to be doing a
6:54:44rank and percentile of that salary year
6:54:46average column once again now this one
6:54:49depending on the size of your computer
6:54:51may take up it may even crash your
6:54:53computer so if you're concerned that
6:54:56this is not going to be able to
6:54:58performed on your computer don't run it
6:55:00just look at my example and understand
6:55:02what get out of it anyway I selected
6:55:03rank in percentile and then for the
6:55:05input range once again I'll select that
6:55:08column M and then we'll output it to a
6:55:10new worksheet ply and I can do something
6:55:12like even name it in this case calling
6:55:15it Rank and percentile of the sheet that
6:55:16it's going to go to so clicking okay it
6:55:19says Rank and percentile input range
6:55:20contains non-numeric data basically I
6:55:24forgot to click this of labels in the
6:55:25first row clicking again it's thinking
6:55:28how long is it going to take all right
6:55:31so Excel just on me maybe that wasn't a
6:55:33great idea let's try that again using
6:55:35Rank and percentile this time instead of
6:55:38selecting the whole column I think
6:55:40because it had some blank value
6:55:41especially down to a million rows sort
6:55:43of crashed it instead what I'm going to
6:55:45do is I'm going to just select A1 and
6:55:47then select down all the way to the
6:55:49bottom I don't know why it changed it
6:55:51over to column F but the main point of
6:55:52me to doing this is that way we select
6:55:55column M and also I need to remove this
6:55:59A1 at the beginning okay and also need
6:56:01to update this to be starting the second
6:56:03cell and we're going to try this again I
6:56:05gave it the name of rank percentile I
6:56:07didn't have the labels in first row
6:56:08selected because we're going from the
6:56:09second cell how long is it going to take
6:56:11this time all right so that was a lot
6:56:14quicker this time and we have our now in
6:56:16this Rank and percentile sheet our
6:56:18actual data it did take about a minute
6:56:21to do so once again if you have a
6:56:22computer that's not necessarily that
6:56:24fast don't try this at home all right so
6:56:27some key statistics about this it
6:56:29provides a point which is the row number
6:56:32it's itself and then from there what is
6:56:34the value that's the column the rank and
6:56:37then the percentile what's cool about
6:56:39this because of provided point we could
6:56:41do something like the index function and
6:56:44you provided an array and then the row
6:56:46number in this case that's the row
6:56:47number so if I wanted to find out what
6:56:50the job title is I could select column B
6:56:54and then from there for the row number
6:56:56go back to rank and percentile and
6:56:58select this value right here then close
6:57:01parenthesis press enter looks like it's
6:57:03a clinical NLP data scientist and I can
6:57:06actually autofill this all the way down
6:57:09anyway let's make sure this is actually
6:57:11correct okay yeah just double checking
6:57:13the row number at 25589 is clinical NLP
6:57:17data scientist so we have it correct
6:57:19anyway I could go through now and I did
6:57:21this for the job title itself but you
6:57:23could imagine you could pull out things
6:57:25like the job country job tile short all
6:57:27sorts of other key information and get
6:57:29this in a list of what it's rank is
6:57:32along with its
6:57:36percentile our last feature to look at
6:57:38is moving average and this is what we're
6:57:41going to be calculating here the Blue
6:57:43Line already is data we already have of
6:57:46what are the job postings over time but
6:57:48that orange line is the moving average
6:57:51we can use this analysis tool pack in
6:57:53order to calculate this and as you can
6:57:55see it removes a lot of these fluctu
6:57:57these weekly fluctuations if you will
6:58:00from it and makes it a lot more are
6:58:02basically readable to see where actual
6:58:04the Peaks and the troughs are now in
6:58:06order to do this I can't necessarily
6:58:08just put in that job posted date into it
6:58:11I have to actually get a count of the
6:58:15dates and also what are the counts of
6:58:17the job postings per date so we need to
6:58:19create a pivot table so we go in insert
6:58:21pivot table from table we're going to do
6:58:23it from this table which is named jobs
6:58:25and we're going to insert it into a new
6:58:27sheet similar before we're going to put
6:58:29that job posted date into the rows and
6:58:32I'm actually going to take out you can
6:58:33see it aggregated by month I'm going to
6:58:35take out the month from there so it does
6:58:38by days and now I'm going to throw into
6:58:41the values here it's going to do a count
6:58:43so I'm just change this to job count and
6:58:48we can actually visualize this by itself
6:58:50by going to insert pivot charts
6:58:51inserting in a pivot chart we want a
6:58:55line and that's what we saw before with
6:58:58our Blue Line before that showed how it
6:59:00basically went across uh went through
6:59:01time
6:59:02all right so goes ahead and I'm going to
6:59:04delete this chart because we're going to
6:59:05be making it and once again we're going
6:59:07to that data tab into Data analysis and
6:59:10we're going to be forming moving average
6:59:13for the input range I'm going to select
6:59:15B4 and then select all the way to the
6:59:18Bottom now this grand total went into it
6:59:21so actually I'm going to back up one and
6:59:23change this to 368 we didn't select any
6:59:26labels in the front row so I'm going to
6:59:28leave that on blank in the interval I'm
6:59:30going to just set it something like
6:59:32seven for the time being for the output
6:59:35range I want it to go right next to my
6:59:36chart so I'm going to copy this above
6:59:39and paste it below and change these B's
6:59:41into C's so it's C values right next to
6:59:45it and we want a chart output along with
6:59:48standard errors I'm going to go ahead
6:59:49and click okay now this chart is not
6:59:52correct um we made a little bit of a
6:59:54mistake but I did want to show you real
6:59:56quick this moving average we can see
6:59:58that it starts 7 days later right here
7:00:02and so that's what's happening in this C
7:00:04column here that's the actual moving
7:00:06average and then the actual error itself
7:00:08is right next to it it's pretty
7:00:10consistent around 30 to 40 anyway we
7:00:12need to fix this we need to take this
7:00:13entire value if you will and move it out
7:00:16of a pivot chart so I'm going to select
7:00:18this all the way down to the bottom and
7:00:21copy it then inside of a new sheet I'm
7:00:23going to come in and paste it I'm going
7:00:25to just paste looks like a pasting with
7:00:27the pivot table formatting I'm going to
7:00:28paste uh the values only and change this
7:00:32to job date so let's try this again
7:00:34using data analysis going to moving
7:00:36average for the input range we're going
7:00:39to select B2 and then all the way down
7:00:41to the bottom remember this has a grand
7:00:43total so I actually need to change that
7:00:45to minus one for the interval I'm going
7:00:48to adjust it a little bit I'm going to
7:00:49actually change this now to a 21-day
7:00:52moving average and then for the output
7:00:53range this actually needs to be adjusted
7:00:55to match what the input range is but for
7:00:59b or c sorry anyway go go ahead leave
7:01:02everything else checked click okay and
7:01:05Bam now we have blue and also orange if
7:01:09you will for the actual and the forecast
7:01:13now one thing I'm noticing with this
7:01:14chart is well the markers are pretty
7:01:18heinous they're making they're clogging
7:01:20up this chart so what I can do is Select
7:01:22something like this orange line right
7:01:23here I can rightclick it go to format
7:01:26data series and then here underneath
7:01:28this fill and line go into markers and
7:01:32then for the marker options just
7:01:34basically do none we just want to have a
7:01:36line instead additionally we can just go
7:01:38ahead and click that blue the blue line
7:01:41and for the markers there we can do none
7:01:43as well okay sensory overloads gone now
7:01:46looks a lot more readable with the
7:01:49exception of down here for some reason
7:01:51it didn't pick up the dates on mine and
7:01:54we can adjust that by right clicking
7:01:57that and going to select data underneath
7:02:00the horizontal ax labels I'm going to go
7:02:02ahead and edit this I'm going like from
7:02:05A2 all the way down minus one we don't
7:02:08to do grand total click okay that
7:02:11changed the names let's see if that
7:02:12updated the chart and Bam it did now I'm
7:02:15going to do some minor cleanup I'm going
7:02:17to remove that Legend from there and
7:02:21that looks a lot better so now we have a
7:02:23graph of our moving average of the job
7:02:26postings and as we sort of suspected in
7:02:30August we had a peak along with January
7:02:32seemed sort of high then went down a
7:02:34little bit but then up again in August
7:02:36so we see a lot more Trends and then
7:02:37tapering out towards the end of the year
7:02:40all right now it's your turn to go
7:02:41through and practice with those practice
7:02:43problems and exploring some of these
7:02:45features in the analysis tool pack add
7:02:47in with that we're going to be wrapping
7:02:50up this chapter and in the next one
7:02:52we're be jumping into Power query which
7:02:55I'm super excited about in order how to
7:02:56clean up our data and load it in in the
7:02:59format that we want easily all right
7:03:01with that see you
7:03:06there welcome to this chapter on power
7:03:08query and no pun intended but this is
7:03:11one of the most powerful tools within
7:03:14Excel it allows us to perform ETL
7:03:17processes or extract transform and load
7:03:21which just some fancy data engineering
7:03:23talk for connecting to a data source and
7:03:25loading it in after you clean it up
7:03:27anyway in this chapter we have five
7:03:30lessons specifically in this one we're
7:03:31going to have an intro to power query
7:03:33what it's all about how to actually
7:03:35connect to a data source in the next one
7:03:37we'll be moving into the power query
7:03:39editor and we'll be covering that for
7:03:42three lessons in order to go in how to
7:03:44actually clean up your data and get it
7:03:46prepared to a format that you want in
7:03:49the last lesson we'll be diving into the
7:03:51M language which is powering power query
7:03:55don't worry we're not going to do any
7:03:56in-depth coding or anything like that
7:03:58just want you to have some familiarity
7:03:59with you so we have more experience with
7:04:01using power
7:04:05query so what's this lesson about well
7:04:08in order to understand that we have to
7:04:10understand is what is power query and
7:04:13here on Microsoft's learning platform
7:04:16they have this fancy Dancy diagram that
7:04:17basically shows this what power query
7:04:20does it allows us to connect to
7:04:22different data sources it could be
7:04:23something like a database a text file or
7:04:26even something on the cloud from there
7:04:29power query will then pipe it in to a
7:04:32bunch of different products they have
7:04:33and we're going to be using it for
7:04:34Microsoft Excel but it's also famously
7:04:37also in powerbi now if you have a
7:04:39Windows version of excel power query is
7:04:41going to work just fine on the Mac
7:04:43versions it is available however it's
7:04:47very limited so a lot of the stuff we're
7:04:49going to do within this lesson you're
7:04:51not going to be able to do and also
7:04:53Microsoft online is just completely not
7:04:55available so as a reminder power query
7:04:57is an ETL tool or extract transform load
7:05:01and we can connect to as a data source
7:05:03such as this here's a Wikipedia page on
7:05:05the list of S&P 500 companies and it has
7:05:09all the different 500 companies that are
7:05:11part of the S&P 500 anyway let's say I
7:05:14want this table I could go through and
7:05:17try I mean as you can see I'm trying to
7:05:18select it right now and it's like
7:05:19selecting the whole page it's a whole
7:05:22mess if I'm trying to get this but we
7:05:24can actually use power query to extract
7:05:26all this components out all I have to do
7:05:29is go in and provide the web address of
7:05:32this which I know it's located right
7:05:34here I'll then select which of the
7:05:36tables I want out of the web page which
7:05:38is this one right here and then I just
7:05:40load it in and here it is in our
7:05:43workbook now don't worry I sort of ran
7:05:45through that example real quick we're
7:05:46going to go more in depth and Detail in
7:05:48the last example in this lesson but I
7:05:51just wanted to show the power of this
7:05:53and how we can actually get data even
7:05:55from online into our workbook so easily
7:05:58so why do we need to use power query
7:06:00well we're going to find that out as we
7:06:02go along but I'm going to give you the
7:06:04tidbits right now of One it automates
7:06:07the ETL process so I don't have to do
7:06:09that annoying task of going to a sheet
7:06:12and copying it over every time I get new
7:06:14data I can just get power query to do it
7:06:16for me additionally with that sometimes
7:06:18I may have mistakes I'm copy and paste
7:06:20and sheets over therefore I have
7:06:23reproducibility and then finally with
7:06:25this I'm now allowed to bring data in
7:06:28that potentially exceeds that 1 million
7:06:30row limit of Excel which we'll show how
7:06:33we can deal with that in a bit so let's
7:06:35actually get into performing our first
7:06:36example of loading in a simple data set
7:06:39specifically from another Excel sheet
7:06:41like I talked about the beginning of the
7:06:42advanced chapters you're not going to be
7:06:44able to actually work inside of the
7:06:46workbooks that I have given so in this
7:06:47case power query intro has the final
7:06:50results but I don't want you working in
7:06:52that I'll tell you what works you need
7:06:54to be working with as we go through this
7:06:56which you're probably getting the
7:06:56security warning of external data
7:06:58connections have been disabled and we'll
7:07:00get to troubleshoot shooting that at the
7:07:02end so instead we're going to be
7:07:03starting with a new blank workbook I'm
7:07:06going to go to navigate over here to the
7:07:07data tab this is where power query is
7:07:10located specifically under this get and
7:07:13transform data it doesn't really say
7:07:15power query but that's where power query
7:07:17is hidden now anytime I'm importing any
7:07:19data I typically go to this get data and
7:07:22then from there I navigate Down Deeper
7:07:25depending on it's file database from
7:07:27fabric and power platforms or from even
7:07:30other sources they do have for all these
7:07:32for it here they also have smaller icons
7:07:35right next to it that you can navigate
7:07:37over and basically highlight okay this
7:07:39is from web and then this is from a
7:07:41table of range and whatnot we're going
7:07:43to be going over multiple examples in
7:07:45this video so don't worry if you're not
7:07:47following along with which data sources
7:07:48you can actually import I think you'll
7:07:50have a good idea by the end of
7:07:54this so what are we going to import
7:07:56first well if you navigate into our
7:07:59course folder under resources under dat
7:08:01ass sets and then data jobs monthly we
7:08:04have Excel files for every single month
7:08:08we're going to start by just importing
7:08:10one Excel file to start and then in the
7:08:12next exercise we'll go into how to
7:08:14import all these at once anyway we're
7:08:15going to start simple first with just
7:08:17this Excel file so for this I'm going to
7:08:19go to get data and it's a file
7:08:22specifically it's from an Excel workbook
7:08:25inside the course folder I'm going to
7:08:27then navigate to the data set going to
7:08:29resources data sets monthly and then
7:08:31select that January data set and click
7:08:33import with power query you're going to
7:08:35find that it has this Navigator window
7:08:37pop up and from there it will show you
7:08:40what is actually importing in in this
7:08:42case January data jobs the Excel sheet
7:08:45and then if it had one or multiple
7:08:47sheets it will appear there underneath
7:08:49it whenever I select sheet one it then
7:08:51shows me to the right hand side a
7:08:53snapshot or a preview of all the
7:08:56different data in there it doesn't show
7:08:58all the columns but a snapshot of it at
7:09:01the bottom there's a few options to load
7:09:04or load to and then also transform we're
7:09:08going to keep it simple for the time
7:09:10being and we're just going to load so
7:09:13I'll go ahead and click it so we just
7:09:15imported in this data set from another
7:09:17worksh sheet it's already in its own
7:09:20table and because it also was sheet one
7:09:24it's naming the sheet sheet one
7:09:26parenthesis 2 to signify as the second
7:09:29one so congratulations we just completed
7:09:31our first ETL process of actually
7:09:34extracting transforming and loading an
7:09:36Excel workbook into another
7:09:41workbook so we loaded this table in but
7:09:44how do we actually go about using it
7:09:47well in this portion we're going to be
7:09:48demonstrating how we can manipulate it
7:09:49with a pivot table and how to basically
7:09:52control all our different queries if you
7:09:54notice we had over on the right hand
7:09:56pane this queries and connections now if
7:09:59it's not popping up you can go up here
7:10:01to the data Tab and then you see queries
7:10:04and connections you can navigate it on
7:10:06and off by clicking this button power
7:10:09query sets up these queries and in this
7:10:11case it named it sheet one after the
7:10:13sheet one in that workbook that we
7:10:15exported in I'm sorry that we imported
7:10:17in and if we hover over it we can get
7:10:19some details about the columns when it
7:10:22was last refreshed it's load status and
7:10:24even data source now connections over
7:10:28here on the right right now we have zero
7:10:29connections that's actually what's
7:10:31controlled by power pivot which we're
7:10:33going to be going over in the next
7:10:35chapter on power pivot but anyway back
7:10:38to Power queries itself right now we see
7:10:40with sheet one that 3,000 rows are
7:10:43loaded and if necessary we go through
7:10:46and refresh the data set as showing it
7:10:48loaded the data and 3,000 rows are
7:10:50loaded again pretty quick so let's
7:10:52actually get into manipulating this well
7:10:54it says that 3,000 rows are loaded but I
7:10:56actually I can go in and delete this tab
7:10:59and it's going to give you this warming
7:11:00that's going to per delete the sheet do
7:11:02you want to continue yes I do and
7:11:04whenever I do that since the data is no
7:11:06longer loaded it now displays that it's
7:11:09connection only so we can actually
7:11:11change where we load our data to if you
7:11:15will and I can get to this by right
7:11:18clicking it and then going into here and
7:11:20we'll be exploring all these other
7:11:22options as we go through but I'm only
7:11:24want to focus right now on this load to
7:11:27and they have a few different options in
7:11:28here let's actually explore them right
7:11:30now it has only create connection so
7:11:32right now it only has a connection if we
7:11:34go back to that table and click okay it
7:11:37once again loads it into that table if
7:11:40we want to actually get into a pivot
7:11:42table we'll select this on pivot table
7:11:44report we can also do a pivot chart and
7:11:46it asks whether we want to put it in the
7:11:48existing worksheet or a new worksheet
7:11:50and then finally it has ADD this data to
7:11:53the data model you've seen this one
7:11:55before and once again we're going to be
7:11:58going over data models more in depth in
7:12:00chapter eight on power pivot so we're
7:12:02not going to be enabling this checkbox
7:12:05just yet anyway I went in the existing
7:12:07worksheet I don't need that table there
7:12:09so I'm going to click okay and it says
7:12:10hey there's possible data loss because
7:12:12we're going to be basically getting rid
7:12:13of that table and replacing it with a
7:12:15pivot table do I want to continue yeah
7:12:18and now like we did before in the pivot
7:12:19table chapter we're now using a pivot
7:12:22table and so we can put things like job
7:12:24title short and analyze it for the count
7:12:26of different jobs that it has within it
7:12:28there's no change whatsoever in
7:12:29everything we learn in pivot tables
7:12:31still same application that we're using
7:12:33it here
7:12:36for so now let's actually get into
7:12:39importing multiple different Excel files
7:12:41we're going to specifically be importing
7:12:43all 12 of these of January through
7:12:46December this time whenever I go into
7:12:48the data tab under get data and we want
7:12:50to get it from a file but I'm not going
7:12:53to select an Excel workbook instead what
7:12:55I'm going to do is select a folder
7:12:57because all those Excel files are in the
7:13:00same folder inside my course I'll
7:13:02navigate into resources data sets and
7:13:05then I'm going to select the folder
7:13:06itself and select open now you may
7:13:08notice the Navigator window looks a
7:13:10little bit different and that's because
7:13:12now it contains the metadata of these
7:13:15Excel files itself such as the name data
7:13:18access modified created and whatnot and
7:13:20with this one before we had that load
7:13:22and load to along with transform data
7:13:25we're just going to go into combining
7:13:27this data set so I'm going to go ahead
7:13:28and click that and specifically we're
7:13:30going to use combine and load now we
7:13:33navigate to a window we're more familiar
7:13:35with of combined files and what this is
7:13:38doing is showing is how it's going to
7:13:40actually combine the files in that we
7:13:43need to make sure one that they're all
7:13:44the same format but if I actually click
7:13:47sheet one of which the sample file is
7:13:50looking at is the first file this is
7:13:52what it looks like and we know this
7:13:54already because we looked at the January
7:13:55file anyway if you wanted to you could
7:13:57also change this to a specific file I'm
7:14:00fine with just using first file
7:14:01selecting a sheet if I was having errors
7:14:04I would do skip files with errors but
7:14:05I'm not worried about that just yet I'm
7:14:07going go ahead and click okay and Bam
7:14:09now we have that once again that table
7:14:11loaded into here and this has all the
7:14:13data so I expect it to have around
7:14:1530,000 results similar to what we've
7:14:18been working with before and it looks
7:14:20like it does and if you notice we have
7:14:22this new column right here on Source
7:14:24name which tells which Excel file each
7:14:27of these comes through and just doing a
7:14:29cursory check it looks like all the
7:14:30different months are in there now onto
7:14:33this queries and connections paint up
7:14:35here I'm going to actually make this
7:14:36smaller so I can actually see it all
7:14:38previously we only had our sheet one
7:14:40query but now we have also this data
7:14:43jobs monthly query and with that up here
7:14:47at the top because we're connecting
7:14:50multiple different files we have these
7:14:53helper queries that were created during
7:14:55the process so you can navigate over
7:14:57these and basically see that hey it used
7:15:00the September file as a sample and this
7:15:02is the steps it took or this is what the
7:15:04sample file actually looks like anyway
7:15:07I'm not too concerned with those helper
7:15:08queries right there or with anything
7:15:10underneath this transform from files I
7:15:12mainly care about what's under those
7:15:14other queries so we have sheet one and
7:15:16data jobs monthly speaking of which
7:15:18sheet one is a really bad name for this
7:15:20so I'm going to rename this to data jobs
7:15:23January I also rename the sheet so just
7:15:25to prove with the data jobs monthly that
7:15:27we actually imported it all in we're
7:15:30going to go in and load to and we're
7:15:32going to do this time a pivot chart
7:15:35going to go ahead and click okay we're
7:15:37doing the existing sheet with the table
7:15:39I don't care if I get rid of that table
7:15:40so I'll click okay and similar four I'm
7:15:42going to put that job title short this
7:15:44we're going to put in the Axis or the
7:15:45rows and then we're going to want a
7:15:47count of that as well and then I'll just
7:15:49organize this in descending order based
7:15:52on the count of job title short so bam
7:15:55we now connected with power query to
7:15:58multiple Excel files and imported in at
7:16:01once I hope you realize that now this
7:16:05unlocks a lot of potentials because say
7:16:07you get January of next year's data you
7:16:10could just put it into this folder here
7:16:13and then just all you need to do is go
7:16:15back into the data tab click refresh
7:16:18it's going to go through and refresh all
7:16:20that data set and pull those new numbers
7:16:25in all right in this example you're not
7:16:28going to follow along I'm just want to
7:16:29show the power a power query okay the
7:16:32pun's getting old by now anyway I have
7:16:34this CSV pile or comma separated values
7:16:38basically it has comma separating
7:16:40everything I looked at this is in VSS
7:16:42code don't worry about any of this stuff
7:16:43like I said you're not doing it the main
7:16:45point is to show this data set itself
7:16:47right here it's starting at the top row
7:16:48of one and if I scroll all the way down
7:16:51we get to the last entry and that's
7:16:552.7 million jobs that I have here in
7:16:59this data set we can actually import
7:17:02this into Excel now if you recall if you
7:17:04scroll all the way down to the bottom of
7:17:06excel it only includes about 1 million
7:17:09rows so how the heck are we going to do
7:17:11this with power query so this is a CSV
7:17:13file I'm going to go to data get data
7:17:15from file specifically it's a text or
7:17:17CSV and I'm going to import in this data
7:17:19jobs large file that I have reminder
7:17:21again you don't have access to this file
7:17:23it's just too big to even get onto
7:17:24GitHub so that's why this is a demo only
7:17:27this is the data set itself so I'm going
7:17:29to go in and actually go and look load
7:17:31it now this has taken a little bit of
7:17:33time as you can see it's loading around
7:17:35100,000 rows as it goes through also it
7:17:38had well it has three errors now in here
7:17:41this usually appears whenever it has a
7:17:43row of data that doesn't necessarily
7:17:45make sense for what it's supposed to
7:17:46import it alert you there's an error so
7:17:48the 2.7 million rows are loaded but I
7:17:50get this error message the query
7:17:52returned more data that will fit on a
7:17:53worksheet remember it automatically by
7:17:55default tries to load it into a table
7:17:58into Excel and it's telling me that hey
7:18:00it's not going to fit so I'll click okay
7:18:03now it's still going to try to load that
7:18:04table but it's going to cut it off at
7:18:07that 1.5 million but this doesn't mean
7:18:10we can't analyze it if I scroll over
7:18:13this query it reminds me that the
7:18:14results of this query is too large to be
7:18:16loaded to the specified location
7:18:17worksheets have a limit of 1 million
7:18:19rows sure instead what I'm going to do
7:18:21is go and load to and I'm going to load
7:18:23to a pivot table click okay and it's
7:18:26going to warn me again about the table
7:18:28loss yeah I know so once it loaded like
7:18:30a hot minute to do that I can actually
7:18:32go through and now analyze these 2.7
7:18:35million rows so if I do something like
7:18:37put the job posted date into the rows
7:18:40and we also want and we want to get a
7:18:42count of this so I'm going to put the
7:18:43job poster date also into the values so
7:18:45we get this counts anyway reformatting
7:18:47it with commas to actually be able to
7:18:48read this now we can see that we did
7:18:52actually get in 2.7 million different
7:18:54data points for this and as a side note
7:18:57this is all the data that I've collected
7:18:59since I started in 2022 doing this so
7:19:03there's a lot of different jobs so Excel
7:19:05is not necessarily limited to just
7:19:07analyzing 1 million rows of
7:19:12data all right so let's finally get into
7:19:14that last example of importing in this
7:19:16list of S&P 500 companies feel free you
7:19:19don't necessarily have to do this table
7:19:20from Wikipedia but I'll drop a link
7:19:23below on where this table is located and
7:19:25you can use that if you want so I copied
7:19:27the web page of that table then I'm
7:19:29going to come in here and select like
7:19:30this of from web you can do basic or
7:19:33Advanced with Wikipedia it's perfectly
7:19:35fine to do the basic version putting in
7:19:37that URL clicking okay we get into that
7:19:39Navigator window and there's actually
7:19:42multiple tables inside of here one is
7:19:45the list of 500 companies and the second
7:19:48one is a list of companies that have
7:19:50been added and also removed from there
7:19:53they also just have random tables in
7:19:55there as well just because in the
7:19:56internet you're going to have random
7:19:57tables like this one of main menu
7:19:58contents tools appearances not
7:19:59applicable anyway we want to do table
7:20:02one I'm going to go ahead and click load
7:20:04and now that we have it in here anytime
7:20:06we do this probably need to rename it
7:20:08appropriately from something like table
7:20:10one to S&P 500 in this case and Bam
7:20:13scrolling down we can see that we have
7:20:16um should be 500 oh a little bit more
7:20:18than 500 apparently the list has been
7:20:21updated to clear a little bit more I
7:20:23don't know why that is but got all the
7:20:25DAT
7:20:28nonetheless now quick note on if you
7:20:31want to actually navigate into any of
7:20:33the files and see what I've done
7:20:36whenever you go to open it so in this
7:20:38case I want to open power query intro
7:20:40I'm going to open it up you're going to
7:20:42get this of external data connections
7:20:44have been disabled do you want to enable
7:20:46content in this case yes you want to
7:20:48enable all that now the problem you now
7:20:50may also have is that it may give you a
7:20:53warning that your data source settings
7:20:54aren't correct and what do I mean by
7:20:56that if I go into data and then under
7:20:58get data we're going to see this thing
7:21:00here for data source settings and it's
7:21:04managing settings for your data sources
7:21:06anyway you're going to see these
7:21:07locations here these are file locations
7:21:10of the data sets and they reference the
7:21:12files that are on my computer that's not
7:21:16going to be the same for your computer
7:21:17it's probably going to be in a different
7:21:19location with a different name so here I
7:21:22know that this is the data jobs monthly
7:21:25folder if I wanted to actually go in and
7:21:28update it with the actual location for
7:21:30where it is I would go down here select
7:21:33change source and then from there select
7:21:36browse to navigate to it you're going to
7:21:38once again navigate to your course of
7:21:40excel. analytics into resources data
7:21:43sets and then there's that data jobs
7:21:44monthly click open and okay and then
7:21:48it's going to update you're going to
7:21:50have to do that for this file and all
7:21:52the files within a power query and also
7:21:55power pivot because your file locations
7:21:58are not the same as my file locations
7:22:00then after you do that all it should go
7:22:02through and refresh but if it doesn't
7:22:03you can manually refresh it underneath
7:22:05the data tab by clicking refresh
7:22:09all the last item to call out is the
7:22:12options menu we're going to be going
7:22:13into the query editor in the next video
7:22:16so we're going to save that for that one
7:22:17anyway query option has a lot of
7:22:20advanced details in controlling power
7:22:23query in this case of showing the query
7:22:25Peak when hovering on a query in the
7:22:27query's task pane that's sort of
7:22:29annoying to me it pops up every now and
7:22:31then I'm going to go ahead and unclick
7:22:32it but they also have different
7:22:34behaviors you contr control for data
7:22:35load for the power qu editor the
7:22:37security privacy and even Diagnostics so
7:22:40feel free to go through this and
7:22:41navigate and see what is available to
7:22:43actually customize with this I'm going
7:22:45to go ahead and click my changes of okay
7:22:48and now whenever I go to the queries and
7:22:49connections and actually hover over
7:22:51something like data jobs Jan doesn't
7:22:53just pop up on the screen and sort of
7:22:55catch me off guard so I sort of like
7:22:57that all right we now got some practice
7:23:00problems for you to go through and get
7:23:01more familiar with performing Bally ETL
7:23:04with power query and loading in some
7:23:07different data sources with it with that
7:23:09we'll see you in the next one we're
7:23:11going to get into the power query editor
7:23:13anyway nothing to be intimidated by as a
7:23:15lot of the core principles we've learned
7:23:17already in Excel are going to be applied
7:23:19to this new window so you're going to
7:23:21pick it right up on it all right with
7:23:23that I'll see you in the next
7:23:24[Music]
7:23:28one in this lesson we're going to be
7:23:30continuing on with power query focusing
7:23:32on specifically getting you introduced
7:23:35to this power query editor and in order
7:23:38to facilitate this we're going to be
7:23:40going through or walking through
7:23:42actually importing and cleaning up our
7:23:45data science job posting data set that
7:23:47has over 30,000 rows of data we're going
7:23:50to be automating a lot of the steps
7:23:52using power query that previously we had
7:23:55to use functions and formulas for so
7:23:57it's going to be saving us a lot of
7:23:59times in order to actually automate this
7:24:01data in
7:24:05justest for this we're going to be
7:24:07starting out with a blank workbook so I
7:24:09know we do have this power query editor
7:24:11but I don't want you actually editing
7:24:12from that that's more for a reference
7:24:15now if you do open this file in order to
7:24:17reference it as we go along this
7:24:19remember you're going to have issues or
7:24:21an error saying hey data source isn't
7:24:23there remember you need to go in and
7:24:25actually select where this data set is
7:24:27so under the data tab get data and then
7:24:30under data source settings you're going
7:24:33to need to update this link or this
7:24:36address right here of where you're
7:24:38actually accessing the data job salary
7:24:41all Excel file this is my location not
7:24:44yours got to update it anyway like I
7:24:46said we're not going to be using this so
7:24:47I'm going to open up a new notebook and
7:24:50like before we're going to be importing
7:24:51in that data set so we'll go to get data
7:24:54from file from Excel workbook you'll
7:24:57navigate to the course itself under
7:24:59resource under data sets and then we're
7:25:02going to be using this data jobs salary
7:25:04all Microsoft Excel file go ahead and
7:25:07import this in we're going to select
7:25:09that sheet one and this time instead of
7:25:11doing load or even the load two we're
7:25:14actually going to go into transform data
7:25:17and this is now going to pop open the
7:25:19power query editor and this is where all
7:25:22the magic happens behind the scenes in
7:25:24order to get our data cleaned up so
7:25:27let's go over a quick overview of the
7:25:29window itself it's very similar to laid
7:25:32out to excel up at the top we have a
7:25:34ribbon with four different tabs of Home
7:25:36transform add columns and view we'll be
7:25:38walking through each one of these as we
7:25:40go through this lesson underneath here
7:25:42on the Le hand side we have which query
7:25:44we're selected to once we're building
7:25:46multiple queries they'll start popping
7:25:48up underneath each other we can close
7:25:50this if we want and make more room is
7:25:53right here in the middle is what the
7:25:55current step or what the current status
7:25:58is is of our data set now yours may look
7:26:01a little bit different right now
7:26:03specifically I have this column
7:26:04distribution enabled underneath the view
7:26:06tab which I'm going to go to more in a
7:26:08second but anyway it basically outlines
7:26:10all the different columns or where we're
7:26:11at with the data set itself before we
7:26:14finally loaded in now right above this
7:26:16area is a Formula bar just like similar
7:26:20again to the Excel UI and this has all
7:26:24the steps or all the code the M language
7:26:28done in this current step if you will of
7:26:32actually cleaning up this data set and
7:26:35you're like step like what step well
7:26:37over here on the right hand side we have
7:26:39our query settings and in it we have the
7:26:42name of our query and then we have the
7:26:44applied steps this lists all the
7:26:48different Transformations that we've
7:26:50walked through so just a brief walkr the
7:26:52first step is source and if I look at
7:26:55the formula bar basically what it's
7:26:56doing is it's connecting to that Excel
7:26:59file with the file path that it has in
7:27:02the next step of navigation it's
7:27:04basically selecting hey out of that
7:27:05Excel file actually select sheet one
7:27:08from there to actually load in then from
7:27:10there we can see that the headers are
7:27:13actually in the first row and not up at
7:27:14the top so the next or third step is the
7:27:18promote the headers up to the top and
7:27:20then finally the last step is change
7:27:23type it actually goes through and
7:27:24assigns for each of these what data type
7:27:27it is so in this case job title short it
7:27:30assigns to type text whereas something
7:27:32like job posted date it assigns to type
7:27:35number which needs to be a date which we
7:27:36going to fix that in a little bit down
7:27:38at the bottom there's a few statistics
7:27:39on this specifically talks about 16
7:27:42columns and over 999 rows and it tells
7:27:45you when the last preview is downloaded
7:27:47anyway if I just wanted to stop here
7:27:49with this data transformation if you
7:27:50will I would just come up into home go
7:27:52into close and load we're just going to
7:27:54do close and load two and in this case
7:27:58like I'm just going to put in a pivot
7:27:59table
7:28:00specifically analyzing for job title
7:28:03short specifically how many different
7:28:05counts or that we have of this we can
7:28:07see totaling it all up have around
7:28:1032,000 anyway that's a quick overview
7:28:12let's actually get into exploring each
7:28:14one of those tabs in the power query
7:28:16editor so we're going to go back to data
7:28:18get data and from there you can just
7:28:21select this of launch powerquery editor
7:28:24similarly you can also use a shortcut of
7:28:26just alt F12 I'm on a Mac so I have to
7:28:29press option
7:28:30but actually launching this up boom it
7:28:32has it with just a shortcut anytime you
7:28:35launch it it may be grayed out here so
7:28:37we need to make sure that we go in and
7:28:38actually select a query that we want to
7:28:41analyze and
7:28:45transform for this overview we're going
7:28:47to start with the view tab because
7:28:49mainly I want to get into actually how
7:28:51we can use the power query editor for
7:28:54Eda and thus save us a lot of time of
7:28:57actually having to analyze it in Excel
7:29:00in the spreadsheets itself instead we
7:29:02can do it right here so going through
7:29:04this first thing is you can toggle on
7:29:06and off the formula bar I always leave
7:29:08the form on so I don't know why that's
7:29:09an option next is the data preview I can
7:29:12change the font type I can also CH the
7:29:16column quality so this is telling us if
7:29:19there would be a potential error in here
7:29:23or if in this case of job location if
7:29:25there's empty values you typically have
7:29:27error values whenever the data type
7:29:29isn't being being understood correctly
7:29:31so in this case job tile short is text
7:29:33everything in there is a text column if
7:29:35I were to change this to number press
7:29:37enter to run I'm going to get errors all
7:29:39the way through here because well that
7:29:41was text and can't convert text to
7:29:43numbers also not sure why but it should
7:29:45say 100% error but it's not anyway they
7:29:47also have this green bar up at the top
7:29:50and you can use this that's what I
7:29:52actually prefer so I'm going to unclick
7:29:53on The View and changes from the con
7:29:55quality because you can actually look up
7:29:58here and see and then also togg it so in
7:30:00this case for salary or average it looks
7:30:03like there's 60% of them are valid and
7:30:0640% are empty now remember this is only
7:30:10doing the data sets around 30,000 or
7:30:1332,000 rows but it's only profiling so
7:30:16down here on the bottom column profiling
7:30:18based on the top 1,000 rows so that's
7:30:21all we're seeing right here if I wanted
7:30:23to see all of the data itself now
7:30:25depending on how big it is we may not
7:30:27want to do this I can select this at the
7:30:29bottom and column profiling based on
7:30:31entire data set and it's going to reload
7:30:34back into here not sure how long it's
7:30:36going to take now going over I can see
7:30:38there's 22,000 data sets of for data
7:30:41points of the salary year where 10,000
7:30:44are empty the other thing that you may
7:30:46have enabled by now is that column
7:30:47distribution to be able to see what are
7:30:50the what is the breakdown of distinct
7:30:53and also unique values investigating
7:30:56what actually distinct unique means I
7:30:58went back to the job tile short looks
7:31:00like now it's actually picking up on all
7:31:01the different errors I'm going to
7:31:03actually change this back we don't want
7:31:04this to be number for job tile short
7:31:06we're going to change this back to text
7:31:08and I'm also going to refresh the
7:31:10preview by going to that Home tab
7:31:13basically refreshing it to get it all
7:31:15cleaned up anyway if we recall from our
7:31:17previous analysis there's 10 different
7:31:20job titles of sat senior data scientist
7:31:23data engineers and whatnot and so that
7:31:26is the 10 distinct values they're
7:31:30distinct because they have repetitive
7:31:33values in here like right in here in six
7:31:35and 7 data engineer appears more than
7:31:38once now if we go over to something like
7:31:40job country they have 111 distinct so
7:31:43meaning 111 countries that have multiple
7:31:46different countries and only 12
7:31:48countries that have one value for it or
7:31:51one unique value all right the last
7:31:53thing in data preview is column profile
7:31:56and this is pretty neat right now I'm
7:31:58selected on the job tile short column
7:32:00it provides one on the left- hand side
7:32:02key statistics about the column and then
7:32:05two it actually shows a breakdown of the
7:32:08value distribution of it so this is
7:32:10really helpful in performing Eda if I
7:32:13wanted to go through here and actually
7:32:14see something so I can easily go in and
7:32:16even see something like job country and
7:32:18see how United States has the majority
7:32:20of the values and then how the different
7:32:22other countries Fall underneath that now
7:32:24this takes up a lot of room and sort of
7:32:27valuable real estate so I find myself
7:32:29togging Ling this column profile on and
7:32:31off all right last few sections in this
7:32:34view tab go to column if you have a
7:32:36large data set with a ton of columns you
7:32:38can just come down here select the
7:32:39column you want to go to and then it
7:32:41will navigate you to it parameters this
7:32:44is beyond the scope of this course we're
7:32:46not going to be enabling parameters or
7:32:47even using them so we'll call This na
7:32:50next is the advanced editor which allows
7:32:52us access to basically the behind the
7:32:55scenes of our am uh M language which
7:32:58we're going to be breaking down further
7:32:59in an upcoming lesson so we're going to
7:33:01save that but you can also access that
7:33:04from the home menu in advanced editor as
7:33:06well lastly is query dependencies
7:33:09whenever it gets into complicated ways
7:33:11that you're actually building your
7:33:13different queries and how they're
7:33:14connected to each other this is going to
7:33:16come in handy and this case we're
7:33:18showing that hey we connected to that
7:33:20Excel file on my MacBook and we loaded
7:33:24it into a pivot
7:33:28table all right next up is query
7:33:31settings I'm actually going to go ahead
7:33:32and close this out for queries over here
7:33:35anyway with the query settings we can
7:33:37actually change the name of the query if
7:33:40we want to in this case is named sheet
7:33:43one I don't really like that I'm going
7:33:44to name it something like J jobs and I
7:33:47know it has salary data in it so I'm
7:33:49going to have salary down here on the
7:33:51applied steps like we mentioned this is
7:33:52a step through walkth through of each of
7:33:55the individual steps that power query
7:33:57has taken to actually clean up our data
7:34:00set now one thing I will call out in
7:34:02this if I need to modify anything so in
7:34:05this case if I wanted to modify the data
7:34:08source here I could come inside of here
7:34:11into the formula bar and edit it I would
7:34:14encourage you if you're not familiar
7:34:15with the phone of the bar with using
7:34:17that or comfortable using it instead
7:34:19click click this settings icon over here
7:34:22on the right hand side and then
7:34:24typically a window will pop up and allow
7:34:26you to edit it so I could technically
7:34:29change the location of this or change
7:34:32what type of file it is the same for
7:34:34navigation as well I can basically pull
7:34:36back up that navigation window that I
7:34:38had before and change the sheet I wanted
7:34:40to for the change type this doesn't
7:34:42really have a gear icon next to it for
7:34:44us to edit so we're about to go through
7:34:47and actually change it but if we inspect
7:34:51the job posted date we'll see that here
7:34:54one it has it underneath the type number
7:34:56but then actually looking at the column
7:34:58itself it's a number value because
7:35:00remember Excel stores ex uh dates as
7:35:03number values behind the scene well we
7:35:05could convert this to a date by typing
7:35:07in date here but you may not be
7:35:09comfortable doing that just yet anyway
7:35:11with that that's a great segue into the
7:35:13Home tab into how actually we can change
7:35:16something like a data
7:35:19type with the home typ we've already
7:35:21seen a lot of things already right we
7:35:23saw the close and load too we also saw
7:35:26that I can go through and actually
7:35:28refresh my query query to make sure that
7:35:30it's fully loaded and up to date if I
7:35:33have multiple queries I can not only do
7:35:35this refresh pery I can go to this
7:35:37refresh all and it does refresh of all
7:35:39queries we've already seen Advanced
7:35:41edited before properties just allows us
7:35:44to actually go in and change the name of
7:35:46this query if you want to and manage is
7:35:48more advanced we'll be dive in that in a
7:35:50little bit similar to Under The View tab
7:35:52with goto column we also have this
7:35:54option of choose column and go do column
7:35:58we can also just actually select a
7:36:00column if you will so if I wanted to
7:36:02actually select job post to date or even
7:36:05more than that I can just do that and
7:36:07it's going to select it and it's going
7:36:10to actually remove all the other columns
7:36:12so which is not what we want to do which
7:36:14brings us a good point if we want to get
7:36:16mid rid of a step all we have to do is
7:36:18come over to the applied steps and
7:36:20there's a red x mark that will appear
7:36:23over any step that you do so I'm just
7:36:25going to go ahead and click X here and
7:36:27it's going to remove anything that I've
7:36:29done moving on to remove columns which I
7:36:31think is pretty self-explanatory if you
7:36:32want to remove a column you just select
7:36:34it and you select remove column
7:36:36additionally if I want to remove all
7:36:38other columns so in this case job title
7:36:40let's say I want to keep that I could
7:36:41select remove all other columns and it
7:36:43would do that I want to cancel this step
7:36:45so I'll click X similarly to remove
7:36:47columns we have well keep rows and also
7:36:50remove rows and then we have options for
7:36:52also sorting our values if we want to
7:36:55sort them from a to z or Z to A
7:36:57depending on a column so back to job
7:37:00post to date maybe I wanted them in
7:37:02numerical order I could just click A to
7:37:05Z and it would go through and actually
7:37:06sort it anyway I don't really want to do
7:37:08this I'm going to clear this step as
7:37:10well this brings us actually into what
7:37:12we want to do of we want to change this
7:37:15job posted date to a date time and
7:37:18that's we're going to use underneath
7:37:20this transform section in the Home tab
7:37:22right now this data type as I'm
7:37:25selecting this job posted dat it notices
7:37:27that it's a decimal number I go to
7:37:29something like search location it
7:37:31changes to text so what I want to do is
7:37:34change this data type of decimal number
7:37:37to specifically a date time because
7:37:40that's what we have in here we have date
7:37:41and time now this popup is going to come
7:37:44up if you're doing this underneath the
7:37:46step that has changed type already what
7:37:48it's noticing is that the selected
7:37:51column has an existing type conversion
7:37:53would you like to replace the existing
7:37:56conversion or basically preserve that as
7:37:58a number and add a separate step I'm
7:38:01just going to go ahead we're going to do
7:38:02replace current but I just want to show
7:38:03what it looks like of adding another
7:38:05step in this case I converted it in this
7:38:07step to a number and then the next step
7:38:11I converted it to a date time I don't
7:38:13like having a bunch of steps I want to
7:38:15make this as concise as possible so I'm
7:38:17going to clear that step instead and
7:38:19instead this time whenever we go through
7:38:21it and select date time I'm going to say
7:38:24hey replace current now underneath here
7:38:27it updated that job post to date type to
7:38:30date time and it's all within one step
7:38:32love this similarly to that date time I
7:38:35also want to convert the salary or
7:38:37average and the salary hour average
7:38:40columns right now they're decimal
7:38:42numbers which is nothing wrong with that
7:38:45but I actually have the option to change
7:38:47it to something like a currency in this
7:38:50case once again I want to replace the
7:38:52current step for that I'm going to do
7:38:54the same for salary hour average and
7:38:57change that to a currency as well for
7:39:00replace current covering briefly these
7:39:03other sections in the Home tab first up
7:39:05is merge and append we're going to be
7:39:08covering an entire lesson on this and
7:39:10how we can actually take different Excel
7:39:13files and different queries and combine
7:39:14them together with this manage
7:39:17parameters is outside the scope of this
7:39:19course I don't find myself ever really
7:39:21doing this so not something we need to
7:39:22worry about data source settings similar
7:39:25to what we saw outside of the power qu
7:39:28in Excel basically the same popup is
7:39:30going to come here to allow you to
7:39:32change where your data source is and
7:39:34then down here at the very end if we
7:39:36have wanted to put in a new query I
7:39:38wouldn't necessarily have to back out of
7:39:40the power query editor I could just come
7:39:41in here and select a new source a file
7:39:44or database or other source and then
7:39:46work through actually importing it in in
7:39:48a query sometimes I find myself also
7:39:50using this one of enter data say I had a
7:39:53simple table that I wanted to input into
7:39:57Power query to have I could go through
7:39:59and just create that
7:40:03table all right next up is the transform
7:40:06Tab and this one I feel is maybe
7:40:08actually although it looks like a lot of
7:40:09options it's probably one of the most
7:40:11simplest as you can see we have things
7:40:12like text column number column date and
7:40:15time columns structured columns
7:40:16basically if we have a data type of this
7:40:19we're going to go to you can go to if I
7:40:21have a number column I want to go to
7:40:23this and see what things I could do to
7:40:24it such if I could do statistics to it I
7:40:27could do rounding to it or I could even
7:40:30get information out of it if it's even
7:40:31or odd I also have this section on any
7:40:34column that basically applies to any
7:40:36column this is allows us to one like we
7:40:39saw in the Home tab actually convert the
7:40:41data type of something but also even
7:40:43more advanced Transformations such as
7:40:46pivoting and unpivoting columns which
7:40:49we're going to be diving deeper into in
7:40:50the next lesson on Advanced
7:40:52Transformations and finally we have this
7:40:54section on tables which just does more
7:40:56of generic things to this data set such
7:40:59as if I wanted to actually go through
7:41:01and count the rows on this could and I
7:41:03find out I have 32,000 different rows on
7:41:06this anyway I actually want to transform
7:41:08a column of this specifically this job
7:41:11via column as you notice from here that
7:41:15all these different job platforms have
7:41:17via and then a space right at the
7:41:20beginning of it I want to actually
7:41:22remove that so in order to do this I
7:41:24make sure that one job via column is
7:41:26selected I notice up here in the any
7:41:29columns it has the data type of text now
7:41:32there are a few options in underneath
7:41:34the text column section for like
7:41:36splitting columns I could split it by
7:41:39this half and then delete that via but I
7:41:41find actually the easiest way to do this
7:41:43is just go through this replace values
7:41:47and we're not going to do replace errors
7:41:48we're going to just do replace values
7:41:49itself and we find a value in here in
7:41:53this case we want to find VIA with a
7:41:56space and we want to replace it with
7:41:58well nothing if I wanted to go into
7:42:00advanced options and I have a few
7:42:02different selections available but
7:42:03neither of these applicable does so
7:42:05we're going to just go ahead and click
7:42:06okay and Bam now we have these job
7:42:09platforms cleared up now we've been
7:42:12going through this and keeping the names
7:42:14of these steps the same but sometimes I
7:42:16like to be more descriptive in when it's
7:42:19not a general tyag now it named this new
7:42:22Step replaced values I may actually do
7:42:25that a few times and I want to be able
7:42:27to whenever I go back to this actually
7:42:28be able to identify what steps did what
7:42:31in this case change type promoted
7:42:32headers navigation Source those are all
7:42:35only usually typically done once so I
7:42:37know what that means however however for
7:42:38this one I don't know so I'm going to
7:42:40right click it and go to rename and I'll
7:42:42say this is replaced via in job via
7:42:46which is much more descriptive in my
7:42:50opinion all right only one more tab to
7:42:53cover and that is the add column with
7:42:56transform we transformed a current
7:42:58column with ADD column we're adding
7:43:00additional column to this similar
7:43:02transform it has these options for text
7:43:05number and also date and time so very
7:43:08familiar features with this so let's say
7:43:10I wanted to extract the month and the
7:43:13year out of the job posted date column
7:43:15basically I want to Callum for month and
7:43:17I want to Callum for Year anyway
7:43:19previously we learned with that
7:43:20transform tab if I were to come into
7:43:21here under date time and then select
7:43:24something like month it's going to
7:43:27transform this tab so it's going to get
7:43:30rid of the contents of the job posted
7:43:32date is not necessarily what I want I
7:43:35want a new column so I'm going to
7:43:36actually get rid of this Stu so with ADD
7:43:39column what this does is with that job
7:43:41posted date column selected I select
7:43:44date in this case I want month I could
7:43:46do start a month end of month day of
7:43:48month whatever I just want the month
7:43:49itself and then inserted month is pretty
7:43:52descriptive I however don't like the
7:43:55name of this so I could come in here
7:43:57this is an option and change I double
7:43:59clicked on this and name this job posted
7:44:03month and then press enter now with this
7:44:07I'm going to get a renamed columns here
7:44:11so now I have two steps of this month
7:44:14was inserted into this and then we
7:44:16rename the column I would encourage you
7:44:19to minimize the amount of steps you have
7:44:21because these queries can get quite long
7:44:23in this case I'm going to delete this
7:44:25rename column go back to this inserted
7:44:27month if we actually re read this you
7:44:30don't actually need to understand what's
7:44:32going on much in here but I can see
7:44:35basically that we have this month in
7:44:37quotation marks and this is named month
7:44:41so I basically can reason that this is
7:44:44probably the new column title of this so
7:44:48instead of using month I'm just going to
7:44:50edit this in the formula bar to job
7:44:52posted month then I'm going to click at
7:44:55the end and press enter and now all
7:44:58within one step I inserted that month
7:45:01and renamed it as well if you're not
7:45:04comfortable doing that feel free to go
7:45:06through that next step of actually
7:45:07double clicking this and actually
7:45:08changing it but I would encourage you if
7:45:10you can actually try to mess around with
7:45:11the formula if you make a mistake it's
7:45:14pretty simple to just X out of that step
7:45:17and then redo it again so there's no
7:45:19harm to your actual data set now
7:45:21similarly if I wanted to create that job
7:45:23posted year column I could just go
7:45:25through here select year whether I want
7:45:28start year end of year year itself once
7:45:30again it inserts year and then I would
7:45:32want to change the name of this and
7:45:33change this to job posted year and then
7:45:36click enter and Bam now we have it I
7:45:39don't actually need this all these are
7:45:41from 2023 I don't actually this is not
7:45:44going to provide any useful data for me
7:45:45so I'm actually going to delete this
7:45:49Stu all right I want to do one last
7:45:51transformation before we actually load
7:45:53this and going to actually visualize
7:45:55this so we have our salary year average
7:45:58column and then also want to compare
7:46:00this to the salary hour average column
7:46:05but right this is on a yearly basis this
7:46:07is on an hourly basis what we could do
7:46:10is do a conversion to our salary hour
7:46:14average column to get it to an equal
7:46:16value or comparable value to our yearly
7:46:18value meaning we could put the number of
7:46:20hours in a year multiply it times this
7:46:23value and from there get what would be
7:46:26the expected yearly salary for this hour
7:46:30data so I could do this via the
7:46:32transform tab right going into that
7:46:34number column under standard we want to
7:46:37actually multiply and then there's 2080
7:46:41hours in a year working hours for 40
7:46:44hours of work week I could go through
7:46:47and actually do that and that's going to
7:46:49update this column itself but remember
7:46:52we probably want its own column so I'm
7:46:55not going to use that instead we'll go
7:46:57to add column with this
7:46:59hour average column selected select
7:47:02standard multiply put in those hours of
7:47:052080 and then click okay once again I'm
7:47:08going to rename this I can see that this
7:47:10multiplication column is titled this via
7:47:14in this step right here so I'm going to
7:47:15rename it to salary hour adjusted and in
7:47:20this case I'm going to also rename this
7:47:21step to adjusted hourly salary to yearly
7:47:26now I'm sort of a stickler for keeping
7:47:27my data set in order right now I have
7:47:29this job posted month and it's sort of
7:47:32right away from it's pretty far away
7:47:34from my job posted date I would actually
7:47:36want to move it right next to it so
7:47:38there's a couple options I can do to
7:47:40move it I can select the column and then
7:47:43come up here to the transform Tab and
7:47:45move go left right to beginning to end
7:47:49or I can actually just take it and then
7:47:52drag it and this is taking forever it's
7:47:54like paint dry but find where I want it
7:47:57boom plant it in and then inserted the
7:47:59step of reordered columns I'm going to
7:48:01do the same thing with salary hour
7:48:05adjusted and put it right next to salary
7:48:07hour average and both of these done with
7:48:10one step of reordered columns so I'm
7:48:13fine with
7:48:16that so now let's actually get into
7:48:19analyzing this specifically I want to be
7:48:21able to analyze and compare this salary
7:48:23hour adjusted column that we just
7:48:25created compared to the salary year
7:48:28average so going back to home I'm going
7:48:30to close and load this in we have this
7:48:33previous analysis that we did before
7:48:35doing Eda on the jobs actually want to
7:48:38create my own from scratch all right so
7:48:40back on sheet one we can see our queries
7:48:41connection specifically that data job
7:48:43salary remember the data tab you can go
7:48:46into that and it can toggle on all that
7:48:48queries and connections anyway we want
7:48:50to insert I want to analyze that hourly
7:48:53adjusted salary so I'm going to come in
7:48:55to create a pivot chart we also do pivot
7:48:59chart and pivot table at the same time
7:49:00anyway when this pops up for pivot table
7:49:02or pivot charts we want to we're not
7:49:04going to select a table AR range because
7:49:08this is a power query connection if you
7:49:10will we're going to use this external
7:49:13data source and we're going to say
7:49:15choose connection what connection do we
7:49:17want to use for this specifically I want
7:49:19to use that DOA job salary so go ahead
7:49:22and click that and open and we're going
7:49:24to insert it into the existing worksheet
7:49:27so now the pivot table set up for us go
7:49:28forward to do one quick note you may be
7:49:31tempted say if we went back to jobs Eda
7:49:34to rightclick this and then go load to
7:49:37and let's say hey I wanted to create a
7:49:39new pivot chart well the problem is is
7:49:42going to then get rid of this pivot
7:49:45table that we previously created so you
7:49:48don't want to necessarily if you want to
7:49:50keep this you don't want to actually do
7:49:52that back to the pivot table itself
7:49:54you'll notice now because we have these
7:49:55queries and connections but you can
7:49:57toggle between the two over here on the
7:49:59right hand side anyway what I want to
7:50:01compare is that salary hour adjusted to
7:50:06that salary year average right now it's
7:50:09doing sums we don't want that we do
7:50:12eventually we're go to Value fail
7:50:13settings we're going to do average here
7:50:15we're eventually going to do median I
7:50:17promise you but we're going to STi for
7:50:19average for the time being I'll adjust
7:50:21both of these to be of average then I'm
7:50:24not really liking the formatting here I
7:50:26know we adjusted it as currency back in
7:50:28the the power query but this is the one
7:50:30data type that I find doesn't actually
7:50:34follow through in actually making into
7:50:36the correct data type when you import it
7:50:38into Excel so you do need to go back
7:50:40still and actually convert it into the
7:50:42correct thing anyway we're seeing that
7:50:43the hourly salary is much less than the
7:50:48yearly salary and moving this over we
7:50:50can also see this via visualization this
7:50:53doesn't really show as much I would
7:50:55rather look at this when compared to job
7:50:58type
7:50:59so I'm going to go ahead and grab job
7:51:00title short and throw it into the axis
7:51:04now closing out of this and then closing
7:51:07out of this on the side we can now get a
7:51:10better view of this I'm not liking the
7:51:13format of this pivot chart specifically
7:51:15I'm going to go in here design under
7:51:16change chart type and change this to a
7:51:19bar chart I feel like it's going to be
7:51:21easier to read yeah it's a lot easier to
7:51:23read also for these visualizations I'm
7:51:25going to rightclick this and I'm going
7:51:27to say hide all field button so that
7:51:29make this easier to view and I'm going
7:51:31to go ahead and stick The Legend at the
7:51:34bottom okay we're off to a good start
7:51:37other things I want to do to clean this
7:51:39up is oh my goodness this is so long I'm
7:51:41going to change these column titles to
7:51:44hourly adjusted salary and then yearly
7:51:47salary additionally I want to sort this
7:51:49a little bit better specifically from
7:51:51high to low so under sort options more
7:51:54sort options I'm going to go into
7:51:56sorting this as sending based on the
7:51:58year L salary from high to low sorry
7:52:01that's actually descending selecting
7:52:03year salary clicking okay no it was
7:52:06right the first time it's ascending okay
7:52:08this is looking good you know also I
7:52:09don't like having different colors I
7:52:11like actually going with a consistent
7:52:14theme so going into design change colors
7:52:18I'll change this to this monochromatic
7:52:20pallette 8 and Bam we now have our final
7:52:24visualization that we use power query to
7:52:26basically ingest all our data in clean
7:52:29it up create this new column of hourly
7:52:33adjusted salary perform an analysis in
7:52:35Excel to average it and we can see that
7:52:39consistently the hourly salary is well
7:52:43below that of the yearly salary so I
7:52:46guess it pays to have a salary job all
7:52:49right we have some practice problems for
7:52:50you to now go through and test out all
7:52:54these different features and get more
7:52:55familiar with the power query editor in
7:52:59the next lesson we're going to be going
7:53:00into advanced Transformations and Diving
7:53:03deeper specifically in analyzing skills
7:53:06and using power query to actually clean
7:53:08it up so where we can actually analyze
7:53:10skills with that see you in that
7:53:15one all right welcome to this lesson
7:53:17we're going to continue on with power
7:53:19query specifically focusing on using
7:53:22more advanced Transformations and for
7:53:25this we're actually going to get into
7:53:27analyzing those skills and being able to
7:53:30put them on a graph and actually
7:53:31visualize what are the top skills of
7:53:34data nerds now if you recall way back in
7:53:37the functions and formulas chapter when
7:53:40we went over text functions we did a
7:53:42little bit of text cleanup to clean up
7:53:44this column and then plot it but we were
7:53:46only able to do that with around 20 rows
7:53:49now with the power of power query we're
7:53:52actually going to be able to clean up
7:53:53all these values and be able to
7:53:56visualize it for all 30,000 job post
7:53:59so let's jump in if you want to you can
7:54:01continue on from that worksheet that we
7:54:03used in the previous lesson and just
7:54:07make sure that you do go through and
7:54:09actually save it before you continue on
7:54:12however if you got lost in the way or
7:54:13you just don't have that file anymore
7:54:14feel free to use the lesson or the file
7:54:17from the last lesson of power query Eder
7:54:20once again you don't want to be using
7:54:21the actual one working cuz that has the
7:54:23final results we're going to want to
7:54:24work with that one and this has all the
7:54:26different work that we did it also has
7:54:28some some additional analysis whenever I
7:54:30looked at plotting it over time to see
7:54:33if how the salary of yearly versus
7:54:35hourly
7:54:38compared anyway let's get into editing
7:54:41this and we can get to the power query
7:54:42editor by going up to get data launch
7:54:45power query or pressing alt F12 once it
7:54:49loads and need to click on the query
7:54:50that I actually want to look at and I'm
7:54:52going to close this or minimize this the
7:54:54first thing that I want to do is start
7:54:56an index column on this data set because
7:55:00in general whenever you have a source
7:55:02data set or a fact table like this is
7:55:05you want to have an index associated
7:55:08with it yeah these row numbers are good
7:55:09but that's not good enough and we'll be
7:55:11using it more in the power pivot chapter
7:55:13but it's good practice to start it now
7:55:15so moving over to the add column tab I'm
7:55:17going to go to index column it allows us
7:55:20to start from either zero or one I'm a
7:55:22coder so I like from zero now Pro tip I
7:55:25want this index at the front now I could
7:55:27go to to transform and then move and
7:55:30then move this to the beginning but
7:55:32remember we did this reordered columns
7:55:34right here so what I'm actually going to
7:55:35do is take this added index put it
7:55:38before reordered columns now that the
7:55:41reordered columns is right there
7:55:43whenever I select this index and move
7:55:46this over to beginning it's going to be
7:55:48included in part of this step of all of
7:55:51our column reord so I don't have once
7:55:53again multiple different reordered
7:55:54columns
7:55:58all right in order to clean up this job
7:56:00skills column we're going to end up
7:56:02being putting this uh these skills right
7:56:06now they're separated by column inside
7:56:07of this list we're going to be breaking
7:56:09them up into their own individual rows
7:56:12and because we're breaking this up into
7:56:13different rows this now is going to put
7:56:16for this Row one value here this is
7:56:18going to make 1 2 3 4 5 6 7 this is
7:56:21going to make seven different rows of
7:56:23data this is going to mess up anytime we
7:56:25want to analyze anything because imagine
7:56:27if you have like a salary data it's then
7:56:29going to appear seven times so the main
7:56:31point of explaining that is we want a
7:56:33new query to actually populate and
7:56:37actually break these skills out into
7:56:39their own separate rows so in order to
7:56:41create a query or another query right
7:56:43now we have queries one to create
7:56:46another query from this we have two
7:56:48options and that's underneath Home tab
7:56:50they have manage and we can either
7:56:54delete a query which we're not going to
7:56:55do we can either duplicate it or
7:56:57reference it I can also get to this by
7:56:59just right-clicking the query and it
7:57:01also has these of duplicate and
7:57:03reference let's actually look at both of
7:57:05those starting with duplicate first so
7:57:08I've created my duplicate query and as
7:57:10you can see it basically has a duplicate
7:57:13of the original query nothing really has
7:57:17changed from it now this is cool if I
7:57:19want to walk through all the different
7:57:21steps again and I wanted to have it in
7:57:23this new query but I actually like this
7:57:27other option so I'm going to go to data
7:57:28job salary this CL I'm going to go down
7:57:31select reference okay this query this
7:57:34one named three is referencing data job
7:57:38seller and it only has one applied step
7:57:41if we look at the applied step all it is
7:57:43doing is referencing the data jobs
7:57:46salary so this first query right now and
7:57:48populating it for us and this is really
7:57:50good because say now I make changes to
7:57:53the original query such as say I want to
7:57:56go through and I don't want any any more
7:57:58of the hourly data in here I only want
7:58:00the yearly data so I filter down to only
7:58:02have the yearly data so now it's
7:58:05filtered these rows for the yearly data
7:58:07don't worry we're actually not going to
7:58:08do this I'm going to delete this Stu but
7:58:09anyway if I go to that duplicated query
7:58:12the one with the three at the end this
7:58:15one only has year values in it this I
7:58:19can verify is 100% yearly by looking
7:58:22either the column distribution or the
7:58:24column profile everything is your anyway
7:58:27we don't actually want to do that step
7:58:29so I'm going to go back to this original
7:58:31query clear the filtered rows and once
7:58:34again it's going to just clean this back
7:58:36up to have two distinct values so
7:58:38compare checking the S rate yearly and
7:58:39also hourly okay so we like the
7:58:42reference for our case cuz I like we may
7:58:45make changes to the original one so I'm
7:58:47going to delete this number two because
7:58:49remember that was the duplicate and
7:58:50we're going to keep the number three one
7:58:52which was the reference we're also going
7:58:54to be doing all our alterations on the
7:58:56skills on this one so I'm going to to
7:58:58rename this one data jobs
7:59:03skills so with this new query data jobs
7:59:06skills let's actually get into cleaning
7:59:08up this column of data of job skills
7:59:11specifically we're going to be
7:59:12separating this into each of these
7:59:15skills into the new rows by this comma
7:59:18delimiter but we need to remove a few
7:59:21things from this specifically this has
7:59:23brackets around it and it also has
7:59:24single quotes we don't need any of that
7:59:26we need to remove it so going to that
7:59:28transform tab we're going to go into
7:59:30replace values and we've done this
7:59:32before so for the value defin I'm going
7:59:34to just start with the first square
7:59:36bracket we want to replace with nothing
7:59:38I'm going to click okay additionally we
7:59:40want to replace the other bracket as
7:59:43well replace it with a blank and then
7:59:45finally we want to replace that single
7:59:48quote as well also I'm going to just
7:59:51rename these all next thing we going to
7:59:53do is actually split these columns on
7:59:55this delimiter of a comma so under
7:59:59transform we can go here to split column
8:00:02it has a few different options by
8:00:03delimiter number of characters by
8:00:05positions we can go to by delimiter I'm
8:00:09going to select that for this we're
8:00:11going to use a comma delimiter because
8:00:13there's multiple different options you
8:00:14could potentially use for this we want
8:00:16to split at not just the leftmost but we
8:00:18want to split at each occurrence there's
8:00:21no quote characters in here we removed
8:00:23all the quote characters so I'm going to
8:00:24click none and then click okay so now we
8:00:28just split these skills into let's see
8:00:31how many different columns we have here
8:00:33looks like we have up to 24 skills for
8:00:37all these different skills that we have
8:00:39so now what we need to do to get all of
8:00:41these if you will skills within a single
8:00:45column we need to unpivot them but the
8:00:49one issue right now so I have all these
8:00:50skills right here but we also have all
8:00:52these other columns right here I don't
8:00:55really care about all them just I don't
8:00:57really care about around too much I want
8:00:59to mainly just analyze job title short
8:01:01and indexed so what I'm going to do to
8:01:03make this easier because I need to
8:01:05basically select which columns I want to
8:01:07remove or which ones I don't want to
8:01:09remove in this case so what I'm going to
8:01:11do is go back to source and this one has
8:01:15before we actually broken up the job
8:01:17skills so I'm going to select job skills
8:01:20hold down control and then from there
8:01:22select job title short and also index
8:01:25and then underneath the Home tab we're
8:01:26going to go to remove call s what we're
8:01:28going to do remove other columns
8:01:30basically going to keep those three
8:01:32columns that we have now we are doing
8:01:34this in the applied steps after that
8:01:35first step of source so it's asking hey
8:01:37do we want to insert this step yes we do
8:01:40and so now we've limited it down to
8:01:42those three columns and Bam now whenever
8:01:45we go down here down to that last step
8:01:48of change type we can see that we have
8:01:50all our different job skills and then
8:01:53over on the right hand side we have our
8:01:55index and our job tile short which I
8:01:58don't really like the order of this I'm
8:01:59actually going to go back to reorder
8:02:01this over here I'm going to just take
8:02:04these column values and then put them in
8:02:06this order of index job title short and
8:02:09job skills so now we actually get into
8:02:12unpivoting these job skills columns
8:02:15basically making all these job skills
8:02:17into one column so I'm going to select
8:02:19instead of selecting all the job skills
8:02:21column I'm actually going to select the
8:02:22opposite holding control select the
8:02:24index and job title short and I'm going
8:02:27to go into to transform tab into unpivot
8:02:30columns and for this one once again
8:02:33we're going to use the other we want to
8:02:34unpivot other columns and go ahead and
8:02:37do this all right so what we do here we
8:02:40now have this new column of attribute
8:02:42and value attribute if we go back that
8:02:46is just the name of the column that was
8:02:48created previously and then the value is
8:02:51what was in the cell itself and that's
8:02:53filled with all the skills so personally
8:02:56I don't really care for use of this
8:02:58attribute so I'm going to go ahead and
8:03:01just remove this column by right
8:03:02clicking and selecting it additionally
8:03:04I'm going to go back up here and I don't
8:03:05want this to be named value so I can go
8:03:09in and inspect this under unpivot other
8:03:11columns I can see in here that it
8:03:14renames these columns attribute and
8:03:16value in this case I don't want to be
8:03:18value like I said I want to be job
8:03:21skills clicking enter boom renamed it to
8:03:25job skills and then in here it is job
8:03:27skills
8:03:28now one thing that's bothering me real
8:03:29quick before we continue on to actually
8:03:31visualizing this data is this column
8:03:34here typically I like to name things
8:03:36something like job uncore whatever it is
8:03:38in this case index I want to Name jobor
8:03:41ID but if you recall back we created
8:03:43this back in this data jobs salary
8:03:46portion especially here under the step
8:03:48of added index I want to change this
8:03:51from index as we've done before going in
8:03:53and renaming it to job ID however
8:03:57whenever I do this press enter this is
8:03:59going to break my queries and this is
8:04:02going to happen to you anytime you're
8:04:03manipulating it so I think we need to
8:04:04get familiar with it so if I go to the
8:04:06next step of reorder columns we're going
8:04:08to have this expression error the column
8:04:11index of the table wasn't found duh
8:04:14because we named it job ID in the
8:04:17previous step instead of index but this
8:04:19step is still the same so what I can do
8:04:21is come in here change index to job ID
8:04:25press enter and Bam that updates but
8:04:28then now going to data job skills we're
8:04:31going to have the same thing you're
8:04:33going to notice with this one right the
8:04:35column index the tail wasn't found index
8:04:37so same error message what we want to do
8:04:39you can do is go to error it's going to
8:04:41go to the first occurrence of that error
8:04:43in this is trying to reference index we
8:04:46if you call back from if we go to the
8:04:47first step of source we expect it to be
8:04:50called job ID now because we renamed it
8:04:52right so I'm going to change this to job
8:04:55ID and then scrolling through the
8:04:58applied steps to see whenever we get to
8:05:00our next error if there is an error and
8:05:03that's unpivot other columns
8:05:05specifically they have job title short
8:05:07and index I don't want index here I want
8:05:09job ID and now bam now we have it
8:05:12cleaned so I should have done that job
8:05:14ID but that was actually good
8:05:15troubleshooting to walk through that you
8:05:17may
8:05:20encounter so let's actually get into
8:05:23visualizing this so we're going to go to
8:05:24home and we're going to close and we're
8:05:26going to close and load
8:05:28now it's popping up as a table but we
8:05:30actually want to analyze this I don't
8:05:32really care to have it as a table so I'm
8:05:34going to right click it and I'm going
8:05:35click load to specifically we're going
8:05:37to go to a pivot chart and we'll insert
8:05:41in the existing worksheet because we're
8:05:43going to get rid of that data yes
8:05:44there's going to be possible data loss
8:05:45we understand that so I'm going move
8:05:47this chart off to the side select inside
8:05:49the pivot table and we want to analyze
8:05:51the job skills so I'm going to take the
8:05:53job skills put them in rows and then the
8:05:55job skills also in the values to to
8:05:57count up the values then also I'm going
8:05:59to sort them I want to sound them from
8:06:02high to low so I went to more sort
8:06:04options um we're doing a descending
8:06:06order count of job skills so now there's
8:06:10a ton of different skills in here but
8:06:12want you to inspect this if you notice
8:06:15one these skills have sometimes have
8:06:18spaces in the front of them basically we
8:06:21didn't do a full cleanup of this so
8:06:24that's why we have python twice in here
8:06:26is cuz this one has a space of it so
8:06:28opening up the power query editor by
8:06:30playing by pressing alt F12 so
8:06:33underneath the data job skills query I'm
8:06:36going to go ahead and we want to do a
8:06:38text
8:06:39transformation specifically if we look
8:06:41underneath this underneath for format we
8:06:42can change this to lower case upload
8:06:44case capitalize each word we're going to
8:06:46do trim which removes leading and
8:06:49trailing white space from each of the
8:06:51cells in the selected cell from there
8:06:53we'll go back to home close and load
8:06:55this and now it's going to be reloading
8:06:58the data and those duplicate values are
8:07:01now going to be removed now there's a
8:07:03lot of skills here so I really only want
8:07:05to see the top 10 so I'm going to put a
8:07:08filter on here go into value filters and
8:07:12top one specifically want to see the top
8:07:1410 items by count of job skills also I'm
8:07:18going to rename this to skill count and
8:07:21because these are text values down here
8:07:23I'm actually going to change this from a
8:07:25column chart going to change chart type
8:07:28into a bar chart instead clicking okay
8:07:32boom and then with this obviously it's
8:07:34not sorted from high to low that's how I
8:07:36want actually to sort it so I'm going to
8:07:38go in here back underneath our more sort
8:07:41options Chang this from descending to
8:07:43ascending and the good thing about this
8:07:46is we still have that top 10 filter on
8:07:48it so it's still going to apply this and
8:07:49have the top 10 values on there first
8:07:52last little clean up I'm going to hide
8:07:53all field buttons I'm going to get rid
8:07:55of this Legend right here and and then
8:07:57I'm going to rename this to what are the
8:08:00top skills of data
8:08:04nerds now let's say that I'm frequently
8:08:08referencing the top 10 skills as we have
8:08:12right here and instead of having to
8:08:14populate this every single time I want
8:08:17to actually create a own or create a
8:08:19query for this so opening power query
8:08:22going to alt F12 I could do the same
8:08:25analysis inside of power query query and
8:08:27get this into its own table to be reused
8:08:30but for this I don't want to use this
8:08:31data job skills query instead like we
8:08:34did before I'm going to create a new
8:08:35query we're not going to duplicate this
8:08:37instead we're going to reference it so
8:08:39now it's Unique and distinct and I'll
8:08:41rename this data jobs skill count
8:08:45because we're get the top 10 and their
8:08:47Associated count so in order to do this
8:08:49analysis to find what is the count of
8:08:51all these different skills we want to do
8:08:54a group buy and it's right here under
8:08:56transform form under that Home tab and I
8:08:58can do group by which group rows in the
8:09:00table based on the values in the
8:09:02currently selected column we're going to
8:09:04be forming a basic Group by we're using
8:09:06that job skills column I could change it
8:09:08to another column if I wanted to and
8:09:09that new column name is going to be
8:09:10skill count operation we're going to be
8:09:13counting the rows we could do any other
8:09:14type of aggregation as well if we had
8:09:16numerical data we could do average
8:09:18median min max whatnot go ahead and
8:09:20click okay so we've done this
8:09:22aggregation now the next thing is I just
8:09:25want to get the top 10 values but before
8:09:27to do that I need to actually sort this
8:09:30in descending order right now I can tell
8:09:32looking into the numbers this isn't
8:09:34necessar although it looks like it isn't
8:09:35right so clicking the arrow up at the
8:09:38top I'm just going to say hey sort
8:09:39descending and then we want the top 10
8:09:42values so underneath the Home tab under
8:09:43keep rows I'm going to have keep top
8:09:47rows and it's going to prop me how many
8:09:49number of rows do I want to keep 10 in
8:09:51this case I want the 10 values and now
8:09:53from here all I got to do is close and
8:09:55load this into its own separate query
8:09:58and Bam here we have it and so if I
8:10:01needed to reference the top 10 skills
8:10:03any time all I would have to do is just
8:10:04reference this query and I wouldn't have
8:10:06to like we did last time go through this
8:10:08full analysis so power of query is
8:10:10really great at automating some
8:10:12repetitive analysis and having it just
8:10:14ready for
8:10:17you all right last little cleanup if we
8:10:20look at these skilled names they're not
8:10:22formatted correctly specifically if I
8:10:24look at something like SQL I expect to
8:10:26be all capital letters SQL capital
8:10:28letters python I expected to be Capital
8:10:30At the beginning python so we're going
8:10:32to go through and actually fix this so
8:10:34that way whenever we present our data to
8:10:36someone it doesn't look like a hot mess
8:10:39so opening up the power query menu by
8:10:41pressing alt F12 we're going to go into
8:10:44the data jobs skills query specifically
8:10:47on that last step on and we're want to
8:10:50alter the job skills column so the first
8:10:52thing I want to do with this text
8:10:54cleanup the easiest thing looking at
8:10:56this is we just need to capitalize the
8:10:59first letter of every single word and
8:11:02then from there we'll go through and
8:11:03actually fine-tune it to capitalize in
8:11:06case of SQL capitalize all letters we'll
8:11:08have to put in special case for this
8:11:09anyway if you recall from before we have
8:11:11that transform format and they have this
8:11:14capitalize each word we're going to do
8:11:17that the next thing though the more
8:11:19complicated one is we're going to go
8:11:20into add column and we're going to add a
8:11:24conditional column so what we're going
8:11:25to do is go through we're going to keep
8:11:27the the name of custom column cuz we're
8:11:29technically going to be since we're
8:11:30adding a column we're going to have to
8:11:31go and delete this job skills column
8:11:33once create this new one I don't want to
8:11:35name a job skills right now going to
8:11:36call MK anyway what we want to do is we
8:11:39want to select the column that we want
8:11:40so if job skills equals in this case we
8:11:43expect to equal something like SQL we
8:11:46want the output to equal SQL then if we
8:11:49want to add more conditions or Clauses
8:11:51to it we go to add Clause once again I'm
8:11:54going to select job skills and I'm going
8:11:56to put something like
8:11:57powerbi it had a lowercase ey at the end
8:12:00I want the powerbi to be fully
8:12:02capitalized at the end I also went
8:12:04through and added some other ones such
8:12:06as AWS gcp no SQL and SAS most all these
8:12:11required them to just capitalize fully
8:12:14except for the no SQL one then what do
8:12:17we want it to be if it's not any of
8:12:18these conditions well we'll add this
8:12:19else clause and we want it to be
8:12:23basically the results of an entire
8:12:25column we want it to be whatever it is
8:12:27already in the job skills column I'm
8:12:30going to go ahead and click okay so now
8:12:32we have this cleaned up data set as well
8:12:36with nice looking names now if you want
8:12:38to if you're going through and finding
8:12:40anything in here that you want to clean
8:12:41up feel free to add to that conditional
8:12:43column statement those are the ones I'm
8:12:45just going to go for right now anyway
8:12:46because we added this new column and I
8:12:48don't really know an easy way to do this
8:12:51without actually creating this new
8:12:53column we need to now go ahead and
8:12:55remove job skills and rename custom so
8:12:59going to the Home tab I'm going to
8:13:01remove column I'm going to remove the
8:13:03one that's selected and I'm going to
8:13:05renames custom to job skills and
8:13:09conveniently because we're using that
8:13:11same name and just replacing it if I go
8:13:13to the data jobs skill count that one
8:13:16because it references this one will also
8:13:19get updated and all those values in
8:13:21there are updated as well anyway let's
8:13:24go ahead and close and load and inspect
8:13:27this is our previous pivot table and
8:13:30pivot chart that we analyzed it's now
8:13:31going through and loading all the data
8:13:34and now we have it updated with all that
8:13:36correct formatting for those different
8:13:38data points one last thing before we go
8:13:41this is generic these top skills of data
8:13:43nerds tall data nerds and that is using
8:13:45the data job skills query which has the
8:13:48job title short column in it so we can
8:13:51actually visualize this for a certain
8:13:53job by going into pivot chart analyze
8:13:56I'm going to go into insert slicer
8:13:58specifically we're going to look at job
8:13:59title short I'm going to put it over
8:14:01here and then as usual I'm going to
8:14:04rename it real quick to job title and
8:14:07now let's say we want to analyze
8:14:08something like data analyst we can see
8:14:11that SQL is the top skill but Excel is
8:14:15in second place followed by python
8:14:17Tableau and SAS what about for business
8:14:20analysts very similar in that sqls top
8:14:23and then Excel is in that second place
8:14:25so really unique and showing the
8:14:27importance of excel Within These skills
8:14:29and pretty meta that we used Excel to
8:14:32find this out all right now it's your
8:14:34turn to give it a shot you have some
8:14:35practice problems to go through and get
8:14:38more familiar with doing these Advanced
8:14:40Transformations specifically pivoting
8:14:42unpivoting and then also Group by all
8:14:45right with that I'll see you in the next
8:14:47one we're going to be diving into append
8:14:49and merging queries specifically going
8:14:52to be doing this with that skill query
8:14:54that we did previously all right see you
8:14:56there
8:15:00let's now get into how to perform a pend
8:15:04and also merges and so the first portion
8:15:07of this lesson the easiest portion of my
8:15:09opinion is going to be a pend
8:15:11specifically going back to that Excel
8:15:13sheet where we had all those different
8:15:16uh sheets for the months of the year and
8:15:19they're job posting on each because all
8:15:20these data sets are of the same format I
8:15:22have the same columns we're going to be
8:15:24able to append all these together and
8:15:26get what is our final data set of all
8:15:2830,000 rows if you recall each month had
8:15:31around 3,000 postings so that's how we
8:15:33get to that value from there the primary
8:15:35focus of this lesson will then shift to
8:15:37merge for this we're going to be
8:15:39combining our two queries that we built
8:15:42previously one which was our original
8:15:45data set so we titled that one data jobs
8:15:47salary and then that new query that we
8:15:50created in the last lesson on the skills
8:15:53so data job skills we're going to be
8:15:55merging those two together
8:15:57and this will allow us to do some pretty
8:15:59interesting analysis specifically now
8:16:01that we've merged those we'll be able to
8:16:03see based on a skill what is the
8:16:06expected salary and we're going to build
8:16:08a visualization for that for the top 10
8:16:10skills now merge unlike a pend is a very
8:16:14complex operation mainly because there's
8:16:17a lot of different types of merges
8:16:19specifically there's six type of merges
8:16:21in Microsoft alone so we're going to be
8:16:23walking through each one of those so you
8:16:25understand the differences and know
8:16:27which one to use when for this first
8:16:29append example we're going to be using
8:16:31this data job salary monthly data set
8:16:35and just as a refresher this contains
8:16:37everything for in this case I'm selected
8:16:39on the January sheet down here and this
8:16:41has all the January data which has
8:16:43around 3,100 rows for this and we have
8:16:47each one of the months for the year
8:16:52here anyway let's use power query to
8:16:54append all these together because
8:16:57previously before you knew about this
8:16:58you'd have to go through and actually
8:17:00copy and paste all these different
8:17:03options right here and then put it into
8:17:04a new sheet doing this 12 times is a hot
8:17:07mess so since this is only a simple
8:17:09example that we're not going to use
8:17:10later on I recommend just opening up a
8:17:13new workbook for this now coming into
8:17:15the data tab I can come down to get data
8:17:18and they do have this option right here
8:17:20for Combined queries merge and also
8:17:22append but this is for append two
8:17:25queries from within in this workbook
8:17:27it's basically assuming you've already
8:17:29imported it in so instead what we need
8:17:32to do is actually go to from file and
8:17:34actually start our first query of
8:17:36connecting to that Excel workbook with
8:17:38all those different sheets navigating to
8:17:40the course underneath resources data
8:17:42sets and then here down on data job
8:17:44salary monthly I'll select that select
8:17:46Import in the Navigator we can see all
8:17:48the different sheets that are available
8:17:50we want to actually do enable this of
8:17:52select multiple items and then go
8:17:54through and select all the items with
8:17:56all these loaded we're going to then
8:17:58shift into not just loading it we want
8:18:00to actually go into the power query
8:18:01editor so I'm going to select transform
8:18:02data and it's going to start by loading
8:18:04each one of those sheets and just going
8:18:06to be naming each one of the queries
8:18:08respectively after those sheets with
8:18:10power query editor launched we can see
8:18:12over here in the left hand pan all 12 of
8:18:15those queries for each of the months so
8:18:17these are all their separate own queries
8:18:20because of that we need to now move into
8:18:22actually appending them and make it one
8:18:25final query that we can actually export
8:18:27into or import into Excel so underneath
8:18:29the Home tab they have the option for
8:18:32combine append queries they have appen
8:18:34queries and append queries is new with
8:18:36the January query selected I'm going to
8:18:39go to append queries and for this I can
8:18:42say either do two tables and specify the
8:18:45table I want to do we're going to do
8:18:46three or more cuz we want to do all of
8:18:48them with them all selected I'll now go
8:18:50through and click okay to append now
8:18:52this inserted a step of appended queries
8:18:56inside inside of that January query so
8:18:58now that January query is all those
8:19:01different data sets so I just want to
8:19:03verify that I got all the data in here
8:19:05right now if we scroll down well I'm
8:19:06just going to show it right here we're
8:19:07only showing column profile based on the
8:19:09top 1,000 the fastest way to actually
8:19:12find this out is just go to the
8:19:13transform Tab and go to count rows which
8:19:17it tells me there's 36,000 rows which
8:19:19it's a few thousand too many and if I go
8:19:22back into the appended query option and
8:19:25actually look into it I can see in the
8:19:27formula bar we have August in here I
8:19:29accidentally selected it twice so I'll
8:19:31go ahead and delete it and then look at
8:19:33the counted rows that's actually what I
8:19:35expect the value to be around 32,000
8:19:37anyway that was just to count the rows
8:19:39additionally I don't want the append
8:19:41query to be inside of that January query
8:19:43so I'm going to delete this step as well
8:19:45instead with the January query selected
8:19:48I'll go back to that home append queries
8:19:51and then select append queries as new
8:19:55this is going to create a completely new
8:19:57query once again we want to do three or
8:19:59more tables this time I'm going to hold
8:20:01control and select all of them and then
8:20:04move them over at once make sure we
8:20:06don't have duplicates this time so this
8:20:09now starts a new query right now it's
8:20:11called aend one I would probably name it
8:20:12something like data jobs all and then
8:20:15pressing enter it then loads in here but
8:20:17you can see these queries like imagine
8:20:19the case where I right now we have 13
8:20:21queries I want to organize these a
8:20:23little bit better so we can actually
8:20:25group these specifically we can group
8:20:26these monthly ones I selected April and
8:20:29then holding control selecting all the
8:20:31other queries as well then right clicked
8:20:33it and I'm going to select this option
8:20:35to move to group we need to have a new
8:20:38group and I'll call this real uniquely
8:20:41data jobs
8:20:42monthly and click okay so now we have
8:20:45these two folders one with data jobs
8:20:47monthly I'm going to close that down and
8:20:49then there other queries which we've
8:20:50seen before and there's one query inside
8:20:52of this of data jobs all this cleans it
8:20:54up also you may get this disclaimer up
8:20:56here the preview may be up to 33 days
8:20:58old feel free to refresh it if you've
8:20:59been getting that should have no effect
8:21:01on your data then if we wanted to we
8:21:03could go through and actually Analyze
8:21:05This by pressing close and load to I
8:21:07pretty maturely selected close and load
8:21:10I recommend you select close and load to
8:21:11anyway nonetheless I'll go to the data
8:21:13jobs all we'll go to load to
8:21:16specifically I want to look at a pivot
8:21:17table I know there's going to be some
8:21:19data loss because it's going to remove
8:21:20the data in the sheet and then I can
8:21:22inspect that job posted date
8:21:24specifically for the account dragging
8:21:27job post date into the rows and then
8:21:29also dragging job posted date into
8:21:31values and once again this is why we
8:21:33double check it this time it looks like
8:21:36I accidentally imported in January twice
8:21:40with this as we can see that it's
8:21:4235,000 anyway opening up that power
8:21:45query editor going to the data jobs all
8:21:47query and updating it to remove that
8:21:50second January that I should have caught
8:21:51from before and then close and loading
8:21:54it and now it should refresh and update
8:21:56for these these correct values boom so
8:21:59now it's actually aligned with what I
8:22:00expect to see this why we always double
8:22:02check any type of query or analysis you
8:22:05do this double check of the work is
8:22:07going to save your
8:22:10butt all right let's now get into the
8:22:12bulk of this lesson I'm moving into
8:22:14merge for this feel free to continue
8:22:17working with that workbook that you were
8:22:19working with in the last lesson if you
8:22:22didn't Happ to save it or you got lost
8:22:24you can use the advanced transform
8:22:26workbook from the last lesson that'll
8:22:28pick right right back up where we left
8:22:29off and then as usual the append and the
8:22:32merge are the final examples that you're
8:22:34going to see at the end of this which
8:22:36specifically for append you've already
8:22:38saw so let's actually get into merging
8:22:40those queries for this I want to press
8:22:41alt F12 and right now we have three
8:22:45queries in here the data job salary
8:22:47which is basically like our fact table
8:22:49this includes all of our data going into
8:22:52transform and count rows we have as
8:22:55expected around 32 data point points I'm
8:22:56going go ahead and delete that Stu
8:22:58similarly we have this data jobs skills
8:23:01which has all of our skills in it let's
8:23:03see how many rows are in this by going
8:23:05up to transform and to count rows and
8:23:07this has
8:23:10167,000 now it's important to understand
8:23:12these numbers because we're going to be
8:23:13using them or need to understand them
8:23:15whenever we actually get into the joins
8:23:16to see when we have missing or more data
8:23:19so I'm going go ahead and delete the
8:23:20step of counted rows as well we don't
8:23:22need it then we have also this final
8:23:24query of data job skills count this was
8:23:27made as an example only we're not going
8:23:28to use this any further into the future
8:23:31so I'm actually going to go ahead and
8:23:33just delete this to minimize my queries
8:23:35it's going to ask them I'm sure want to
8:23:37delete it yep so let's get into merging
8:23:39these queries I have data job salary
8:23:40selected come up to the Home tab under
8:23:43merge queries we're going to have merge
8:23:45queries and merge queries as new like we
8:23:48learned from the append of appen queries
8:23:51and appen queries is new we're going to
8:23:53want a new query so that way we still
8:23:55have these Source queries so I'm going
8:23:57to go merge queries as new with this
8:23:59this merge window pops up and it says
8:24:01select the tables and matching columns
8:24:04to create a merge table specifically we
8:24:07want to go with the data jobs salary and
8:24:10we want to merge it on the job ID that's
8:24:13why we created that a few lessons ago
8:24:15we're trying to connect to the data jobs
8:24:18skills on also that job ID now down here
8:24:23underneath this there's a join kind and
8:24:26there's six different options from this
8:24:29of left outer right outer full outer
8:24:31inner left anti and right anti now Kelly
8:24:34put together this fancy chart that shows
8:24:37visually what is happening with these
8:24:40merges and we're going to be walking
8:24:43through all of these briefly in order to
8:24:46understand which type of join you should
8:24:49be choosing depending on which scenario
8:24:52you're in as a quick overview these
8:24:54circles are signifying the two different
8:24:56tables so in this case table a and table
8:24:59B and the Shaded Blue Area shows what
8:25:03portion of the contents from those
8:25:06tables will be included in the final
8:25:09table first up is a left outer join and
8:25:13with this join what's showing here is
8:25:16that all rows from table a will be
8:25:19included in the final table and then
8:25:22from that Center portion right there
8:25:24where A and B overlap this signifies
8:25:26that it's only going to keep items from
8:25:29table B that are in table a or match
8:25:33with table a so what does it actually
8:25:35mean so if we go here into join kind and
8:25:37select left outer and then what we get
8:25:40told based on this next to this check
8:25:42mark is the selection matches 29,000 of
8:25:4532,000 rows from the first table so what
8:25:49are those missing jobs well basically
8:25:51there's some jobs that don't have a
8:25:52skill now this isn't necessarily a bad
8:25:55thing although we're not going to go
8:25:56with this join this could be an option
8:25:58we could use I'm going to click okay to
8:26:00load it in so right now we have it under
8:26:02this query called merge one and as you
8:26:04can see there's not repeating any job
8:26:07IDs basically we have the original dat
8:26:10jobs salary table and then we scroll all
8:26:12the way to the right we have the data
8:26:14job skills over here and if you see each
8:26:17one of these items is a table if I click
8:26:20on it and expand it to see hey what's in
8:26:22this table we can see that for this one
8:26:25there job posting or job ID of 10,000
8:26:28And1 this is the table associated with
8:26:31it so I'm going to go ahead and actually
8:26:32delete out of this step and go back to
8:26:35it so what we could do is expand it out
8:26:38and there's this icon up in the top
8:26:41right hand corner I'm going to go ahead
8:26:42and click it and it's going to ask me
8:26:45how it wants to basically expand out and
8:26:49in this case I already have the job ID I
8:26:51already have job title short I would
8:26:52expand it by job skills so now seeing
8:26:56how these skills are broken over I can
8:26:57actually scroll all the way over and see
8:26:59that now 10,000 And1 ID is duplicated
8:27:02multiple times and if actually looked at
8:27:04the number of rows within this data set
8:27:07this new data set we have 170,000 rows
8:27:12now technically this merge has exactly
8:27:14what we want but we still need to go
8:27:16through those other merge examples to
8:27:18understand them so we're going to show
8:27:20them as well now for this I want to go
8:27:22back to that merge window and I'm going
8:27:24to click the settings icon I need to get
8:27:26rid of the step we're going to be trying
8:27:27out different types of merges so I'm
8:27:29going to xit out and then go in here and
8:27:31click the gear icon now it's popping
8:27:33back up we did left outer next thing
8:27:35we're going to look at is Right outer
8:27:37for right outer this takes all of the
8:27:40rows out of table B and then from there
8:27:43any that match those rows in table a are
8:27:47included now this one when we look down
8:27:49here it says Hey the selection
8:27:51matches
8:27:53167,000 of 167,000 rows from the second
8:27:57table if you recall back from that left
8:27:59outer we had
8:28:01170,000 so 3,000 higher why is that well
8:28:05that table a or data job salary has
8:28:083,000 roles in here that don't have any
8:28:10skills listed hence why 3000 is less
8:28:14this provides a similar type of merge
8:28:17that we did before where we need to
8:28:18actually go over to that data job skills
8:28:20and expand it out selecting the job
8:28:23skills column and with this table we can
8:28:25just check that we have 167,000 rows
8:28:28which bam we confirm all right I'm going
8:28:30to get rid of these two steps we're
8:28:32going to move into the next merge next
8:28:34is inner join and this provides only
8:28:37matching rows from table a and matching
8:28:41rows from table B so depending how
8:28:43you're join it there could be missing
8:28:45data on both A and B for this one it's
8:28:47saying hey the selection matches about
8:28:4929,000 of 32,000 rows from the first
8:28:51table which what we expect and then
8:28:54basically all of the rows from the
8:28:56second table so this one if actually go
8:28:58into it and then expand out those data
8:29:01job skills looking only at the job
8:29:03skills column with it expanded out
8:29:05actually counting the rows we have once
8:29:08again 167,000 so missing that 3,000 of
8:29:11jobs that don't have skills next is left
8:29:14anti and in this case it checks to see
8:29:17what matches it doesn't have and Returns
8:29:20the value for that specifically for
8:29:22table a whichever values don't have a
8:29:24match it's going to return that so in
8:29:26this case it says the selection excludes
8:29:2829,000 out of the 32,000 when I go to
8:29:31load it I get the rows from table a or
8:29:34data jobs salary and it still has the
8:29:37data job skills but actually if I looked
8:29:39into here right we should be matching on
8:29:41things that don't match or don't have a
8:29:44value specifically there shouldn't be
8:29:46inside anything in this table that I'm
8:29:47clicking on and as expected they're null
8:29:50values because it doesn't have skills so
8:29:53exiting out of navigation going back to
8:29:54Source counting these rows we can see
8:29:57that we have 3,000 jobs basically with
8:30:01no skills for right anti this gets rows
8:30:04from the right table that do not have
8:30:06matches in the left table and for this
8:30:09with right anti- selected this selection
8:30:11excludes 167,000 out of 167 rows from
8:30:14the second table so basically everything
8:30:17from this table is included we're not
8:30:19going to walk through this in the power
8:30:20query cuz this is also not what we want
8:30:22the final one we're going to actually
8:30:23use is a full out join from this it
8:30:27takes all rows from table a and all rows
8:30:30from table B and if there's a match it
8:30:32will join those two if there's no
8:30:34matches it's still going to return them
8:30:35in the table it will just be a null
8:30:37value for where it doesn't match up and
8:30:39this talks about how basically selection
8:30:41matches 29,000 of 32,000 rows from the
8:30:44first table and all the rows from the
8:30:45second table loading this in once again
8:30:48we have data job skills we need to
8:30:50expand out and we only want to expand
8:30:52out those job skills and then from there
8:30:54just going to do a double check I'm I'm
8:30:55going to do count rows and this has
8:30:59170,000 rows in it so similar to our
8:31:03left outer we could have done either of
8:31:04these these are one the twos that we
8:31:06want but I'm going to stick with this
8:31:08one of the full outer because I have all
8:31:09the work here any I'm going to close out
8:31:11the step and I think that's a great
8:31:13example of sometimes there may be
8:31:15multiple joins that fit the example it's
8:31:18important that you go through and
8:31:19actually count the rows and understand
8:31:22the data set to figure out which one you
8:31:23need to use and for what purpose anyway
8:31:26one thing I glossed over real quick
8:31:27going back to source and that gear icon
8:31:30is right underneath this underneath the
8:31:32join kind they have used fuzzy matching
8:31:35to perform the merge right now we're
8:31:37doing basically exact matching as the
8:31:39job ID of 10,1 we're matching up exactly
8:31:42with the 10,1 from the other table fuzzy
8:31:45matching allows you to connect to tables
8:31:47that have basically non-exact matches so
8:31:50in this case we have table a with a
8:31:52student ID and a student's name and only
8:31:54their first name but then in table B we
8:31:57have the student name full so first and
8:32:00last name and the grade with the fuzzy
8:32:03matching we could merge table A and B
8:32:07based on that student name First Column
8:32:09and the student name full column now
8:32:12what happens if we get to where we have
8:32:13students with multiple similar first
8:32:16names it's going to create a hot mess so
8:32:18I don't always recommend using this
8:32:20unless you know the data and you know
8:32:21you're going to cause complications with
8:32:23it so that was a quick overview of of
8:32:26the different joins within power query
8:32:29if you want a more indepth tutorial for
8:32:32how this is done then and you can check
8:32:34out my SQL tutorial where I go through
8:32:36it with all the different SQL analysis
8:32:37that we do in that course and break it
8:32:39down step by step I'll include a link to
8:32:41that video right here for you to go and
8:32:43see
8:32:46it all right so we have the final table
8:32:49that we actually want for this remember
8:32:50these do have duplicate values in it so
8:32:52you have to keep that in mind anytime
8:32:53you're doing analysis I'm going to
8:32:55rename this as data jobs merged one last
8:32:59thing for close and load we have this
8:33:01job skills column which is sort of
8:33:02redundant right now because we actually
8:33:04have the data job skills not job skills
8:33:07the actual skills itself so I need to
8:33:09get rid of this column I actually want
8:33:10to do this I'm going to do this in the
8:33:13source step before we even break this
8:33:15out so I'm going to select job skills
8:33:17and select remove columns it's going to
8:33:19ask if I want to insert the step which I
8:33:22do and then after we remove the columns
8:33:25we go into expanding it out and because
8:33:29we did it in that order I can actually
8:33:30come in here instead of renaming it here
8:33:32I can just rename it via the formula
8:33:36inside of expanded skills and just
8:33:37change it to job skills and Bam now I
8:33:41only added two steps Vice one all right
8:33:44go ahead now we're going to close and
8:33:47load two I'm going to want a pivot table
8:33:49and also pivot chart so I'm going to
8:33:50select the pivot chart option here and
8:33:53underneath quers and connections it's
8:33:54going to show that it's loading this in
8:33:56here under data jobs merged so let me
8:33:59show you what we're going to be creating
8:34:00with this I want to build this
8:34:01visualization that's showing what is the
8:34:03salary of the top 10 skills top 10
8:34:06skills by count for data nerds and this
8:34:09is a combo chart we're going to have not
8:34:11only the salary or the average salary
8:34:14for a skill but also for this line
8:34:17portion we're going to have the
8:34:19associated count for the number of
8:34:22skills that appears or how many jobs it
8:34:24appears in all right so I'm going to go
8:34:25ahead and move this pivot chart out of
8:34:27the way and select the pivot table
8:34:29remember we want to use the job skills
8:34:31we're going to be analyzing that so I'm
8:34:33going to throw in the rows the first
8:34:35thing I'm going to look at is the
8:34:36easiest is the count of these job skills
8:34:39and I'm going to rename this to job
8:34:42count along with changing the value
8:34:44field settings going to number format I
8:34:47want to change the number specifically I
8:34:48want to use a thand separator with zero
8:34:51decimal places I'll go ahead and press
8:34:52okay so we have a count now we want the
8:34:55average salary so I'm going to take
8:34:56salary your average drag it into the
8:34:59values right now it's doing a sum so
8:35:01I'll go into value field settings select
8:35:04average and then for number format we're
8:35:06going to do currency with zero decimal
8:35:09places click okay and okay again and I'm
8:35:12going to change this one to average
8:35:15salary and then specify the units of USD
8:35:19all right so now xing out of this and
8:35:21xing out of this now our pivot chart is
8:35:24sort of all jacked up well it is jacked
8:35:26up mainly it's trying to PR this as like
8:35:28a dual column chart and that's not what
8:35:30we want so we're going to change this
8:35:32design of it going to design change
8:35:35chart type I'm going to go over to combo
8:35:38and then underneath here for the combo
8:35:40for the job count I want that to be a
8:35:44line so I'm going to go up here and
8:35:45select line and for the average salary I
8:35:47actually want that to be the column now
8:35:50I want the job count on a secondary axis
8:35:53I don't want the same axis as the salary
8:35:56itself because they're just not
8:35:57proportional I'm going to go ahead and
8:35:59click okay I want to clean this up a
8:36:01little bit further by removing the
8:36:03legend and then also right clicking here
8:36:06and hiding all field buttons on this
8:36:08okay there's now there's still too many
8:36:10skills on here remember we want the top
8:36:1210 skills so going into the pivot table
8:36:15itself I'm going to come up into the
8:36:17filter into value filters and top one
8:36:22we're going to do top 10 items by job
8:36:24count all right this is getting a lot
8:36:26more readable now because I have the top
8:36:2910 by job count I want to order this
8:36:32from high to low by salary so I'm going
8:36:34to go to more sort options and we're
8:36:36going to do descending on average salary
8:36:40I'll click okay and Bam now we're
8:36:42getting somewhere so we're seeing things
8:36:44like spark and AWS have the highest and
8:36:47Excel did make the top 10 so it's on
8:36:49there at 100,000 other things I'm going
8:36:52to change selecting on this pivot chart
8:36:53is the actual design itself you know how
8:36:55I am about colors so we're going to
8:36:57change the colors I'm going to use this
8:36:59monochrom MAAC palette 8 I want the line
8:37:01to be a lighter color than the actual
8:37:03bars itself I'm going to go ahead and
8:37:05add access titles for primary vertical
8:37:07and secondary vertical for this I'm
8:37:09going just select the box go into the
8:37:11formula bar and say hey for this one
8:37:13make it equal to average yearly salary
8:37:15for this one selecting the Box going
8:37:17into the formula bar pressing equal I'm
8:37:19going to make it equal to job count I'm
8:37:21also going to add a title to this I'm
8:37:24going toall this of what is the salary
8:37:26of the top 10 skill of data nerds and
8:37:29remember this is for all data nerds so I
8:37:32want to be able to actually what's the
8:37:34great thing about this of joining these
8:37:35tables now we not only get salary data
8:37:37but we can get job title information so
8:37:39I'm going to add a slicer now but going
8:37:41in pivot chart analyze insert slicer add
8:37:45in that job title short only going to
8:37:47move that out of the way now I'm going
8:37:49to go to slicer I'm going to rename this
8:37:52to a more friendly title of job title
8:37:55and now now let's actually look at it
8:37:56for data analyst so with this looks like
8:37:59python arlor the highest Excel still
8:38:02makes that top 10 and for data analysts
8:38:05at
8:38:0686,000 it's also if we look at this it's
8:38:09the second most important skill behind
8:38:12SQL which has a value of 96,000 let's
8:38:17see what it is for a business analyst
8:38:19once again SQL and Excel are two of the
8:38:21highest and for business analysts Excel
8:38:23is paying 87,000
8:38:26so bam we just showed the power of well
8:38:28append but also more specifically merge
8:38:31we can now take this analysis to another
8:38:33level analyzing skills to other data
8:38:36points from our main fact table or that
8:38:39data jobs salary table that has all of
8:38:41the data in it so now you have some
8:38:43practice problems to go through and get
8:38:45more familiar with using both a pend and
8:38:48also merge after that we'll be jumping
8:38:50into the last lesson of power query
8:38:53focusing on the M language as I warned
8:38:55at the beginning don't worry if you
8:38:57don't have coding experience or anything
8:38:58like that we're going to be taking it
8:38:59nice and easy and you're going to be
8:39:00able to follow along and fill it out
8:39:02pretty easily we're going to be doing
8:39:04some final prep before we finally send
8:39:06this data set on over to power pivot
8:39:08which we're going to cover in the next
8:39:09chapter all right with that I'll see you
8:39:15there welcome to this final lesson on
8:39:17the M language and we're going to be
8:39:20going into some pretty Advanced
8:39:22Techniques and understanding how to read
8:39:25and better utilize the M language in
8:39:27building your power query queries anyway
8:39:31nothing in this lesson is going to be
8:39:34used that we actually go through and do
8:39:36used to build on our project so if any
8:39:39time you're not following along or
8:39:40you're not able to do anything don't
8:39:42worry too much nothing's actually be
8:39:44used it's more to inform you about the M
8:39:47language so you get more familiar with
8:39:48it as a disclaimer you will not be an
8:39:51expert on M language you not be able to
8:39:52code in M language after this mainly
8:39:54you'll just be able to look look at it
8:39:56understand what's going on there from
8:39:57there and make slight adjustments if
8:39:59necessary feel free to continue working
8:40:01on in that worksheet that you've been
8:40:03using previously where we just
8:40:05calculated in the last lesson looking at
8:40:07the top 10 skills and what the salary is
8:40:09for them however if you got lost or
8:40:11wasn't able to follow along or just
8:40:13starting over feel free to use this
8:40:15merge notebook don't use once again that
8:40:18M language one that one's going to be
8:40:20what is going to be done at the end of
8:40:21this lesson so what are we going to be
8:40:23covering in this lesson well if you open
8:40:25up the power query editor we can
8:40:27navigate into it we're going to be
8:40:28covering three main things first is the
8:40:31Z Advan editor actually walking through
8:40:34a previous query and understanding how
8:40:36to read it and then from there under add
8:40:38column tab we're going to go into these
8:40:41different examples on creating custom
8:40:44columns and also custom
8:40:48functions so what exactly is this m
8:40:52language well if we dive in
8:40:54documentation we can see that the power
8:40:56query engine uses a scripting language
8:40:59behind the scenes for all power query
8:41:01Transformations the power query M formul
8:41:04language also known as M so although
8:41:07we're doing all these edits inside of
8:41:08this power query editor behind the
8:41:10scenes if we navigates something like
8:41:12the advanced editor it's actually using
8:41:15this m language right here to carry out
8:41:18all the Transformations and it goes on
8:41:21to say if you want to do Advanced
8:41:22Transformations using the power query
8:41:24engine you can use the advanced Editor
8:41:26to access the script of the query and
8:41:28modify it as you want it even goes on to
8:41:31discuss that if you're not finding what
8:41:33you need in the actual GUI or the
8:41:35graphical unit user interface of the
8:41:37power query editor you can use the M
8:41:39language editing it in the advanced
8:41:40editor for
8:41:44this so let's go into breaking down this
8:41:46m language more by going to that data
8:41:48jobs merge and entering the advanced
8:41:50editor and we're going to be just
8:41:52breaking down this simple query right
8:41:54here up here on the right hand side
8:41:56there's a few different options display
8:41:57options I'm going to do this render Whit
8:42:00space basically it shows me the
8:42:01indentation that's going on here right
8:42:03now I'm seeing that there's four spaces
8:42:05in here anyway the key thing here is
8:42:08we've have first have this let keyword
8:42:10and then in keyword this Begins the
8:42:14basically definition block if you will
8:42:16this whole portion right here for
8:42:18defining different variables and
8:42:19specifically different tasks if we look
8:42:23we have things like source expanded data
8:42:26job skills sorted rows remove column
8:42:28remove columns if I go ahead and move
8:42:31this over to the right those applied
8:42:33steps are the same thing those are the
8:42:36variables itself I currently have enable
8:42:39word wrap enabled and I'm not liking the
8:42:41format and how it looks I'm going to go
8:42:42ahead and unclick that finally we have
8:42:44the in keyword and then this displays
8:42:48the final value that we want to appear
8:42:52for our query so in this case we want
8:42:54the final value of rename columns or the
8:42:56last applied step to be what appears now
8:43:00this Advanced ER I'm going to expand it
8:43:01back out again is also a syntax Checker
8:43:04so in this case let's say I deleted this
8:43:07quotations at the end of this rename
8:43:08columns it's going to one it's going to
8:43:10give me these red squiggly lines to say
8:43:11that hey there's something wrong here
8:43:13and two it's going to actually give you
8:43:14an error of invalid identifier and so we
8:43:18would probably know that we probably
8:43:19need to fix this so we're not going to
8:43:21be breaking down much more of the
8:43:22formulas here but I do want you to spot
8:43:25two main things from this the first
8:43:27thing is this column names column names
8:43:30are always put in quotes in here and
8:43:34conveniently they're also highlighted in
8:43:36here so if you needed to do any changes
8:43:37to column names or see what's happening
8:43:40that's one quick way to identify it the
8:43:42next thing is this every step that is
8:43:44taken refers to the previous one what do
8:43:47I mean by this so this first step is
8:43:49assign the valuable variable of source
8:43:52and I know it's assign this variable
8:43:53because it has an equal sign right next
8:43:54to it
8:43:55and then whenever we go to the next line
8:43:57of expanded data job skills inside this
8:44:01function of table expanded table column
8:44:04it references source which if I scroll
8:44:07over it I can see that it's giving me
8:44:09the same formula for source which is
8:44:11right above it so basically it's
8:44:12plugging right into it similarly this
8:44:15expanded data job skills is going to be
8:44:18located in the next one below it on
8:44:20sorted rows and it's going to be the
8:44:21first value in here for this table
8:44:23sorted and if you're curious about what
8:44:26these different functions are doing you
8:44:27can just scroll over it as well in this
8:44:28case table. sort sorts the table using
8:44:31one or more Columns of names and
8:44:32comparison criteria and it tells us via
8:44:35the syntax inside the parentheses that
8:44:37the first parameter is table is table so
8:44:39it takes that previous variable which is
8:44:41a table anyway one minor last thing
8:44:43about this if you notice these are
8:44:45surrounded by these variables have a
8:44:47hashtag and then double quotes on each
8:44:49side and that's because they have white
8:44:51space in the actual names that we're
8:44:54doing for this in the case of source
8:44:56there's no white space it's only one
8:44:58value with no white space so it doesn't
8:45:00need to have this around it anyway why
8:45:02am I yaen about all this stuff if you
8:45:03need to understand this m language
8:45:05anyway we're going to actually create
8:45:07this data jobs merge query I'm going to
8:45:09select it all press contrl C to copy it
8:45:12then from there I'm going to close out
8:45:14of it we're going to now create a new
8:45:16query so underneath the Home tab I'm
8:45:18going to go to new source I'm and then
8:45:21under that other source and I'm just
8:45:22going to go into blank query okay right
8:45:25now this is completely blank but I can
8:45:28go into that advanced error of query 1
8:45:31and it has the let and instill and
8:45:34obviously nothing going on here what I
8:45:37can do is just highlight this all and
8:45:39then using contrl V paste all of that
8:45:43other query into this now when I press
8:45:47done it goes through and actually
8:45:51creates that same exact query from data
8:45:53jobs merged now it could could have gone
8:45:55through and right click data jobs merged
8:45:57and click duplicate but this is more of
8:45:59to show that you can actually go in copy
8:46:02queries or copy portions of queries and
8:46:05thus paste it into other ones which
8:46:07we're going to do in a little
8:46:11bit so let's get into more of learning
8:46:13about the M Language by actually
8:46:15cleaning up this query one that we just
8:46:18created by using this column from
8:46:20example first thing though I do want to
8:46:22rename this query one this is the one
8:46:24we're be working with for the remainder
8:46:26of this lesson and I'm going to call it
8:46:28data jobs clean because that's what
8:46:29we're going to do we're going to clean
8:46:30it up so we have four major tasks that
8:46:33we're going to do with this the first is
8:46:35for job schedule type I just want to
8:46:37extract out the first value out of here
8:46:39that's full-time out of it additionally
8:46:41we're going to be using the date and
8:46:42date time columns to extract the weekday
8:46:45and also the hour of the job postings
8:46:48and then finally we're going to do some
8:46:49data cleanup on this job title column
8:46:52that frankly is a mess specifically
8:46:54we're going to move job postings that
8:46:56have this parentheses remote around it
8:46:59anyway let's start with this first one
8:47:00of this job schedule type if I go into
8:47:03view and then look at the column profile
8:47:05it looks like we have that full-time
8:47:07contractor part-time and whatnot but we
8:47:09have a lot of combines of full-time and
8:47:12part-time contractor and temp work
8:47:13full-time parttime and internship I
8:47:15basically want to go through and just
8:47:16extract out what is the first value that
8:47:19appears in here so in the case of this
8:47:21full-time and parttime just want to
8:47:23extract full-time contractor and temp n
8:47:24work only contractor so under add column
8:47:27and then column from example we'll do
8:47:30from selection and this appears at the
8:47:33top of add column from examples enter
8:47:35sample values to create a new column
8:47:38control enter to apply so I'll first go
8:47:40by entering fulltime and it's already
8:47:43picking it up I'm just going to type it
8:47:45in first okay and then I'm going to
8:47:47scroll down but in this case I'm going
8:47:49to put in hey I want full time for this
8:47:54one this is the example remember so now
8:47:57it's cleaning up that let's scroll down
8:47:58further if it's done this fully for even
8:48:00more okay it's getting the first of
8:48:02these and you might think that this is
8:48:04correct but the problem we're running
8:48:06into now is if we go down to this one
8:48:09where it says contractor it's only
8:48:12contract do and just looking at the
8:48:14formula this is the formula it's
8:48:16generated so far it's doing teex start
8:48:19and nine I don't really know too much
8:48:21what's going on here but I'm assuming
8:48:22that it's taking the first nine values
8:48:24that's not I want so inside this
8:48:26contractor one I'm going to type in
8:48:29contractor with an R so that way it
8:48:31hopefully fixes this so this is good and
8:48:34now it has text before delimiter and a
8:48:36space so I'm going to go ahead and click
8:48:39okay to load this in so let's scroll
8:48:42down to just inspect it to make sure
8:48:44that we have this correct and an easier
8:48:47way instead of scrolling down and trying
8:48:48to find something I can just use this
8:48:49drop down right here and look in here
8:48:52and it looks like we're good
8:48:55except for we now have a comma here
8:48:59specifically I have a fulltime and then
8:49:01a full-time comma so what's going on
8:49:03here well for values that have more than
8:49:06two so three they actually insert a
8:49:08comma in there and when we inspect our
8:49:12formula opening up the formula bar here
8:49:14it's only checking for a space so the
8:49:18easiest way to fix this is actually just
8:49:21like we did before we're pretty familiar
8:49:23with it let's go to the trans form Tab
8:49:25and then under replace values we want to
8:49:27go to replace values specifically we
8:49:29want to find commas we want to replace
8:49:31it with a blank bam so now pulling down
8:49:34that drop down we don't have multiple
8:49:37different full times we just have that
8:49:38single one without the comma we have
8:49:41what we want all right we're going to
8:49:42rename this and I can just go ahead and
8:49:44double click this and rename it but I'm
8:49:45actually going to do something first I
8:49:47see that I have the step already for
8:49:49renamed columns so I'm going to take
8:49:51that and I'm going to drag it to the
8:49:53Bottom now with rename columns as the
8:49:55last step I'll then rename it to job
8:49:58schedule type first press enter and then
8:50:00it inserts it into that current step as
8:50:02we can see from here cuz we're now
8:50:04familiar with it and we don't have
8:50:05multiple rename columns in there and
8:50:08then finally you know how I get about
8:50:10column ordering this job schedule type
8:50:12first I want it next to the job schedule
8:50:14type so I'm going to drag this on over
8:50:16here see how long it takes and we've
8:50:19moved it over and we now have this new
8:50:21step of reordered columns all right
8:50:23let's look at some other quick examples
8:50:24for column from examples for this we're
8:50:27going to be using the job posted date
8:50:29for this using column from example I'm
8:50:31going to select from selection now with
8:50:34some of these things whenever I type in
8:50:36this box I want to get let's say the
8:50:38year in this case if I were to type in
8:50:40four one it would pop up that hey with
8:50:44all these different options we can do
8:50:45and so this provides a lot of different
8:50:46options as far as okay I do know if I
8:50:49wanted to do the month I could do that
8:50:51and pressing enter it's going to copy it
8:50:53all the way down that's not what I
8:50:54wanted this case though I'm going to
8:50:55double click it again go
8:50:572023 and scrolling down and looking
8:51:00through this this option here of year
8:51:03from job post to date so we're going to
8:51:04go with that then press enter and
8:51:07looking at the transform we can see what
8:51:09is the m language code that it used for
8:51:11this it used the date and year function
8:51:14putting in job posted date this is what
8:51:16we want we'll click okay you know I I'm
8:51:19with naming so we're not going to keep
8:51:20this named year so I'm going to modify
8:51:22this m language to be job post posted
8:51:25year with that renamed let's actually
8:51:27move over to our other example
8:51:29extracting out the hour for this we're
8:51:31going to be using that job posted
8:51:32datetime column column from example from
8:51:35selection in this case I want the hour
8:51:37out of it so I'm just going to put
8:51:38something like nine and we can see that
8:51:42we also have this here for hours from
8:51:43job post to date time I want that one
8:51:46press enter again inspecting the M
8:51:48language formula it's extracting the
8:51:50hour out of this one I'm good with it
8:51:52I'm also seeing the other values are
8:51:53updating correctly I'll click okay and
8:51:56we have our new column called hour which
8:51:58you know me we're going to fix this an
8:52:00updated hour to job posted hour press
8:52:02enter all right now we got it so you're
8:52:05probably like look I already know how to
8:52:07go something like the transform Tab and
8:52:08already extract out that information
8:52:10using these functions that we used
8:52:12before well that was mainly as a primer
8:52:14for this next example we're going to be
8:52:16doing and that's that with this job
8:52:18title column there's some job titles in
8:52:20here that have a lot of sort of
8:52:22frivolous information that we don't need
8:52:24like in this case supervisor information
8:52:26technology specialist and then
8:52:27parentheses it has associate director I
8:52:29don't need anything in parenthesis
8:52:31similarly for this for the senior data
8:52:33engineer I don't need this remote in
8:52:34here so let's select this job title go
8:52:37into add column column from example and
8:52:41from selection for this first one with
8:52:43the associate director I'm going to
8:52:44select it so it appears below and then
8:52:46just highlight what I want press contrl
8:52:48C and then paste it in here then
8:52:51scrolling here through here to do a
8:52:53cursor check so I'm seeing that senior
8:52:55data engineer remotes in here I could
8:52:56select it and copy this down here
8:52:58another option is I just go in here
8:53:00double click it since it's now
8:53:02populating and delete out that remote
8:53:06press enter and it looks like it's doing
8:53:09this it's getting the text before the
8:53:11limiter job title specifically before
8:53:13the parenthesis and looks like in this
8:53:16case University grad data scientist PhD
8:53:18only now hiring it removed all that okay
8:53:21so this is now doing what we want click
8:53:23okay and I don't want I want to call
8:53:24this column text for delimiter I want to
8:53:26call this job title clean pressing enter
8:53:31all right so last thing I want to now
8:53:33clean up these columns and you know how
8:53:35I get I want the year an hour to be next
8:53:37to the date time the job tile clean be
8:53:39next to the job tiles I could drag and
8:53:41drop these I'm going to show you
8:53:42something else this reordered column
8:53:45step we're going to be modifying the M
8:53:47language for this and I don't want
8:53:50reordered columns to appear more than
8:53:51once so I'm going to take it once again
8:53:53and drag it to the very end now what I
8:53:56can do is take and modify this m
8:53:59language that we have in here now if we
8:54:02actually inspect this reordered columns
8:54:04it may do this or may not in my case it
8:54:06didn't add anything after job skills it
8:54:09basically let any new columns just fall
8:54:11towards the end so this job skills all
8:54:14these other columns after it aren't
8:54:15included which not a big deal so what I
8:54:18want to do is I want to move this year
8:54:20and hour to near job posted date and job
8:54:23posted month so I'll enter inside of
8:54:25here put in job posted year and also job
8:54:29posted hour make sure we're putting
8:54:31commas after both of those then I'm
8:54:33going to run this to make sure there's
8:54:34no issues with it and it looks like it
8:54:36moved it over inspecting next to job
8:54:39post a date we have our month and also
8:54:40year and hour all right the last one is
8:54:43this job title clean and I want this to
8:54:45be right after job title so I'll go
8:54:48ahead and put that in right here making
8:54:50sure to put a comma after that and then
8:54:53from there press ing this check mark up
8:54:55here to move it inspecting over we have
8:54:58job title clean right next to
8:55:02it our next to look at is custom column
8:55:06we'll go ahead and actually just select
8:55:08this and whenever we pull this up this
8:55:11tells us this allows us to add a column
8:55:13that's computed from the other column
8:55:15provides a box to basically put in the
8:55:17new column name but right here this is
8:55:19where we put in the custom column
8:55:22formula or the M language to maybe clean
8:55:25it up now let's start with something
8:55:27simple let's say I just wanted to repeat
8:55:29the job ID column I would come over here
8:55:31select job ID click insert it's going to
8:55:34put it in notice that the variable
8:55:37itself is inside of brackets and I'm
8:55:39going to rename this job ID repeat down
8:55:43at the bottom it's telling me that no
8:55:44syntax errors have been detected I'll
8:55:47click okay and then I get this new step
8:55:49for added custom and we can see hey it's
8:55:52job ID repeat scrolling over yep it
8:55:55repeated it if I want to go back in to
8:55:57edit it I'll press that settings icon
8:56:00and it's going to pull this back up so
8:56:02let's do something a little bit more
8:56:03complex now and it going to involve the
8:56:06salary year average column and that
8:56:08salary hour adjusted column go ahead and
8:56:11cancel out of this what I want is to
8:56:13create a new column that if there's a
8:56:15salary year average value it will
8:56:17basically be in that new column and then
8:56:19if there's a salary hour adjusted value
8:56:22it will be in that column instead
8:56:24just as for warning anytime salary year
8:56:27average is null there's always a value
8:56:29for salary hour adjusted and vice versa
8:56:32so like I said we're not going to
8:56:33becoming coding experts with this so I
8:56:36recommend taking use of chat Bots like
8:56:38chat gbt gemini or whatnot lots of free
8:56:40options available out there anyway we
8:56:42have this prompt of generate a power
8:56:44query formula for a custom column on
8:56:46building make the column salary your
8:56:48average if it's not blank otherwise it
8:56:50is salary hour adjusted now it's giving
8:56:54do the entire M language right this is
8:56:56what we providing to the advanced editor
8:56:58providing that previous step name what
8:57:00column we're using everything like that
8:57:02I care about really this formula right
8:57:06here specifically everything after the
8:57:08each I'm going to copy this from if all
8:57:11the way to the end that's the actual
8:57:13code right here going back to the custom
8:57:16column I'm going to delete that job ID
8:57:18out of there I want to make sure that
8:57:20there's an equal sign still there and
8:57:22I'm going to paste this in
8:57:24and you can see from this this is just
8:57:26basically an if formula it's doing if
8:57:28salary year average is not equal to null
8:57:32then salary year average else perform
8:57:35salary hour adjusted down at the bottom
8:57:37we can see that no syntax errors have
8:57:39been detected so I'm going to go ahead
8:57:41and click okay so bam we now have this I
8:57:44did in that jav ID repeat value here so
8:57:47we're going to actually change that to
8:57:48rename that value to salary year
8:57:50combined and then clicking the check
8:57:53mark in order to to rerun that formula
8:57:55to update the column and you know I like
8:57:57have my steps in order so I'm going to
8:57:58grab reordered column and I'm going to
8:58:00drag it to the very end and for this one
8:58:02I'm just going to drag it over to salary
8:58:04hour adjusted right after it to salary
8:58:06year combined so now scrolling down just
8:58:08to double check it it looks like we got
8:58:10140,000 here 140,000 82,000 82,000 there
8:58:14so the formula filled out
8:58:19correctly so let's get into our final
8:58:21task so we've been working this data
8:58:23jobs clean data set we made this salary
8:58:26year combined which is pretty useful
8:58:28actually what happens now if we want it
8:58:30in something like data jobs merged what
8:58:33do we need to do to actually add it into
8:58:36here because we have everything we need
8:58:38for it specifically we have that salary
8:58:40year average and we have the salary hour
8:58:42adjusted columns well we could recreate
8:58:45it in here going through all those steps
8:58:47creating that if statement or we could
8:58:48just copy it out of the advanced error
8:58:50and bring it in here so I'm going to go
8:58:52back to data jobs cleaned and then under
8:58:54home Advanced editor I'm going to go and
8:58:57find the step that's in here
8:58:59specifically it was this of added column
8:59:02and I'm going to copy it because I can
8:59:04see that hey it has the salary year
8:59:05combined in it I'm going to copy it all
8:59:06the way the the end and I'm going to
8:59:08copy it by pressing contrl C okay go and
8:59:11close out of this one and then bring
8:59:12over to data jobs merged go into the
8:59:15advanced editor and I want to insert it
8:59:17in right at the end so I'm going to go
8:59:20to at the end of this block of this let
8:59:22block going to press enter and then from
8:59:25there press contrl + V to paste it in
8:59:28now I'm already getting an error message
8:59:30and it's saying hey token comma un
8:59:34basically expected and it's not getting
8:59:36it if I scroll over I can see these
8:59:38squiggly lines right here basically
8:59:39there's not if we can see there's commas
8:59:42after every one of these variable
8:59:43definitions so I need to come up here
8:59:45put a comma in there next is this a
8:59:48comma cannot proceed an in so if we
8:59:50scroll over we can see this is red
8:59:53highlighted probably wrong not to have a
8:59:54comma here so we'll get rid of it now
8:59:56we're not done it's going to say there's
8:59:58no syntax errors but we didn't complete
9:00:01this remember you have to have the name
9:00:04of the it's got to reference the
9:00:05previous name here in it so if I tried
9:00:08to even though it says no syntax errors
9:00:10if I try to click done and go to load it
9:00:13I'm basically getting an error I can see
9:00:15this by this basically air Bo at the top
9:00:18of each one of these columns also
9:00:20there's only one applied step and it's
9:00:22calling it data job
9:00:24merged of the actual title itself but we
9:00:28need to fix this query and actually get
9:00:30it back to where it had multiple
9:00:31different applied steps so I'm going to
9:00:33go back to the advanced editor we're
9:00:34going to show what we did wrong here and
9:00:36that has to deal with remember we had
9:00:38before where we had something like
9:00:40remove columns you reference the
9:00:42previous column in it so in this case
9:00:44remove columns right there well rename
9:00:47columns is the last one we had I'm going
9:00:50to go ahead and copy this by control
9:00:51cing it but yet we have inex inserted
9:00:54text before delimiter one which is not
9:00:57correct so I'm going to select all of
9:00:59that and replace it by pressing crl +v
9:01:01so we have the rename columns now one
9:01:04other thing we have to do this last
9:01:06statement or and the in portion needs to
9:01:09be referencing that last variable of
9:01:11added custom so I'm going to go ahead
9:01:13and copy this contrl C and then pasting
9:01:17it in control V click done and now
9:01:20scrolling all the way over we can see
9:01:23that we have that salary year combined
9:01:25column that we created in the last query
9:01:27it's at the end we do need to move it
9:01:29over but it's in there nonetheless so it
9:01:31helps with understanding these queries
9:01:34now one quick thing before we go we've
9:01:35gone through basically every single
9:01:37thing in this chapter on power query up
9:01:40to this point with the exception of this
9:01:43invoke custom functions this basically
9:01:46invokes a custom function defined in the
9:01:48file for each row of this table this is
9:01:50more advanced and Beyond the scope of
9:01:52this course we're not going to be
9:01:53covering it but is available for you to
9:01:55dive into say you're doing a lot of
9:01:57different Imports and you need to
9:01:59automate the Imports that you do this
9:02:02would be a path you would go but for
9:02:04beginners like us I'm going to say stick
9:02:06away from it for the time being so this
9:02:08now wraps up on the M language and that
9:02:12was really a crash course and
9:02:15understanding how to use it by no means
9:02:16do you need be a professional or be an
9:02:19expert coder and codeing the M language
9:02:21if you got lost at any point in the way
9:02:22nothing to feel ashamed about this is a
9:02:24very pretty complex topic if you would
9:02:26like to learn more I do recommend this
9:02:29book which is M is for data monkey it's
9:02:31a good little read talking about not
9:02:33only Power query but also how to
9:02:35manipulate the M language I'll include a
9:02:37link in the description below anyway
9:02:39power query in my opinion is one of the
9:02:41most important features the most
9:02:43powerful tools within Excel and also
9:02:46powerbi and so it's worth your time
9:02:49investing and learning it and so this
9:02:51all culminates and we're now finalized
9:02:52covering power query in this chapter in
9:02:55the next chapter we're going be jumping
9:02:57into Power pivot and that's going to
9:02:59jumping into actually data modeling but
9:03:01before that for those that purchase C
9:03:03practice problems you have some practice
9:03:04problems to go through and get more
9:03:06familiar with that M language for
9:03:07proceeding forward all right with that
9:03:09see you in the next
9:03:13one welcome to this chapter on power
9:03:16pivot and this chapter consists of four
9:03:19different lessons where we're going to
9:03:21go an intro into Power pivot and over
9:03:23the wind window that it actually
9:03:24provides then from there looking into
9:03:27Dax or data analytical Expressions which
9:03:31is a Formula language very similar to
9:03:33excel formulas but before we actually
9:03:35jump into this lesson and going over
9:03:37what we're going for it we're going to
9:03:39focus on what exactly is power
9:03:45pivot so here I am in Excel and this is
9:03:48meant for me to just go through and
9:03:50quickly explain what is the power power
9:03:54of power pivot I know that pun is
9:03:56getting sort of old by now but it really
9:03:58is powerful if you're curious of looking
9:04:00at it it's in the workbook of power
9:04:02pivot intro part one part two is what
9:04:04we're going to be using for the actual
9:04:05lesson so in power query in the last
9:04:07chapter we end up clearing up our data
9:04:09set to have these two main tables versus
9:04:11data job salary which has the complete
9:04:13data set on all the data science job
9:04:15postings and then data job skills which
9:04:17is unique to the skills for a job we
9:04:20also created a data jobs merge table but
9:04:23that table is actually going to be well
9:04:25it's pretty much Obsolete and power
9:04:27pivot is going to help replace that and
9:04:29for good reason so what exactly is power
9:04:32pivot well it's an addin we're going to
9:04:33get to adding it in and it has a few
9:04:36different features that you can do
9:04:37within it such as accessing the data
9:04:39model adding measures kpis and whatnot
9:04:41this lesson is going to be going over
9:04:43this tab as a quick refresher power
9:04:46pivot is going to be available in
9:04:48basically any version of Windows for
9:04:50Microsoft past
9:04:522010 but it's completely not available
9:04:55in either the Mac version or the
9:04:57Microsoft online version so you won't be
9:04:59able to do this chapter if you have
9:05:01those versions or the final project
9:05:03anyway the core portion of power pivot
9:05:06is actually managing a data model and
9:05:09what's a data model well a data model
9:05:12defines how data is basically structured
9:05:15stored and also related in this case we
9:05:20have the data jobs salary table right
9:05:22here and we have the data jobs skill
9:05:25table what we can do with power pivot
9:05:27besides modeling these tables and
9:05:29showing how they're structured is the
9:05:31more important thing of creating a
9:05:32relationship in this case I created a
9:05:34relationship between the job ID of data
9:05:37job salary and that of data job skills
9:05:40and because I created this relationship
9:05:42I can look at things like the job title
9:05:44shot short column see how many jobs it
9:05:46has with it but also I can query across
9:05:50a table over to the job skills and see
9:05:52how many skills has with it in fact
9:05:54let's actually do that real quick here I
9:05:56have my data model itself I have my two
9:05:58tables which are shown anyway I can look
9:06:01at things like what are the count of the
9:06:03different job titles themselves I'm
9:06:05going to do that on job ID and like
9:06:07we've done plenty of times before here's
9:06:09the job count with a little clean up of
9:06:10the actual text here but now with power
9:06:13pivot I can actually reach across to
9:06:15that other table of data job skills and
9:06:17drag the job skills into here and this
9:06:20is telling us obviously the count of the
9:06:22skills based on the job title pretty
9:06:26cool that we can reach across the tables
9:06:27and do this now the other cool thing
9:06:29that power pivot unlocks is Dax or data
9:06:33analytical Expressions recall previously
9:06:35that we were using the average of the
9:06:38salaries and like we learned way back
9:06:40earlier in this Excel course we prefer
9:06:42actually a median salary but
9:06:44unfortunately looking at the value fied
9:06:46settings window here there is no option
9:06:50to actually pick median from this and
9:06:53that's where where Dax comes to the
9:06:54rescue with this I can go to something
9:06:56like the power pivot Tab and now create
9:06:59a measure which is where you actually
9:07:01insert in your Dax and I can create a
9:07:04new one called median salary and we're
9:07:06going to be using this Dax formula in
9:07:08this case I'm going to use the median
9:07:09formula very similar to the Excel
9:07:11formula and I can do it on the entire
9:07:14salary year average column here I'm
9:07:16going to format it real quick and then
9:07:18press enter anyway bam now we have
9:07:21because of the power of Dax we have the
9:07:23ability to get the median salary and
9:07:27those Dax things can do some pretty
9:07:29complicated calculations so in the case
9:07:31of here we have this job count and count
9:07:33of skills and we want to see what were
9:07:35the skills per job specifically in this
9:07:38case what is something like C2 / B2 and
9:07:42then dragging all the way down and
9:07:43filling it for all these this provides a
9:07:46much better analysis of what's going on
9:07:49with these values of counts and skills
9:07:51here when we get this proportionality we
9:07:53can create this with measures as shown
9:07:55in this final pivot table that we're
9:07:56going to be creating coming up in the
9:07:58third lesson of this chapter so in
9:08:01summary power pivot provides us the
9:08:03opportunity to now model our data which
9:08:06allows us to one create relationships
9:08:09and two allows us on unlocks these
9:08:12measures that we can create using
9:08:17Dax all right so let's get into this
9:08:20lesson what we're going to be focused on
9:08:21for well first thing is we're going to
9:08:22enable the power power pivot plugin and
9:08:24then from there actually getting in to
9:08:27data modeling or modeling our data that
9:08:30we imported through Power query after we
9:08:33have everything set up with our data
9:08:34model we're going to then move into
9:08:36performing our first analysis analyzing
9:08:39based on a job title how many different
9:08:42skills they have associated with it like
9:08:45I said we'll eventually get to that
9:08:46skills per job in an upcoming lesson so
9:08:49for this you can continue to work in
9:08:51that workbook that we were working with
9:08:54in the last chapter EMP power query
9:08:55we're going to continue work on that
9:08:57because we want to use those queries
9:08:58that we built if you got lost dur in the
9:09:00way and just want to start back up we're
9:09:02going to be starting from that M
9:09:04language workbook back in the power
9:09:05query chapter as a reminder these
9:09:08lessons or workbooks are what are the
9:09:11completed workbooks at the end of the
9:09:13lesson specifically for this lesson part
9:09:16one was just that intro part two is what
9:09:18will be done at the end of this lesson
9:09:24anyway here I am in the M language
9:09:25workbook we need to get into enabling
9:09:27power pivot right now you probably don't
9:09:29see Power pivot up at the top of the
9:09:30tabs so I'm going to go into file and
9:09:33then go down to options from here I'm
9:09:35going to select add-ins like we did
9:09:37before and instead of excel addins we're
9:09:40actually going to be using those Comm
9:09:41addins I'm going click go and they have
9:09:44three different ones available data
9:09:45streamer power map and power pivot we
9:09:47want Power pivot I'm go ahead and click
9:09:50okay now power pivot should appear up at
9:09:53the top all the way on the right hand
9:09:55side and should look something like this
9:09:58quick little overview of this tab manage
9:10:00here pops up the power pivot window
9:10:04which we're going to be doing a deep
9:10:05dive on this in the next lesson we're
9:10:08going to use it a little bit in this
9:10:09lesson but anyway that's one way you can
9:10:10actually access it you can also go to
9:10:13the data Tab and then here under data
9:10:16tools you should see it also and you'll
9:10:19be able to manage your data model and
9:10:22once again it will pop up the window
9:10:23additionally on this tab you have the
9:10:25ability to create measures and kpis
9:10:28which going to be diving deep into in
9:10:30the third and fourth lesson if you have
9:10:32a table within your worksheets you can
9:10:34add it to your dat model you can also go
9:10:36about detecting relationships although I
9:10:38don't find that this feature works that
9:10:40well and then finally they have settings
9:10:42and settings I don't really touch that
9:10:44much nor does it have much control
9:10:48here so let's actually get into EMB
9:10:51boarding some data into our data model
9:10:53we're going to do a simple example first
9:10:55here I created a new sheet made three
9:10:56columns of ID name salary and then
9:10:59different values associated with it one
9:11:01way I can add to the data model is if I
9:11:03have data in a table is to do this
9:11:05feature of add to data model in this my
9:11:07table has headers I'll go ahead and
9:11:09continue and then it will pop open power
9:11:11pivot a similar like environment will
9:11:14exist with Excel I can't actually edit
9:11:17any numbers in here this is just how
9:11:19you're modeling your data if you needed
9:11:21to actually edit it I have to go back to
9:11:22the sheets and like I said this isn't a
9:11:24method I typically use typically have
9:11:26bigger data sets not located in tables
9:11:28so I'm going to go ahead and rightclick
9:11:29this down at the bottom this table name
9:11:31of table two click delete it's going to
9:11:33say hey do you sure you want to delete
9:11:35this table and Bam it's gone all right
9:11:38so now there's nothing in our data model
9:11:40right now here we are still inside the
9:11:42power pivot window and if you've noticed
9:11:44from this in the Home tab right here it
9:11:47has the option to get external data they
9:11:50have options for you to actually connect
9:11:52Direct ly with power pivot to things
9:11:55like a SQL Server Microsoft Access you
9:11:58could also get it from some sort of data
9:12:00feed and then this option would be more
9:12:02probably useful in that it has a lot of
9:12:04different sources you could use such as
9:12:06other Excel files text files such as
9:12:08csvs and whatnot now you may be asking
9:12:11yourself I'm going to close out of this
9:12:12power pivot why would I import of that
9:12:15whenever we just went through with power
9:12:18query to get data via this when which
9:12:22time should I use which well it's very
9:12:25important to remember the purpose of the
9:12:28tool that you're using power query is an
9:12:31ETL tool extract transform and load we
9:12:35did a lot of Transformations with our
9:12:38data set and so that's really the power
9:12:41of power query and then it loads it in
9:12:45power pivot strengths is not in ETL or
9:12:47data cleaning instead it's in data
9:12:50modeling creating these relationships
9:12:51and Dax now now you may be tempted to
9:12:54come inside of existing connections and
9:12:56try to connect to specifically that
9:12:59salary and skills and if we went through
9:13:02like in the salary case and try to click
9:13:05open we're going to get an error message
9:13:07and I'll be honest this is really
9:13:09confusing because we have this workbook
9:13:11connections why isn't this working well
9:13:13it really just comes down to naming
9:13:15conventions and that the fact that power
9:13:17query connections are not the same as
9:13:19power pivot connections but we have a
9:13:20fix for this we just need to exit out of
9:13:22the power pivot window here inside of
9:13:25queries and connections remember you can
9:13:27get to that by going to the data Tab and
9:13:29going to queries and connections we can
9:13:30go to something like data job salary
9:13:32which right now is a connection only
9:13:34rightclick it and go to load to right
9:13:37now it's only under only create
9:13:40connection but we need to check this
9:13:42check mark of add this data to the data
9:13:46model I'm going to click okay it's going
9:13:48to go through this process of loading
9:13:50the data and now it talks about the rows
9:13:53are loaded but mainly if I go to the
9:13:56connection it has this new connection
9:13:59now of this workbook data model which if
9:14:02I go to and actually open up or manage
9:14:05our data model we can see that it's
9:14:07inside of here we have this basically
9:14:09sheet for the table itself of data job
9:14:12salary inside power pivot inside the
9:14:15data model now we do need to get that
9:14:17other pivot table or other table into
9:14:20there as well so I'm going go to queries
9:14:22data job skills s right click this load
9:14:24to and also add this to the data model
9:14:27okay it talks about 167,000 rows are
9:14:29loaded and another connections still
9:14:32it's only going to be one connection
9:14:34because we only have one data model in
9:14:35this case and now when I go to manage
9:14:38the data model I have two basically
9:14:41sheets down here but two tables and now
9:14:43we have the data job skills in
9:14:48here anyway I want to do some cleanup
9:14:50real quick I'm going to clean up power
9:14:51pivot but this data jobs merged and this
9:14:54data jobs cleaned it's going to be very
9:14:56confusing like I said we're not using
9:14:58this mainly for the fact that we have
9:15:01duplicate values in here for senior data
9:15:04scientists in this case and then for the
9:15:06salaries and so if we don't manipulate
9:15:08this in a correct manner we're going to
9:15:11get the wrong results so we're just
9:15:13going to get rid of these so for data
9:15:15jobs merge I'm going to write click and
9:15:17select delete and it's going to say hey
9:15:20should you want to delete data jobs
9:15:22merge yes I do and then I'm going to do
9:15:24the same thing with data jobs clean
9:15:26right click it and select delete also if
9:15:29you have these tabs down here for data
9:15:30jobs clean or merge you can go ahead and
9:15:32delete those as well with our models now
9:15:35cleaned up let's actually get into going
9:15:37over really briefly this power pivot
9:15:40window with this we have three main tabs
9:15:42of Home Design and advanced advanced
9:15:45we're not going to go into a lot of
9:15:46things inside of this if any at all it's
9:15:49beyond the scope of the course we're
9:15:51going to be focusing mostly on the home
9:15:52and the design t tab so with this tab
9:15:54we've already gone over get external
9:15:56data but we can do things like refresh
9:15:57our data if we know that it's updated in
9:15:59power query generate pivot tables and
9:16:01pivot charts based on our data model
9:16:04itself change the formatting of a
9:16:07particular column in this case is
9:16:09noticing as text if we go to the data
9:16:11jobs salary data we can actually scroll
9:16:14over and see that for the salary your
9:16:15average column it knows that it's a
9:16:17currency we did a lot of this cleanup
9:16:19right in power query and setting these
9:16:21different data types so this saves a lot
9:16:23of steps here in power pivot if it
9:16:25wasn't done now we have options
9:16:27displaying the table below that we can
9:16:28actually sort it we can filter it or
9:16:30sort by a certain column they also
9:16:33provide options to find a specific value
9:16:36within here and then these features for
9:16:38calculations I don't find myself using
9:16:40that much as far as the auto so anyway
9:16:42over on the right the most important
9:16:44thing I find is allows you to toggle on
9:16:47the different views of your data set so
9:16:49right now this is the data View and if I
9:16:51scroll over here this is the diagram
9:16:54View and this is going to show our two
9:16:57different tables side by side I'm going
9:16:58to move them over and actually expand
9:17:01this one out to show all the different
9:17:02columns and then the data job skills now
9:17:05back on that data view clicking that we
9:17:08have data view but also below this we
9:17:10have this calculation area which I can
9:17:12toggle on and off calculation areas are
9:17:16where we're going to be storing our
9:17:18different measures that we build with
9:17:20dacks and so they'll be appearing
9:17:22underneath here here if we have any
9:17:23hidden columns we'll be able to toggle
9:17:25them on and off right now I don't have
9:17:27any hidden columns now one thing to note
9:17:29with this data cleanup some of that we
9:17:30did before with formatting stuff some of
9:17:32it's going to be quite limiting you may
9:17:34not be able to do like in the case of
9:17:36this so data job skills has this job
9:17:37title short column and actually if we
9:17:40look at the data jobs salary data set we
9:17:43have the same repeated column in it so
9:17:45data job skills this job title short
9:17:47right here is unnecessary now I could
9:17:49rightclick it and try to delete the
9:17:52column
9:17:53and ask me if I want to delete it it's
9:17:54going to tell me it's not going to be
9:17:56able to do it because it was created by
9:17:57a query I.E through Power query and
9:18:00instead I should actually update it
9:18:01through Power query which I would
9:18:03actually argue as best practice anyway
9:18:05so I could exit out a power pivot launch
9:18:07power query by pressing alt F12 then go
9:18:11into the data jobs skills query and if I
9:18:14want I can just select this column and
9:18:16select remove columns but you know how I
9:18:19am I like to actually clean up the
9:18:21applied steps because it could depending
9:18:23on how large your power query query is
9:18:26it could take a long time to load it and
9:18:28unload it necessary so if I go to this
9:18:29remove other colums that's the first
9:18:33time that it appears in it I can remove
9:18:35this by deleting it out of there then
9:18:37pressing enter we may get an error
9:18:39message we may not I'm not sure going to
9:18:42the last step in here I notice there one
9:18:45thing of the table wasn't found
9:18:47specifically here it's appearing job
9:18:49title short in here so I can go ahead
9:18:51and delete job title short along with
9:18:52with that comma and Bam we now have this
9:18:55Final Table just to lean for those two
9:18:57steps I'm going to go ahead and close
9:18:58and load this and now going back in to
9:19:02look at our data model and power pivot I
9:19:04can see that it updated for data job
9:19:09skills all right moving into this design
9:19:12tab within power pivot this has a few
9:19:15different options within it for adding
9:19:17columns freezing columns just messing
9:19:18with the columns they also have
9:19:20different options for creating
9:19:21calculations concerning columns we'll be
9:19:24getting into calculating columns more in
9:19:26the next lesson so stay tuned for that
9:19:29right the main thing that we're actually
9:19:30going to be doing in this portion of the
9:19:31video is actually setting up
9:19:33relationships and that is we could go
9:19:36about creating a relationship here and
9:19:39right now I have data job skills and I
9:19:42could relate it with the job ID by
9:19:45pulling the drop down to the data jobs
9:19:47salary table on that job ID now that's a
9:19:50way I can do it I'm actually not going
9:19:51to do it this way I actually prefer
9:19:53going to the diagram View and then from
9:19:57there just dragging and dropping the job
9:20:00IDs across each other and then it
9:20:02establish this connection which we can
9:20:04see through this line through here now
9:20:07there's a few different things that we
9:20:08need to notice from this line here one
9:20:12this Arrow it's going to come to bite Us
9:20:14in the butt later and that's that that
9:20:16Arrow only allows data flow in One
9:20:20Direction and by data flow I mean
9:20:22filtering if I try to filter something
9:20:24in the data job skills table this arrow
9:20:27is only pointing in One Direction I
9:20:29won't be able to filter it back we'll
9:20:31encounter those problems in a little bit
9:20:32and we'll talk about strategies how to
9:20:34actually offset it the other thing to
9:20:36note with this relationship here is you
9:20:38notice right here it says one and over
9:20:40here it says star in this case this is a
9:20:44one to many relationship and what does
9:20:48this mean well going to our data view
9:20:50for data job salary we only have one
9:20:53unique ID for each job whereas in the
9:20:57data jobs skills we have multiple
9:21:01different job IDs or many job IDs now if
9:21:05we only had one job ID in there and we
9:21:08actually looked that diagram view for
9:21:10this relationship we'd have a one to one
9:21:12relationship but we have multiple skills
9:21:13in there so that's not possible now it's
9:21:15also possible to have a basically as to
9:21:18ASIS or many to many relationship but
9:21:21that causes a mess slows down your data
9:21:24model and I don't recommend it so you
9:21:25should typically see either a one to one
9:21:28or a one to many last little wrap up
9:21:30before we actually analyze and use this
9:21:32relationship we have the options for
9:21:34table properties which we're not going
9:21:35to be able to look at because this was
9:21:37created the a power query for this
9:21:39connection and then we have options to
9:21:41create date tables underneath calendars
9:21:44which we're going to be exploring in an
9:21:45upcoming lesson and like always you have
9:21:48a undo and redo anyway let's actually
9:21:50get into analyzing and putting this
9:21:53actual relationship to the test so what
9:21:56we're going to do is inside the Home tab
9:21:58go to pivot table CU we're want to
9:21:59create a pivot table with this we're
9:22:01going to insert a pivot table and we'll
9:22:03have it insert into a new worksheet
9:22:06selecting inside the pivot table it's
9:22:07not having the field list come up so
9:22:09I'll select it under pivot table analyze
9:22:11anyway we want to query across this
9:22:13table to show the power of the
9:22:15relationships so what I'm going to do is
9:22:17from the data jobs salary table I'm
9:22:19going to take that job title short throw
9:22:21it into the r those and then from there
9:22:24going to come down to the data jobs
9:22:27skills table and I'm going to throw the
9:22:29job skills into the values it should be
9:22:31performing a count and then I'm going to
9:22:35organize this real quick from largest to
9:22:37smallest and it looks like data
9:22:39Engineers have the most so this is
9:22:41pretty neat we're able now to query
9:22:43across tables going back into that power
9:22:46pivot window this connection allows us
9:22:48to do that I'm going to just show you
9:22:49something real quick by clicking this
9:22:52Rel ship right clicking it and deleting
9:22:54it want to delete for model and I want
9:22:56to show you how these values are
9:22:57basically going to change inside our
9:22:59pivot table basically to the fact that
9:23:02they're going to have it to where
9:23:04they're all the same value and that's
9:23:06how you know that your relationship is
9:23:08not set up correctly whenever you have
9:23:10multiple repeating values and you expect
9:23:12them not to be anyway sometimes you'll
9:23:14see this popup come up of relationships
9:23:16between tables may be needed
9:23:18autodetect and sometimes it works
9:23:21sometimes it doesn't um in this case it
9:23:23looked like it worked so we're going to
9:23:25go with it and just double- checking it
9:23:27in power pivot it is set up
9:23:31correctly so for this final analysis
9:23:34we're going to be looking at building
9:23:36this visualization right here analyzing
9:23:38what are the top skills of data nerds
9:23:41we're basically remaking what we did in
9:23:43the power query chapter now that we have
9:23:45that updated data model anyway we're
9:23:47going to build this out to see where the
9:23:48skills counts for each of these and also
9:23:51provide filters for job country so back
9:23:54inside the workbook that we're
9:23:55previously working with if I would
9:23:57actually remember we did make that sort
9:23:59of similar visualization that I talked
9:24:00about but however if I go to data and
9:24:03actually refresh the data it's going to
9:24:06give me this error message because once
9:24:07again we deleted dat jobs merged anyway
9:24:10I thought this was actually going to go
9:24:12away it didn't it is not what we want
9:24:14we're going to delete this one and then
9:24:16we're going to do a little bit of
9:24:17cleanup so that one that we created the
9:24:19job analysis on I'm going to actually
9:24:21just rename that quick to job analysis
9:24:25and then now in this new sheet we're
9:24:27going to do we're going to name this one
9:24:29skill job analysis anyway let's insert a
9:24:32pivot table in here so we go to insert
9:24:34pivot table and now what we have the
9:24:36option for is from data model and it's
9:24:40ask if I want to put it in the existing
9:24:41worksheet yes I do remember we want to
9:24:44analyze the skills and specifically how
9:24:47many counts they have associated with it
9:24:49or how many jobs they have associated
9:24:50with it so I'm put the skills into into
9:24:52the rows and then from there I want to
9:24:54count how many jobs are associated with
9:24:56it so I'm just going to drag that job ID
9:24:58into the values right now it's doing a
9:25:00sum going click on it go to Value field
9:25:02settings change this to count now you
9:25:04may be like Luke could we use the job
9:25:08skills count and we can which has the
9:25:11same exact values but actually closing
9:25:13this out and taking out job skills
9:25:15you're probably more interested in why
9:25:18can't I use something like the job ID
9:25:19from the data job salary table well if
9:25:22drag that over and then I change this
9:25:25value field setting to account count and
9:25:28click okay you notice it says
9:25:3132672 which is coincidentally the same
9:25:34number of rows of that data set and this
9:25:36gets into the point of filter Direction
9:25:38what do I mean by that let's go back to
9:25:40the data model itself looking at it in
9:25:42diagram view remember the arrow is
9:25:45pointed towards the data job skill table
9:25:48right now I have job skills in the rows
9:25:51and I'm trying to filter for data job
9:25:54salary based on the count of the job IDs
9:25:56but the arrow doesn't flow in that
9:25:58direction we can't do it now in
9:25:59something like powerbi you can actually
9:26:01rightclick this edit the relationship
9:26:03and change the direction that's not
9:26:05possible within Excel unfortunately
9:26:08anyway we're going to be using Dax to
9:26:09fix this in the future for the time
9:26:11being we're just going to go about using
9:26:14in this case for this analysis the same
9:26:16values in the same table I'm going to
9:26:18remove this other job ID from the other
9:26:21table anyway we're going to sort these
9:26:23values from largest to smallest then
9:26:25additionally I only want to show the top
9:26:2810 skills so I'll go to Value filters
9:26:31and then top one dot dot dot top 10
9:26:34items by count of job ID is what I want
9:26:37and so now we have this so now we have
9:26:39the values we want to visualize I'll go
9:26:41in and actually insert a pivot chart for
9:26:44this I like the bar because it makes it
9:26:46easier to read the different skills that
9:26:48it has right there and I'm realizing now
9:26:50the sword order is actually back
9:26:52backwards in this I want it from
9:26:54smallest to largest I'm also going to
9:26:56right click and hide all field buttons
9:26:58we're also going to be adding access
9:26:59titles for the primary horizontal and
9:27:01then removing that Legend we'll update
9:27:04this title to what are the top skills of
9:27:06data nerds and then the y- axis is
9:27:09self-explanatory but for the x-axis
9:27:12we'll label this skill count in job
9:27:14postings okay the last thing we need to
9:27:16do now is actually add some slicers to
9:27:19this so we can actually control it
9:27:20better so selecting the table itself
9:27:23going to insert slicers I'm going to
9:27:25select the job title short and also we
9:27:28want job country right here which each
9:27:31of these slicers I'm going to rename
9:27:33them also this one job title short I'm
9:27:35going to rename to job title and then
9:27:37job country I'm going to rename to
9:27:40Country now when I go through I can
9:27:43actually select something like data
9:27:45analyst and it will filter down and
9:27:48actually see the associated skills I
9:27:51could also do something like like look
9:27:52at those in the United States
9:27:54specifically for their counts and we see
9:27:56that SQL Excel and Tableau are the three
9:27:59top skills now you may be scratching
9:28:01your head on like okay I thought we were
9:28:03trying earlier to actually aggregate
9:28:05something in the pivot table and it
9:28:06didn't work well remember this arrow is
9:28:10pointing to the filter Direction so in
9:28:12our case we have a job title short
9:28:14slicer because this arrows in the
9:28:17direction back to the data job skills
9:28:19table we can filter in that direction
9:28:22but we cannot conversely filter in the
9:28:24other direction that's why we can't get
9:28:25the counts from these tables little
9:28:27confusing I know but I promise you we
9:28:29will work out as we go through this
9:28:31entire chapter in power pivot so bam we
9:28:35just completed our first analysis for
9:28:37our final project we have a few more
9:28:39analysis coming up in the next lessons
9:28:41you do have some practice problems
9:28:43though to go through and get yourself
9:28:45more familiar with power pivot and
9:28:48understanding what's going on with these
9:28:49relationships the one to many and
9:28:50whatnot all right with that I'll see you
9:28:52in the next one which we're going to do
9:28:54a deeper dive on looking into that power
9:28:57pivot window that I'll see you
9:29:02there all right let's now dive further
9:29:05into Power pivot and we're going to be
9:29:06focusing on the power pivot window for
9:29:08this we're going to be looking at some
9:29:10major aspects of it for this we're going
9:29:12to get into using a little bit of Dax to
9:29:14create our first measure and with those
9:29:17measures we're also going to be
9:29:19exploring the difference between
9:29:20implicit and explicit measures don't
9:29:22worry we'll cover that in a bit from
9:29:24there we're going to move into a feature
9:29:26that's related to measures called
9:29:28calculated columns and it's going to
9:29:30allow us to inside of our data model
9:29:32create different values such in this
9:29:35case we can actually create a date colum
9:29:38from our date time value the last thing
9:29:40we'll explore are date tables which
9:29:42power pivot gives with a click of a
9:29:44button and allows us to connect these
9:29:46data tables of these date tables to our
9:29:49original data source and then filter it
9:29:51by a lot of different data and so we'll
9:29:53wrap this all up with a final analysis
9:29:55where we're looking at job postings
9:29:57based on a day of week using this date
9:30:00table anyway jumping into Excel for this
9:30:02we're not going to be using any of the
9:30:04work that we've done previously instead
9:30:06we're going to open up a completely new
9:30:08workbook and be working out of this
9:30:10instead and the reason is all the work
9:30:13that we're going to be doing within this
9:30:14lesson we're not going to be carrying it
9:30:16on to our project that we're going using
9:30:18this is more this lesson is more to get
9:30:20us more familiar with the powers power
9:30:22pivot oh gosh this pun's killing me and
9:30:25so we'll eventually incorporate some of
9:30:26the stuff into our final project but
9:30:28like I said we're going to be starting
9:30:29with a blank notebook or workbook for
9:30:31this as always if you want to see what
9:30:32the results are at the end of this
9:30:35lesson you can just go to Power pivot
9:30:37window and it will have
9:30:41it all right so let's actually get some
9:30:44data into here to start working with and
9:30:46like I said we're not going to use power
9:30:48query at all for this we're going to use
9:30:49power pivot so I'm going to open up the
9:30:51goto to the manage the power pivot data
9:30:53model and we want to get this external
9:30:55data specifically we want to get that
9:30:57Excel workbook that we've been working
9:30:59with of data jobs salary all so
9:31:02underneath the Home tab I'm going to go
9:31:03to get external data and it's going to
9:31:05be from other sources we scroll all down
9:31:08we could look at how we can import it
9:31:09from different databases or whatnot
9:31:11we're going to be doing it from an Excel
9:31:13file then from there we're going to
9:31:15browse the connections navigating into
9:31:18that data set folder I'm going select
9:31:19data jobs salary all it PR me if I want
9:31:22to use the first row as column headers I
9:31:24do if I wanted to I could go in and test
9:31:26the connection to make sure it's it's
9:31:28going to succeed and it does so we'll go
9:31:30from there to next it sees that it has
9:31:32one sheet within the workbook that's the
9:31:34one that I want I'll click finish next
9:31:36it'll go through the import looks like
9:31:38it completed it has a success got 32,000
9:31:40rows I'll click close now let's go
9:31:43through and actually clean this data set
9:31:46up using power pivot now I know in the
9:31:48last lesson I talked about hey we're
9:31:51using power query for ETL and that's
9:31:53true but let's say you have a quick data
9:31:55set you need to connect to and model
9:31:57quickly in that case you would do some
9:31:59of the stuff that I'm going to do here
9:32:01in order to quickly model it if I wanted
9:32:03to rename it I'd come down to this
9:32:04basically sheet tab down here it's
9:32:06called sheet one after where it's at
9:32:08I'll rename it and we'll keep a similar
9:32:10naming Convention of Jatt jobs salary go
9:32:14ahead and click enter so let's say for
9:32:15this quick analysis that we're trying to
9:32:17do in this lesson I'm trying to analyze
9:32:19only the yearly salary data I don't care
9:32:21care about the salary uh hourly data and
9:32:23I don't even want the data entries in
9:32:25here well I can get rid of that salary
9:32:28hour average row by just deleting this
9:32:30column by right clicking it it's asking
9:32:32me if I want to delete it yes and now
9:32:34there's still blank values in here right
9:32:36so I need to get rid of this salary rate
9:32:39values that are equal to hour so I'm
9:32:41going to click the filter here unclick
9:32:43next to hour and click okay so now we
9:32:46have that out the other thing I want to
9:32:48do is actually clean up the format of
9:32:50the salary and I'm going to change that
9:32:52instead to a currency and this talks
9:32:55about how the data is going to be a
9:32:56changed when where it's stored yeah I
9:32:58don't really care about that no doubt
9:33:00that I care about will be lost it'll all
9:33:02be here still and then I'm going to
9:33:04reduce the decimal places by two the
9:33:06other thing I can do if I wanted to is
9:33:08actually sort this based on that job
9:33:10posted date could come up here and sort
9:33:13from newest to oldest and then it's in
9:33:15order sorry actually want it oldest to
9:33:17newest got confused on that one so bam
9:33:20just did some quick clean up to our data
9:33:22set and now we're ready to proceed
9:33:27forward so let's actually get into
9:33:29building our first measure or measures
9:33:33specifically I want to analyze this to
9:33:35understand what are the different the
9:33:37the amount of jobs in here and then also
9:33:39what is the average and then also more
9:33:41importantly the median salary well
9:33:44there's a few different ways we can do
9:33:45this we're going to do this first within
9:33:47this power pivot window so in order to
9:33:50do this I'm going to first first I want
9:33:52to do a count so we're going to just run
9:33:54this on this job title short column and
9:33:57here underneath on the Home tab under
9:33:59calculations we have this Auto sum I
9:34:01don't frequently use this I use it every
9:34:03now and then but I can run things on
9:34:05this like count or distinct count I'm
9:34:07going to do count in this case and this
9:34:09is going to create our first measure
9:34:11down here remember down below this area
9:34:14is our calculation area I can toggle it
9:34:16on and off by clicking calculation area
9:34:18up here anyway I can also make this
9:34:21column slightly bigger and what's cool
9:34:23about this keep on scrolling over is now
9:34:27it tells us the name of this measure
9:34:29count of job tile short and that there's
9:34:3122,000 remember there's normally around
9:34:3430,000 but because we've taken out that
9:34:36hourly data we're down to 22,000 now I
9:34:39can also edit this measure if you notice
9:34:43it appears right up here similarly they
9:34:45have a formula bar in power pivot and to
9:34:48the left hand side it tells you what is
9:34:51actually selected job title short column
9:34:53and then the actual measure itself in
9:34:55here now one quick note there is
9:34:59basically a colon and then an equal sign
9:35:01that's how we're going to know that
9:35:03we're doing measures and we'll get to
9:35:05calculate columns in a little bit and it
9:35:07will only be the equal sign but this is
9:35:09Microsoft's way of signifying that this
9:35:12we're using a measure so that way you
9:35:14don't confuse with anything else anyway
9:35:16I can edit this the actual title in this
9:35:19case and I can change this something to
9:35:21more more descriptive to job count
9:35:23pressing enter it now runs it and it's a
9:35:26lot shorter additionally if I want to
9:35:29actually format it I can have the
9:35:31measure selected come up here select
9:35:33comma and then it formats it with the
9:35:35comma and then I don't want two decimal
9:35:37places I'll go ahead and remove it next
9:35:39let's get into analyzing that salary
9:35:42column with this once again we can click
9:35:44this I could use that auto sum and do
9:35:46something like average here clicking
9:35:48average and below it it generates that
9:35:51average
9:35:52of salary or average 123,000 and I can
9:35:54change it if I want to average salary
9:35:57but if I wanted to calculate something
9:35:59like the median instead I would have to
9:36:02actually manually type out this
9:36:03calculation so selecting right below
9:36:05average salary and then coming into the
9:36:07formula bar I can type in something like
9:36:09median salary remember we want to create
9:36:12a measure so it's going to be a colon
9:36:14and then an equal to and then for this
9:36:16we want to use the median function now a
9:36:19lot of these functions that are Dax
9:36:22functions are very similar to what we
9:36:24use in Excel so they have a lot of
9:36:25different similarities but with this
9:36:28like we talked about before this allows
9:36:30us to now put in basically an entire
9:36:33column into it and then perform that
9:36:35entire aggregation on it in this case I
9:36:37want to do it all on salary year average
9:36:40making sure I put a close parenthesis to
9:36:42close out that function and Bam right
9:36:45next to average salary we have this
9:36:46median salary now which needs to be
9:36:48formatted so I'll format it as English
9:36:51United States stes USD and remove the
9:36:53decimal places now what happens if I
9:36:55didn't enter that colon equal sign so
9:36:57here I am selected below median salary
9:36:59we'll go ahead and paste in that formula
9:37:01and we'll delete that colon I haven't
9:37:03run this yet now I'm going to run I'm
9:37:04going to press enter and as you notice
9:37:06by this it's not actually calculating a
9:37:09value it actually just converts this to
9:37:12text so this is not what we want that's
9:37:14why we have to do the colon equal to
9:37:16sign for entering in the formula bar
9:37:19there
9:37:23so with measures it's important to
9:37:24understand implicit vers explicit
9:37:27measures so let's close out the power
9:37:28pivot window and actually getting into
9:37:31exploring these different measures by
9:37:33creating a pivot table of that median
9:37:35salary we just created so I'm going to
9:37:37go to insert pivot table from data model
9:37:40we're going to insert it into the
9:37:42existing sheet here we have our table of
9:37:44data job salary I'm going to analyze the
9:37:47salary based on the job title short
9:37:49column so I'll put job title short into
9:37:51the rows and then look we scroll down at
9:37:55the very bottom you'll notice that the
9:37:58measures that we created have this F ofx
9:38:01basically it shows us an equation that
9:38:03it is a measure so I can take these
9:38:06measures this median salary and in this
9:38:09case drag it into the values and now
9:38:11unlike power pivot where it did in that
9:38:13same column we're now filtering down to
9:38:15do it by well the appropriate job titles
9:38:19now we could also do something like drag
9:38:21the the job count into the values as
9:38:23well and actually see the job count
9:38:26there now both of these measures are
9:38:29explicit measures because we explicitly
9:38:33defined it we despine defined what job
9:38:35count is and what median salary is so
9:38:38what is an implicit measure well you
9:38:40actually created this before so in
9:38:43regards to that job count we're doing a
9:38:45count of the job title short column if I
9:38:48were to drag that down into here you can
9:38:50see it says say count of job title short
9:38:54this is an implicit measure these are
9:38:58great for quick short analysis as we
9:39:00demonstrated before you can quickly
9:39:02throw something in and generate it and
9:39:03you didn't even know your us the
9:39:05measures and you were similarly with the
9:39:07salary year average if I drag that in
9:39:09down here we previously well changing
9:39:12this up to actually perform an average
9:39:14mov to average from there that was also
9:39:17an implicit measure so I think you get
9:39:19the point but we're going to see the
9:39:22power of this as we go through this when
9:39:24we start to make newer measures that are
9:39:27actually going to use our explicit
9:39:29measures specifically we're going to be
9:39:31using our job count in other
9:39:34calculations and so these explicit
9:39:36measures are going to save our butt and
9:39:38save us so much time and ensure we're
9:39:40doing the correct
9:39:44calculations so let's get into our first
9:39:46calculated column and we're going to be
9:39:48going back into the power pivot window
9:39:51for this we're going to be creating a
9:39:54column that will convert the salary year
9:39:57average values into Euro values so
9:40:02there's a couple ways we can do this or
9:40:03add these columns we can go under design
9:40:06and right here under columns we can
9:40:08click add to add a column additionally
9:40:11without that that unselected selecting
9:40:13back in into again you see this add
9:40:15column up here we can just go right in
9:40:17and add a column I feel that's actually
9:40:18easier anyway in this case in order to
9:40:21get the Euros value of what it is for
9:40:24Sal year average we need to multiply by
9:40:26a conversion rate so inside of here I'm
9:40:29going to put the equal sign and we see
9:40:31it's popping up here in the formula bar
9:40:33from there I'm going to use the value in
9:40:34salary year average I just selected one
9:40:37of the values and it popped right in
9:40:39then from there similar to how we wrote
9:40:42formulas before I'm going to put times
9:40:450.9 enter now notice from this one I
9:40:48didn't use the colon equal sign right
9:40:50because is not a measure it's a
9:40:52calculated column and it still knew that
9:40:55this was a currency although I don't
9:40:57like it it has two decimal places so
9:40:59I'll remove it and to me it knows it's a
9:41:02currency but it doesn't know that it's a
9:41:03Euro so I'm actually going to convert it
9:41:05over to Euro and then remove the two
9:41:07decimal places additionally I'm going to
9:41:08rename this from calculated column one
9:41:11to salary year Euro you can identify
9:41:15calculated columns because Normal
9:41:16columns are green the calculated columns
9:41:18are black also if I go to the DI diagram
9:41:21view we can see that well you can't
9:41:24really tell that we have the calculate
9:41:25column C Euro but you can see your
9:41:28different measures that you've created
9:41:30all right so back to the data view even
9:41:32though we have this calculated column we
9:41:35could also create a measure on this
9:41:38calculated column clicking in the box
9:41:40below here and then typing in here I
9:41:42could do something like median salary
9:41:44Euro and then put in that median
9:41:47function for salary year Euro and then
9:41:50close the parenthesis and Bam now we
9:41:53have it I'm going to spread it out to
9:41:54actually see we have the value of €
9:41:57103 now going back into here we can take
9:42:00this and actually if we wanted to we
9:42:02could put the salary year euro into
9:42:04there that column it's going to
9:42:05aggregate it appropriately right now
9:42:07it's doing a sum so if I wanted to I
9:42:09could get a average of this of these
9:42:13values or we could actually go to that
9:42:16measure that we created That explicit
9:42:18measure throw it in here and we get the
9:42:21explicit value of the median salary
9:42:25Euro all right so let's shift our focus
9:42:27on this analysis let's say we wanted to
9:42:29analyze more around the date
9:42:32specifically the day of the weeks for
9:42:35when job postings are occurring well
9:42:37let's go back into Power pivot and
9:42:40manage to open up the power pivot window
9:42:42right now investigating our the diagram
9:42:44view of our data model we only have one
9:42:46table in here data jobs salary well if
9:42:50we go under the design tab talked about
9:42:52in the last lesson we can actually
9:42:56create a date table I could also
9:42:59potentially mark this table of do job
9:43:01salaries a it's not a date table so we
9:43:03actually need to create one and you'll
9:43:04see what it looks like after that and
9:43:06with that I did click new on this anyway
9:43:09it created this new table called
9:43:12calendar and expecting all of the
9:43:14different values in here well let's
9:43:16actually just get out of this view let's
9:43:18actually go to the data view one which
9:43:21is pretty cool with it with it what it
9:43:24created it created it based on the dates
9:43:26it knew what was in our original table
9:43:28so from the first of 2023 all the way to
9:43:31the last day of 2023 and with this it
9:43:34has a year column month day of week and
9:43:38day of week number so a lot of great
9:43:40values from it now we need to actually
9:43:42connect these two there's no
9:43:44relationship between the two if we go to
9:43:47that data jobs salary so selecting it
9:43:50here here we only have this job posted
9:43:53date column which is a date and a time
9:43:57so we need only a date so because this
9:43:59column is named inappropriately I'm
9:44:01going to change it to J job posted date
9:44:04time so now let's create that new column
9:44:06with that job posted date time this time
9:44:08though instead of clicking add column
9:44:09we're going to go to insert function and
9:44:12this is pretty neat because it allows us
9:44:14to actually look under different things
9:44:16in this case we wanted sort of a text
9:44:18function and we can look and explore
9:44:20different one specifically I know we
9:44:22want this one a format converts a value
9:44:24and text to the specified number format
9:44:27so I'm going to click okay and it
9:44:29automatically fills it in with this
9:44:31colon and equal sign of format equal to
9:44:34from there I'll select the job posted
9:44:36date time column that's the value and
9:44:39then what do we want for the format well
9:44:41I know we want in the format of
9:44:43basically the year first then two months
9:44:46or two M's and then two D's for month
9:44:48and date in order to match close that
9:44:51double quote because that's the actual
9:44:53format we're using that's all we need so
9:44:56we'll close the parentheses and press
9:44:58enter and then I'm going to take this
9:45:00calculated column one drag it over here
9:45:03and then I can see that it did convert
9:45:04it correctly so I'm also going to go now
9:45:06and rename this appropriately to job
9:45:09posted date press enter so now let's
9:45:12create a relationship between the two
9:45:14remember we can go to that diagram view
9:45:16or I can use this of create relationship
9:45:19go to calendar to match on the date
9:45:21itself let's see what it looks like in
9:45:24that actual diagram view we always want
9:45:26to inspect it to make sure we have this
9:45:28right one to many or one to one anytime
9:45:31we have many to many you need to start
9:45:33questioning it depending on what the
9:45:35data is anyway we now have a
9:45:38relationship established with
9:45:42this so let's actually get into
9:45:45analyzing this with our calendar based
9:45:48on this day of the week and seeing what
9:45:50is the prop portion that they're turning
9:45:52out during the week for job postings so
9:45:55closing out the power pivot window I'm
9:45:56going to go in and create a new sheet
9:45:59from there I'm going to go go insert
9:46:01pivot table from data model we're going
9:46:03to do it in the existing worksheet
9:46:05underneath calendar Underneath more
9:46:08Fields I'm going to drag in day of week
9:46:11into the rows so it has Sunday all the
9:46:14way to Saturday then from there remember
9:46:16we created that job count already so I'm
9:46:19going to take that and drag that into
9:46:21the values so looking at this I can see
9:46:25that I think our relationship is not set
9:46:28up properly cuz we have basically the
9:46:29blanks at 32,000 I think I know what's
9:46:32going on with this let's go back into
9:46:34the power pivot window in calendar when
9:46:36we select the date it's of the time data
9:46:39type date it also has this format of
9:46:41date and time I don't that really
9:46:43matters too much but if we go into Data
9:46:44job salary and we go to that job post to
9:46:47date because we use that format function
9:46:50right now the data type is auto of text
9:46:54we need it to be of date and this now
9:46:58looks a lot more similar to what does on
9:47:01the calendar now when I close out of
9:47:03this bam all the values pop up here so
9:47:07don't forget about your data types and
9:47:08making sure they're match within the
9:47:10data model so let's actually visualize
9:47:12this by inserting a pivot chart and Bam
9:47:15we get this bad boy which we'll rename
9:47:17to to when are most jobs posted during
9:47:20the week and it looks like we have well
9:47:22on Saturday Sunday or the lowest
9:47:24obviously during the week it's the
9:47:25highest with a basically a higher amount
9:47:27on Wednesday so pretty cool analysis
9:47:30that we were able to do based on the day
9:47:33of the week we didn't have to create any
9:47:35additional things and additionally we
9:47:38can evaluate based on this calendar
9:47:40table created we can do other analysis
9:47:42such as by the year month day of the
9:47:45week and whatnot all right so that's a
9:47:47brief intro into measures and also
9:47:50calculated columns don't worry too much
9:47:53if you're not feeling too confident with
9:47:55them just yet as one you have some
9:47:57practice problems to go through to get
9:47:58more familiar with it but the next
9:48:00lesson will be and the next two lessons
9:48:02will be on Dax and Dax advance in order
9:48:05to explore different formulas that you
9:48:07can also use inside of your measures and
9:48:10also calculated columns all right with
9:48:13that I'll see you in the next one where
9:48:15we're getting into deck see you there
9:48:21welcome to this lesson on Dax or data
9:48:24analytical Expressions we' used it a few
9:48:26times before in the previous lesson but
9:48:29now we're going to go much more in depth
9:48:31and actually understanding the basics of
9:48:33it now as we've learned Dax can be used
9:48:35within measures or even calculated
9:48:38columns for the purpose of what we we
9:48:40going through in the project we're not
9:48:42going to create any calculated columns
9:48:44but we will be using it for measures for
9:48:47this we're going to be focusing on three
9:48:48major types of functions in this lesson
9:48:51specifically around aggregation
9:48:53statistics and also filter these
9:48:55functions you're going to notice are
9:48:57very similar to your Excel functions
9:49:00that we did back in Chapter 2 so a lot
9:49:03of those similarities and concept we've
9:49:05learned already are going to be able to
9:49:06be applied to this so we'll be able to
9:49:08move pretty quick now we're going to be
9:49:09answering two major questions regarding
9:49:12our final project the first one involves
9:49:15calculating the number of skills
9:49:18required per job title we're going to
9:49:21use Dax in order to calculate this and
9:49:23then we're even going to go on to
9:49:25actually graph this to show how it
9:49:28correlates with median salary spoil
9:49:31alert the more skills you have the
9:49:33higher median salary you can expect from
9:49:35there we're going to go into a deeper
9:49:37analysis of salar specifically looking
9:49:39at the median salary and specifically
9:49:42being able to compare it from your home
9:49:45country to the US and also non us
9:49:48countries so we're going to use filter
9:49:50function in order to be able to view
9:49:52these things within a pivot table now
9:49:54jumping right into Excel for this you
9:49:57can continue working in the Excel file
9:49:59that you have from that first lesson on
9:50:02power pivot intro where we created this
9:50:05visualization right here which analyzes
9:50:07top skills of data nerds and has some
9:50:08filters for job title and Country if you
9:50:11don't happen to have that file anymore
9:50:12or you got lost along the way you can
9:50:14just use the power pivot intro part two
9:50:17file and you can start from there now if
9:50:19you're loading it via the power pivot
9:50:21intro part two file you're going to have
9:50:23two sheets in there one skill job
9:50:25analysis and then also the skill
9:50:27analysis we're not actually going to be
9:50:28using the skill analysis so you can feel
9:50:30free to delete this or conversely if
9:50:33you're working from the files that
9:50:35you've been building up during this and
9:50:37didn't necessarily load from the power
9:50:39pivot intro part two file you may have
9:50:41multiple tabs in there once again I only
9:50:44care about this skill jobs analysis
9:50:46where we have this this is what we're
9:50:47going to keep for the final project the
9:50:49job analysis and and also this other one
9:50:51that we created back in the power query
9:50:54lesson we're actually going to be
9:50:55recreating it with power pivot so both
9:50:58of these I can just delete or anything
9:51:00else you have in there you can f it free
9:51:01to delete after holding control and
9:51:03selecting both of those I'm just going
9:51:04to delete
9:51:08them all right so we're going to be
9:51:09looking at aggregation functions first
9:51:12conveniently Microsoft has some
9:51:13documentation around the Dax functions
9:51:16and also statements that they have so
9:51:18I'm going to dive right into the link
9:51:20that's provided on the screen underneath
9:51:22Dax functions specifically I'm going to
9:51:24go into the aggregation functions they
9:51:27have this page here on aggregation
9:51:29functions overview and it shows a lot of
9:51:32the different functions they have for
9:51:33this average count Max Min sum let's
9:51:36look at count real quick count is pretty
9:51:39simple all we're going to do is use the
9:51:41following syntax count and inside of it
9:51:44you provide a column and for this it
9:51:46says Hey the column that contains the
9:51:48values to be counted so pretty simple
9:51:51function to use similarly we have
9:51:53distinct count which has the similar
9:51:56syntax of you provide distinct count and
9:51:58the column and the column that contains
9:51:59the values counted and it will return
9:52:02the number of distinct values in columns
9:52:04we're going to use this so what we're
9:52:06going to be calculating with those
9:52:08functions that we just went over is
9:52:10trying to find out how many skills per
9:52:13job we're going to first go through
9:52:14based on a job title and find not only
9:52:16the skill count but also the job count
9:52:19and then we're going to take both these
9:52:20values and divide them to get the skills
9:52:24per job so I'm going to create a new
9:52:25sheet for this and inside of here I'm
9:52:28going to insert in a pivot table from
9:52:30our data model we're going to do in the
9:52:32existing worksheet for the rows we're
9:52:34going to go through the do data job
9:52:36salary table and we're going to put that
9:52:37job title short into the rows and then
9:52:40now we need the skill count remember we
9:52:44could go in and do something and create
9:52:46an implicit measure by throwing job
9:52:48skills and the values we want an
9:52:50explicit measure because we're actually
9:52:52going to be using the skill count in a
9:52:54later calculation to find that skill for
9:52:56job anyway how do we do this well we can
9:52:57also not only create a measure by going
9:52:59to power pivot and underneath here going
9:53:01to new measure you can also just select
9:53:04in here which table you want to use in
9:53:06this case I'm doing a skill count so I
9:53:08want to contain it in the data jobs
9:53:10skills table doesn't really matter which
9:53:12table I'll put it in but I just go by my
9:53:14memory of which one I'm going to know to
9:53:16go look at for which in there it auto
9:53:18selects that table of data jobs skills
9:53:21the measure name is going to be skill
9:53:23count and then for the formula itself we
9:53:26want to do a count of the job skills
9:53:29column from the job skills table make
9:53:32sure it's not from the job salary table
9:53:34okay I'm going to put a closing
9:53:35parenthesis on this and then for this we
9:53:37do want to format it to use a th
9:53:40separator and zero click okay and now in
9:53:42the data job skills table we have this
9:53:45explicit measure can drag it right next
9:53:47to it same values are getting created as
9:53:49the implicit measure so I'm going to
9:53:51take out that implicit measure next
9:53:53thing you want to calculate is that job
9:53:55count we're going to be counting it
9:53:57based on the distinct values of the job
9:53:59ID so I'm going go to add measure we're
9:54:01going to call this one job count and
9:54:03we'll do a distinct count of we want to
9:54:06do it of the job ID column and for this
9:54:09one we want to make sure that we're
9:54:10actually doing it from the salary or
9:54:12data jobs salary table because this has
9:54:15all the job IDs in it once again we're
9:54:17going to format as a number with 1,000
9:54:19separator and click okay and then I'm
9:54:21going to drag at the bottom the measure
9:54:23is going to appear I'm going to drag it
9:54:24into here so now we want to get how many
9:54:27skills per job so we want to take the
9:54:29skill count column and divide it by the
9:54:32job count column this one doesn't really
9:54:34matter too much because it contains both
9:54:36of them but I'm going to put this in the
9:54:37data jobs skills table I'm going to call
9:54:40this skills per job now what's great
9:54:44about these explicit measures that we
9:54:46just created is I can go hey I want to
9:54:48do this skill count and I want to divide
9:54:50divided by the job count and it's right
9:54:53there so you don't have to necessarily
9:54:55write out every single time okay I want
9:54:57to do a count of the job skills column
9:55:00and then divided by a count of the job
9:55:03ID column which actually needs to be a
9:55:06distinct count anyway this is where we
9:55:07run into errors that's why the explicit
9:55:09meas are so measures are so great all
9:55:11right so I have skill count divided by
9:55:12job count I'm going to create it as a
9:55:13number and I want one decimal place for
9:55:16this go ahead and click okay and then
9:55:19we're going to add this skills per job
9:55:20two here now I'm actually going to
9:55:22recommend although we just use the
9:55:24division sign I'm going to actually
9:55:26recommend this divide function with it
9:55:28which is a ma math function and what
9:55:30would you do in this case is you would
9:55:32provide divide and you list a numerator
9:55:35and a denominator and the reason why I
9:55:37like this is because it fixes any type
9:55:41or catches any error specifically it
9:55:43performs Division and returns alternate
9:55:45results or or blank on division by zero
9:55:49so we're not going to necessar error out
9:55:51if we have a division by Z zero issue
9:55:53and you can actually provide as shown
9:55:55down here in the alternate result the
9:55:57value return When division by zero
9:55:59results in an error so you could
9:56:00actually catch that any so I'm go going
9:56:02to go back into that skills per job and
9:56:05I'm going to go to edit measure I'm
9:56:06going to change this to divide specify
9:56:09the first and second parameter with a
9:56:11comma and then click okay okay overall
9:56:15no real change here but just a best
9:56:17practice to know about
9:56:21so now with this skills per job I want
9:56:24to actually get in and comparing this to
9:56:26median salary this is what we're going
9:56:27to be building right here we're going to
9:56:29be comparing it to median salary and
9:56:31then graphing it in a scatter chart in
9:56:33order to see how these different job
9:56:35titles correlate to each other so first
9:56:37so to know what the final analysis is
9:56:39going to be of this I'm going to rename
9:56:41this sheet appropriately specifically
9:56:43calling it salary vers skills and this
9:56:47pivot table here we don't need
9:56:48necessarily the skill count or the job
9:56:50count we just need the skills per job
9:56:53okay we're going to calculate now the
9:56:54median salary and median is a
9:56:57statistical function which is
9:56:59encountered underneath here but there's
9:57:01a lot of different options underneath
9:57:03here such as Med median finding the
9:57:05different percentiles like we did back
9:57:06in the formulas looking at things like
9:57:09standard deviation and whatnot so a lot
9:57:11of good statistical functions that you
9:57:12have access to Via Dax so for this
9:57:15measure I'm just going to come up here
9:57:16to power pivot go under measures and
9:57:17select new measure I do want this in the
9:57:19data job salary table and we're going to
9:57:22call this median salary for this we're
9:57:24going to be using the median function
9:57:27and we need to provide it a column
9:57:29specifically that salary year average
9:57:32value for formatting we're going to
9:57:34format it as a currency with zero
9:57:36decimal places since it's a salary so
9:57:38now we have median salary here I
9:57:40actually want it to appear on the Y AIS
9:57:43so I'm going to throw it over here on
9:57:45the First Column so now we have the
9:57:47median salary and skills per job I'm
9:57:49just going to rate these or sort these
9:57:51from highest to lowest to see if I can
9:57:54see visually if there's anything going
9:57:56on with a correlation right now I am
9:57:58seeing some higher skills than uh with a
9:58:01higher salary but let's actually
9:58:03visualize this so I'm going to insert
9:58:05pivot chart and select PIV pivot chart
9:58:08for this we want to enter a scatter plot
9:58:12and if you remember back from our charts
9:58:13lecture we're going to have issues with
9:58:15this you can't create this chart with
9:58:17the data inside the pivot table doesn't
9:58:19natively support creating Scatter Plots
9:58:22kind of annoying if you ask me anyway
9:58:24let's X out of this and for this what
9:58:26we're going to do is we're just going to
9:58:28set this area starting up here we're
9:58:30going to set it equal to this entire
9:58:33table right here I'm not going to
9:58:35capture the grand total at the bottom
9:58:37because we're not going to be plotting
9:58:38that now with these values I'm going to
9:58:40select the contents in that this column
9:58:42f and g and then from there go insert a
9:58:45scatter plot specifically this one right
9:58:48here I can see it already looks pretty
9:58:49good you can't actually add the data
9:58:51labels in whenever you create this chart
9:58:53we actually have to go about doing that
9:58:55somewhat manually specifically we have
9:58:57to select on the data points and then
9:58:59rightclick it and we have to select add
9:59:02data labels okay now it's giving us
9:59:05points which bar which actually
9:59:07correlate to the skills per job point
9:59:09it's not what we want we want to include
9:59:11the job title we're going to add that so
9:59:13we're going to do is select one of those
9:59:15values and just rightclick it and then
9:59:17from there select format data labels
9:59:20then the pane's going to open up on the
9:59:21right hand side and it should pop you up
9:59:24underneath label options label options
9:59:26then this label options and right now we
9:59:28have this y value selected that's not
9:59:30what we want we want value from cell and
9:59:33it says Hey select the data label range
9:59:36what we want is right here all the way
9:59:38going down it's hidden behind here I'm
9:59:40going to sort of guess but I know it
9:59:42goes down to E11 click okay and
9:59:44scrolling it over bam we got all those
9:59:46data labels on there now all right so
9:59:49now we need to clean this bad boy up
9:59:51because well it's a hot mess that is all
9:59:53up in the upper right hand quadrant
9:59:55labels are overlapping we're going to
9:59:57fix all of this first thing is I'm going
9:59:58to correct the axises so I'm going to
10:00:01click on the y or click on the x axis
10:00:04and it should go immediately to this
10:00:05minimum axis underneath access options
10:00:08and I can see the first value stops
10:00:10around or begins around 880,000 so I'm
10:00:13going to change this to that and press
10:00:15enter okay similarly I'm going to select
10:00:18the Y AIS and if doesn't go to it should
10:00:20be under access options inside that
10:00:22format access Pane and I'm going to
10:00:24select this first value that I want to
10:00:26go to is three I'll leave the default of
10:00:28nine there next thing is we need some
10:00:31axis labels for the y axis we'll call
10:00:34this average skills requested for the
10:00:36x-axis we'll call this median salary and
10:00:38we'll specify the units of USD speaking
10:00:40of which this is not formatted correctly
10:00:43for how we want the numbers so under
10:00:46that format access pane under access
10:00:47options and under access opt options
10:00:50again under number we can go to the
10:00:53custom option specifically you should
10:00:55have this type hopefully appearing up if
10:00:58not you can just enter it into this
10:00:59format code below and then press enter
10:01:02all right the last two things to do is
10:01:04rename the title naming it do more
10:01:06skills equal more money for data nerds
10:01:09which from this chart it looks like it
10:01:11does and we can actually confirm this if
10:01:14we want by adding a trend line now
10:01:16there's different options here for trend
10:01:18lines we've going over linear
10:01:19exponential IAL linear forecast I feel
10:01:22linear best meets this need here also
10:01:24like the coloring aspect of it so we're
10:01:26going to go with that all right the last
10:01:27thing to do is just fix some of these
10:01:29names on here so right now we have the
10:01:33data La labels appearing to the right of
10:01:36the data point and in cases where it's
10:01:39close so data senior data scientist it's
10:01:42too close to the edge and so it's just
10:01:44sort of over the top of it anyway what
10:01:46you can do is actually select it twice
10:01:48so click it twice then you can drag and
10:01:51drop it and it should have these arrows
10:01:55or these connectors that connect the
10:01:56name to where it goes to all right so
10:01:59now we have our final
10:02:02visualization and I'd say it's not too
10:02:05bad some things I'm noticing about this
10:02:08some correlation if you notice yes we do
10:02:11see the average skills requested are
10:02:13going up with the salary but those jobs
10:02:18I mean if you you can pretty much see it
10:02:19they div iding line those jobs that end
10:02:21an engineer Vice analyst or scientist
10:02:24are commanding or requesting more skills
10:02:27but yet have sort of a similar pay to
10:02:31their data analyst or scientist
10:02:33counterparts so I don't know I guess it
10:02:35kind of pays to be a data analyst and
10:02:37not a data engineer don't tell my data
10:02:39engineer friends I said
10:02:43that all right last analysis we're going
10:02:45to get into is using filters to actually
10:02:50aggregate so in this case right here
10:02:51we're showing what we're going to get to
10:02:53the final thing of based on a job title
10:02:56short value what is the median salary in
10:02:59this first column for the us then what
10:03:02is the median salary for non us and then
10:03:06finally that final column of median
10:03:08salary what is the median salary of in
10:03:11this case the selected column is uh
10:03:13Argentina it's filter down basically I
10:03:16call this filter function we're going to
10:03:17go over but we're going to be
10:03:18calculating or figure out how to prevent
10:03:21filters from affecting a visualization
10:03:23so we can get core values what we may
10:03:26want so we're going to create a new
10:03:28sheet and I'm going to call this salary
10:03:30analysis like before we're going to
10:03:32insert a pivot table from our data model
10:03:34insert it into this new sheet and we're
10:03:36going to be putting that job title short
10:03:38into the rows now we're obviously with
10:03:41this going to be calculating median
10:03:42salary so I'm going to go ahead and just
10:03:44drag that into the values to start
10:03:46getting those median salaries
10:03:47additionally we're going to want to
10:03:48include a Slicer in here so based on the
10:03:52job country so I'm going to insert
10:03:53slicer on job country click okay and
10:03:57then with this we can actually see if we
10:03:59select something like Argentina it's
10:04:01going to filter down to what it is or
10:04:04what the salary median salary is in
10:04:06Argentina but remember we're trying to
10:04:08add two columns to this so we can
10:04:10compare these values of something like
10:04:11Argentina to us salaries and maybe non
10:04:15us salaries so basically countries
10:04:17outside the US anyway we're going to be
10:04:19using filter functions for this and for
10:04:22warning on this it says it here the
10:04:24filter and value functions in Dax are
10:04:26some of the most complex powerful and
10:04:29differ greatly from Excel functions so
10:04:31there's going to be a little bit of
10:04:32complexity here in understanding this
10:04:34and for this filter function we're going
10:04:36to be using this one on calculate and
10:04:39what it does is it evaluates an
10:04:41expression in a modified filter context
10:04:45calculate is pretty simple in my opinion
10:04:47first you provide an expression so such
10:04:49as hey perform a count of this column or
10:04:51a median of this column from there you
10:04:54provide a filter or filters and as it
10:04:57states below here filters can be Boolean
10:05:00filter Expressions table filter or
10:05:01filter modification functions main thing
10:05:03is here we're going to use things like
10:05:06logical operators in order to compare
10:05:08this to maybe a certain value we're
10:05:10going to expect so let's jump into
10:05:13creating our first one with median
10:05:15salary evaluating for median salary in
10:05:18the United States
10:05:20so I want to create this measure inside
10:05:22of our data job salary column sorry data
10:05:24job salary table and for this we're
10:05:27going to call it median salary us we're
10:05:31going to be using the calculate function
10:05:33for this and inside of here we're going
10:05:35to insert the ex an expression so in our
10:05:38case the expression is the median of the
10:05:41salary year average column and what
10:05:45we're going to do actually I'm just
10:05:46going to leave this is cuz filter is
10:05:48optional we can tell filter is optional
10:05:49based on the square brackets around it
10:05:51I'm going to just close out this
10:05:53calculate function change this to a
10:05:55format of currency with zero decimal
10:05:57places and then from there take that
10:05:59median salary us and actually drag it
10:06:01onto here so right now calculate is
10:06:03working by calculating the median salary
10:06:07and there's no filters applied to it so
10:06:09pretty simple so let's go in and
10:06:11actually edit this measure now now
10:06:13remember we have an explicit measure of
10:06:15median salary so I actually don't even
10:06:18need to Define it like I did here I can
10:06:21actually just call out median salary in
10:06:23this case kicking okay still the same
10:06:26value going back in and actually editing
10:06:28it we now want to apply a filter
10:06:32specifically for this filter we want to
10:06:35make sure that the job country column is
10:06:38equal to United States so I'm going to
10:06:41type in job country and we can use
10:06:44logical operators so I'm going to use an
10:06:46equal sign right next to this and I'm
10:06:48going to specify United States need make
10:06:51sure it's spelled exactly right I know
10:06:53it's that via the column okay so now
10:06:56we're going to leave everything out El
10:06:57as is click okay and Bam now it has the
10:07:02median salary filtered by the US and I
10:07:05can confirm this by scrolling down to
10:07:07the United States clicking United States
10:07:10and seeing that these values are the
10:07:11same but no matter what I actually click
10:07:14the United States median salary is going
10:07:16to stay the same additionally if you
10:07:19noticed here when I click on something
10:07:20like the US virsion Islands would am I
10:07:22moveing there they only have four job
10:07:25titles available so because of that they
10:07:28just filter this table down to only show
10:07:31those four that are applicable it along
10:07:33with their applicable salaries in median
10:07:35salary in the US so now let's calculate
10:07:38the median salary for non us countries
10:07:40and actually see how they differ so come
10:07:42into D job salary select add measure for
10:07:45this we're going to be using non us
10:07:47values once again we want to use that
10:07:48calculate fun function on the median
10:07:51salary measure that we created and for
10:07:54this one we're still evaluating the job
10:07:58country but we want it not equal to so
10:08:00we're going to use basically a less than
10:08:02and greater than sign right next to each
10:08:03other say not equal to and we'll say
10:08:06United States we're going to format this
10:08:08as a currency with zero decimal places
10:08:10click okay and then add this bad boy to
10:08:13the values and I want to actually see a
10:08:16country with more job postings in it so
10:08:19we'll go to something like Australia and
10:08:21now something like Australia we can see
10:08:24one comparing us to non Us in general
10:08:27the US well except for data Engineers
10:08:30yeah it looks like only data Engineers
10:08:32are the lowest one in another country
10:08:33everything else is higher in the US but
10:08:35now we can with this one compare hey
10:08:38what does it look like something like
10:08:39Australia compared to us and non us
10:08:42countries so super useful in actually
10:08:44filtering down providing the right
10:08:46context for what we want to look at so
10:08:48as a data analyst median salary is
10:08:50around 100,000 Which is higher than us
10:08:53and also any other non us median salary
10:08:55so may have to move to Australia one
10:08:58last clean up right quick slicer itself
10:09:00I don't like it to say job country we're
10:09:02going to name this to country all right
10:09:04now wrap up the analysis for this all
10:09:07right so you now have some practice
10:09:08problems to go through and test out
10:09:10these different Dax functions that we
10:09:12just went through along with some others
10:09:14now in this lesson we just did some
10:09:15basic dacks in the next one we're going
10:09:17to be moving into some more advanced
10:09:19Stacks features that I do find myself
10:09:21using from time to time but overall most
10:09:24of the stuff we apply in this lesson I
10:09:26use dayto day all right with that see
10:09:28you in the next lesson we'll be wrapping
10:09:29up basically our final question in our
10:09:31project and be done with our project see
10:09:33you
10:09:37there all right welcome to the last
10:09:39lesson in this course where we're going
10:09:41to be going over more advanced decks
10:09:44specifically we're going to be focusing
10:09:46more in depth on fil fter and also
10:09:51relation or relationship type functions
10:09:55these are going to be needed by our data
10:09:56model in order to calculate what is the
10:09:59salary or median salary for an
10:10:01Associated skill if you remember back to
10:10:04a few lessons ago we had relationship
10:10:06issues I know I feel that with having
10:10:09them being able to filter tables in
10:10:11certain directions and we're going to be
10:10:13able to see that and fix that in this
10:10:15lesson
10:10:19so in this lesson you can start with
10:10:20some the workbook from the last lesson
10:10:23or if you got lost dur in the way you
10:10:25can go into the Dax intro workbook now
10:10:28let's do a quick overview of where we're
10:10:30at with which analysis we've done for
10:10:32this project we've identified what are
10:10:35the top skills of data nerds along with
10:10:38different filters to filter for whatever
10:10:40our interest is in my case I'm looking
10:10:42for data analyst in the United States
10:10:44and I can see that se SQL Excel and
10:10:46Tableau are some of the highest
10:10:47additionally we've zoomed out a little
10:10:49bit and been able to identify based on
10:10:52job titles where our job title of
10:10:54Interest Falls compared to others and
10:10:56how many skills it requires for data
10:10:59analysts it's right above did business
10:11:01data analysts and based on the number of
10:11:02skills it looks like it's appropriately
10:11:04rewarded for the median salary and then
10:11:06final thing we did was be able to
10:11:08analyze additionally Based on data
10:11:11analyst we can look at different
10:11:12countries and compare it not only in
10:11:14that country but to within the US and
10:11:16outside the US so a lot of good stuff
10:11:19related to well data analyst that
10:11:22position and analyzing the salary but
10:11:24what about skills well we haven't done
10:11:27that yet we're going to get into
10:11:29actually analyzing in this first portion
10:11:32analyzing what is the expected median
10:11:34salary based on one of the top 10 skills
10:11:38we did this back in the power query
10:11:40lesson but now we have this new data
10:11:41model we need to recalculate it anyway
10:11:43we're going to run into some issues with
10:11:44the data model as we're going to find
10:11:46out additionally we're going to be
10:11:47calculating the skill likelihood instead
10:11:50of skill count basically finding the
10:11:53percentage of a skill in a job posting
10:11:56this is somewhat complex so this portion
10:11:58here will be optional and you'll be able
10:12:01to use job count instead if you don't
10:12:02want to follow along with this skill
10:12:06likelihood anyway back in your workbook
10:12:09whether you started from that uh Dax
10:12:11intro or you're continuing on with from
10:12:13the last lesson we're going to create
10:12:15this new sheet for this and for this
10:12:18we're going to name this
10:12:19skill salary analysis as usual we're
10:12:22going to go in and insert in a pivot
10:12:24table from our data model so we can get
10:12:26into analyzing the skills going click
10:12:28okay insert it in and so for this I want
10:12:31to analyze what is the median salary for
10:12:34a skill so if I drag the job skills from
10:12:37the data jobs skills table into the rows
10:12:40we have all the different skills pop up
10:12:43underneath here and then if we went up
10:12:46here and then tried to drag or we will
10:12:48be dragging in the median salary into
10:12:51here all these values are going to be
10:12:54the same addition we get this popup
10:12:56right here that relationships between
10:12:57tables may be needed basically we're
10:12:59running into an issue with our data
10:13:01model even if I click autod detect it's
10:13:03going to tell me no new new
10:13:04relationships are found so what's going
10:13:06on here well let's actually analyze our
10:13:09data model by going to manage and then
10:13:12inside of here go into diagram view so
10:13:15the air resides with their filtering
10:13:18dire remember this Arrow right here
10:13:21signifies which way we can actually
10:13:24filter our data so in our case we have
10:13:27job skills which is over here in the
10:13:30data job skills table and we're trying
10:13:32to find the median salary the problem is
10:13:36is we're basing that off of that salary
10:13:37or average value that's in the data jobs
10:13:40salary table and based on the direction
10:13:43of this Arrow we cannot flow in the
10:13:46opposite direction this is what we're
10:13:48call oneway way or single filtering now
10:13:51unfortunately Excel doesn't support bir
10:13:54directional filtering however in things
10:13:56like powerbi you can actually go in and
10:13:59change it from single filtering to both
10:14:03or bir directional filtering kind of
10:14:05makes me wish I was in powerbi right now
10:14:08so back in Excel we can't actually
10:14:10control this via here and actually click
10:14:13it to change this to bir directional
10:14:15filters we can only control the
10:14:16relationship itself but we we can use
10:14:19Dax to fix this now in order to fix this
10:14:22relationship we actually have
10:14:24relationship functions inside of Dax
10:14:27specifically we're going to use this
10:14:29cross filter function with this function
10:14:32you put inside of cross filter the
10:14:34column names so in our case we can
10:14:37specify basically the job ID from job
10:14:39salary and the job ID from data job
10:14:41skills and then from there we specify
10:14:43the direction which the parameters under
10:14:46here we can go into what we can provide
10:14:48to directions we can either provide none
10:14:50basically don't create a relationship
10:14:52both which is what we want filters on
10:14:54either side or one way which is what we
10:14:58have already we're not going to use this
10:15:00you also control filters left or filters
10:15:02right the one way we're also not messing
10:15:04with that we want both now this cross
10:15:07filter remember is a filter function so
10:15:10we need to use this in an appropriate
10:15:12for formula that we already know
10:15:14calculate in order to filter so I'm
10:15:16going to x out of this box right here
10:15:18cuz that's not applicable
10:15:19what we're going to do is I'm going to
10:15:21calculate median salary or a new median
10:15:24salary if you will inside of the data
10:15:26jobs skills table and because it's uh
10:15:30going to use the same name but we're
10:15:33going to keep it in a different table
10:15:34it'll be perfectly fine and then for
10:15:37this remember we want to use still
10:15:39calculate we want to have an expression
10:15:41in here in our case we want to calculate
10:15:44what is the median salary and we'll just
10:15:47use the explicit measure that we already
10:15:49defined then from there we'll get into
10:15:52the filter one of what we want to
10:15:54actually filter we want to provide for
10:15:56this cross filter and for this we're
10:15:58going to specify the job ID of one table
10:16:01along with the job ID of the other table
10:16:05then for the filter type we're going to
10:16:07use both okay I'm going to go ahead and
10:16:09close this now we're calculating median
10:16:11salary so I want this formatted as a
10:16:13currency with zero decimal places I'm
10:16:15going go ahead and click okay and have
10:16:17an error in my formula should have known
10:16:19that by the X I need to actually put a
10:16:21closing parentheses on here and I'll
10:16:23lied to you a measure a column with the
10:16:25name median already exists okay I
10:16:27thought we could do that it's Sil me so
10:16:29we'll name it median salary skills go
10:16:31ahead and click okay okay now I'm going
10:16:34to drag this into the values and we can
10:16:37actually see with this one now that the
10:16:39associated median salaries are actually
10:16:42there and it's not all that 115,000
10:16:45which is basically the median of the
10:16:46entire data set so I'm going to go ahead
10:16:48andove move this other median salary out
10:16:51of here and from there we're going to
10:16:53also drag skill count into here I just
10:16:56want to look at the top 10 most common
10:16:59skills in this case so I'm going to go
10:17:00up here into our filter and go to our
10:17:03value filters for top one dot dot dot we
10:17:05want the top 10 items by in this case
10:17:08skill count and then from there based on
10:17:11these top 10 skills I'm going to sort it
10:17:14from largest to smallest but like usual
10:17:17this is no good unless we don't actually
10:17:19analyze for the country and also for the
10:17:23title or job title so if I actually go
10:17:25back into that skill jobs analysis I can
10:17:27just select these two slices right there
10:17:30pressing control then copy it and paste
10:17:34them into here now you may notice
10:17:35whenever I'm clicking this this is not
10:17:37affecting this pivot table right here so
10:17:40we can actually inspect this by going to
10:17:42the slicer and going to report
10:17:45connections right now this slicer is
10:17:47only affect ing the skill job analysis
10:17:51tab so this one right here in our case
10:17:53for this job title we actually want to
10:17:55affect it on this page here of skill
10:17:58salary analysis which is right down here
10:18:00click okay looks like the salary is
10:18:02updated also we want to do the same
10:18:04thing for Country adjusting the report
10:18:07connections for this as well and
10:18:09selecting this one right here for
10:18:10underneath the sheet of skill salary
10:18:13analysis clicking okay bam it updated as
10:18:16well so now looking at the top skill of
10:18:19data analyst in the United States which
10:18:20I'm pretty familiar with I can see
10:18:22things like python Oracle and Tableau
10:18:24are top three Excel does make the list
10:18:27and it's the second to last at 84,000
10:18:29now with this I do want a visualization
10:18:32with it specifically I want a combo
10:18:35chart showing this so I'm going go into
10:18:37insert pivot chart pivot chart and for
10:18:40this go down to combo for this I want
10:18:42the median salary to be the main focus
10:18:44and then for the skill count we're going
10:18:46to put that on a secondary axis because
10:18:49right now it's just way too low if we
10:18:50keep it on the same axis and this has
10:18:52the format that I want right here go
10:18:53ahead and click okay I'm going to hide
10:18:55all the field buttons on the chart I'm
10:18:57going to add a primary vertical and also
10:18:59a secondary vertical axis along with a
10:19:02chart title and then for the legend
10:19:05itself I'm going to click it and then
10:19:06rightclick it and go to format Legend
10:19:09and for this it should go under Legend
10:19:11options Legend options Legend options
10:19:13I'm going to unclick this of show The
10:19:15Legend without overlapping the chart and
10:19:16I'm just going to move it up here so not
10:19:19bad I don't necessarily want this orange
10:19:22line right here for the skill kind I
10:19:24don't really feel like a line is best to
10:19:26signify the count instead what I'm going
10:19:28to do is select the line and if it
10:19:30doesn't appear the format data series
10:19:32you can also just right click it go to
10:19:33format data series and then underneath
10:19:36fill and line they have line but also
10:19:39marker for the line we're going to go no
10:19:41line and then for the marker we're
10:19:43actually going to change the marker
10:19:45options to builtin we'll change it to
10:19:48this square is going to be fine or we
10:19:50can change it to a diamond we'll make it
10:19:53slightly bigger and I don't really like
10:19:55the color so I'm going to go into design
10:19:57and change the color to this
10:19:59monochromatic pallette 8 nope never mind
10:20:02not that one I meant monochromatic
10:20:04palette one I want the bar charts to be
10:20:06more visually popping than the actual
10:20:08markers themselves I change the title
10:20:11two what's the pay of the top 10 skills
10:20:13and then change the primary access to
10:20:15median salary USD and the other one one
10:20:18to job count closing this out and then
10:20:21making some room over here for the
10:20:23actual visualization itself so now we
10:20:25have our visualization that we want that
10:20:27looks at this and be able to show us
10:20:29what are the top 10 skills for data
10:20:31analyst and their Associated pay now one
10:20:34last thing for this regarding slicers I
10:20:37want to actually make it to where
10:20:38they're connected between the charts so
10:20:41right now I have it to where this
10:20:43basically this one for skill salary
10:20:45analysis tab if I go over to the skill
10:20:47job analysis tab select business analyst
10:20:50it will change then go go back to skill
10:20:52salary analysis it updated to business
10:20:54analyst anyway I wanted to if we change
10:20:56a slicer to make sure that it changes on
10:20:58the appropriate sheets so the job title
10:21:01slicer is only on these two sheets
10:21:04actually that one's perfectly fine but
10:21:06the one we actually have concerns with
10:21:07now is the country specifically on this
10:21:10one I'm selected on the United States
10:21:12the skill job analysis one it's also on
10:21:14the United States and updates
10:21:15appropriately but then if we look in the
10:21:17salary analysis that one's on Australia
10:21:20it's not updating appropriately so we
10:21:21need to go to slicer report connections
10:21:24and we're going to be putting the
10:21:26country one on all the different sheets
10:21:28so I'm going to go ahead and select all
10:21:29the sheets for this I'm going to do the
10:21:31same for skill salary analysis country
10:21:34slicer which it looks like it updated
10:21:37along for the skill job analysis so what
10:21:38I'm going to do is actually copy this
10:21:39now and put this into the salary verse
10:21:43skills because we're controlling it on
10:21:45this page as well and so now whatever I
10:21:48select select something like maybe
10:21:49United Kingdom it will update
10:21:51appropriately and update on other sheets
10:21:55as well anyway quick one quick note
10:21:57because we move those titles around that
10:22:00one time sometimes it's not going to
10:22:02match up exactly how we had it before if
10:22:04you recall I'm going to go ahead and
10:22:06select all we set up these text box in
10:22:09order to view them whenever basically
10:22:11all countries were selected so that is
10:22:13one of the issues about dragging and
10:22:15dropping those titles and making them
10:22:16stick to a certain location it messes it
10:22:18up your filters whenever you want to
10:22:20filter down for something like the
10:22:21United
10:22:24States so this wraps up basically our
10:22:27four major analysis that we did now I'm
10:22:30going to take it a step further this
10:22:32portion will be completely optional and
10:22:36that's this right now we're using skill
10:22:40count in order to look at what is you
10:22:44know the skill count of in this case for
10:22:45data analyst in we'll do United States
10:22:49we see that SQL is around 400 4,000 and
10:22:53that Excel is around 3500 but what does
10:22:56that actually mean well if we go to the
10:22:58Future file of what we're going to get
10:22:59to we're actually going to be
10:23:00calculating a skill likelihood instead
10:23:04which in this case is looking at what is
10:23:06the proportion of a skill compared to
10:23:10all the different jobs that are
10:23:12available for data analysts in the
10:23:15United States and so that 4500
10:23:18and almost 3500 is equal to well greater
10:23:21than 50% for SQL and about 40% for Excel
10:23:25so that makes in my mind a lot clearer
10:23:28how important that skill is over account
10:23:31in that you probably should be learning
10:23:33SQL and Excel as a data analyst so back
10:23:36in our sheet where we're actually
10:23:37calculating with the job count how do we
10:23:41calculate this well let's actually get
10:23:44to moving this over to here go back into
10:23:47our pivot table self and if we throw up
10:23:50the job count you may get this
10:23:53relationship between toils maybe needed
10:23:55don't worry about it too much now these
10:23:57values are all stagnant based on some
10:24:00issues with the filter Direction but
10:24:02that actually comes to our advantage
10:24:05because for our filter right here
10:24:07specifically data analyst in the United
10:24:09States the amount of jobs that actually
10:24:12are are
10:24:148339 if I actually remove both of these
10:24:17filters we would expect it to be the
10:24:19total rows of the column which is
10:24:2332672 so coincidentally this is actually
10:24:26doing what we need we just need to get a
10:24:28percentage of these two values and that
10:24:30can be done pretty easy so let's open
10:24:33the show field list and actually get
10:24:35into creating this measure we're going
10:24:37to create in the data job skill table
10:24:39we'll call this skill likelihood and
10:24:41what this will do is take skill count
10:24:44and divide it by job count but remember
10:24:46we probably want to use the divide
10:24:47function for for this so putting in
10:24:49skill count and then job count now
10:24:52there's no option to format this as a
10:24:54percentage unfortunately so I'm going to
10:24:55go ahead and click okay from there I'm
10:24:57going to drag the skill likelihood into
10:24:59the values and go through and format
10:25:01this appropriately selecting that it's a
10:25:04percentage and then with this I'm going
10:25:06to select something that a value that I
10:25:07know what it should be of data analyst
10:25:09in the United States and with those
10:25:11values selected I can see that Excel is
10:25:14at 41% which I know that's what it is
10:25:17and SE is at 53% for these values so bam
10:25:21we have this skill likelihood now we can
10:25:24now go in and remove these other two
10:25:25columns of skill count and job count and
10:25:29then from here actually move this graph
10:25:31back over and unfortunately with the
10:25:33adjusting to it we actually have to fix
10:25:35this and turn this back into a combo
10:25:38chart so we're going to design change
10:25:39chart type into combo select for the
10:25:43skill likelihood we want this to be on
10:25:45the secondary axis click okay go back to
10:25:48format data series remove the line and
10:25:51then change the marker option to be
10:25:53built in and to be that diamond at 6
10:25:56point and then finally update that
10:25:57secondary access to basically say it's
10:25:59skill likelihood and Bam now we have
10:26:02this final visualization now there's one
10:26:03more that we actually do need to clean
10:26:05up and that's this one right here what
10:26:07are the top skills of data nerds right
10:26:10now we're doing a count of the job ID an
10:26:12implicit measure which you know how I
10:26:15feel about that we should use an
10:26:16explicit measure specifically we're
10:26:17using skill likelihood instead of that
10:26:20and remove that count of job postings
10:26:22once again I need to actually format
10:26:24this as a percentage so going to home
10:26:26change it to a percentage and then from
10:26:28there clicking in it and sorting from
10:26:31smallest to largest and Bam for this one
10:26:34data analyst in the United States once
10:26:36again we can actually see visually what
10:26:38are the top skills for this so now we
10:26:39just updated both of these charts to
10:26:41have a more represen istic understanding
10:26:44of what's going on with the data all
10:26:46right so you should be super proud of
10:26:48what we just accomplished in this
10:26:50project going through both power query
10:26:52and power pivot and actually diving deep
10:26:54to understand some key statistics about
10:26:57top paying skills and also top skills
10:26:59you should be targeting depending on
10:27:01what job you're pursuing and what
10:27:03country you're in now do have some
10:27:04practice problems go through and test
10:27:06out some of these more advanced
10:27:07functions specifically this cross filter
10:27:09function that we went over then after
10:27:11that in the next lesson we're going to
10:27:12be getting into how we can actually go
10:27:14about sharing this project for those
10:27:16that purchase the course practice ice
10:27:18problems and also certificate you can
10:27:20now go through and complete that end of
10:27:22course survey and you'll be rewarded
10:27:25this course certificate now if you
10:27:26didn't do this it's not too late for you
10:27:28to go in and purchase the course so way
10:27:31you get this course certificate all you
10:27:32got to do is go in and take that Endor
10:27:34survey and you'll get it all right
10:27:36congratulations on your work so far see
10:27:37you in the next
10:27:42one all right congratulations again for
10:27:44finishing that last project in this
10:27:47video and the next video which are the
10:27:49last two videos of this entire course
10:27:52they're going to be focused on how to
10:27:54actually go through and share your
10:27:56projects in my recommended way
10:27:58specifically we're going to be sharing
10:28:00this on GitHub so that way others can
10:28:02see it here I am on GitHub and also if
10:28:05you didn't notice there where you
10:28:06actually downloaded all those Excel
10:28:08files at the beginning of this course
10:28:10anyway inside of here is where I'm
10:28:13hosting my different projects and you've
10:28:15gone through and probably seen this but
10:28:17you may not have clicked on something
10:28:18like the project One dashboard and in
10:28:21this case yeah I have the Excel file but
10:28:23that read me in there displays below
10:28:27this and this is what we're actually
10:28:29going to be doing in the next two videos
10:28:31to set this up and then create this read
10:28:33me and this allows you to detail all the
10:28:36different skills that you used along
10:28:39with detailing all the different
10:28:41analysis that you did while going
10:28:43through this now that was Project one
10:28:45project two is going to follow a similar
10:28:48method and that it has the Excel file
10:28:50and the readme and then in the readme
10:28:52itself it details all the different work
10:28:54that we did in
10:28:57it so you may be like Luke why the heck
10:29:00am I going to be using GitHub in order
10:29:02to share this project I'm not familiar
10:29:04with it I don't know how to use GitHub
10:29:06at all why am I going to waste my time
10:29:07with it well I think it's useful not
10:29:10only in Excel but also other
10:29:12Technologies specifically programming
10:29:14here I have my SQL project for my SQL
10:29:16course and this this is where I host my
10:29:20SQL code and all the different analysis
10:29:22that I did for it and similarly for my
10:29:25python course and the project we
10:29:26creating that I also hosted on GitHub
10:29:29and detailed all the different the steps
10:29:30that we did along with all the different
10:29:33uh python files associated with it so
10:29:35more the story is I think github's a
10:29:37great tool to use in order to share your
10:29:39work not only in Excel but also other
10:29:41tools now if you recall from Project one
10:29:43we walk through the steps to quickly
10:29:45share your project on one drive if you
10:29:48had it accessible via like a paid
10:29:50Microsoft subscription and this provided
10:29:52a method to go through and share if you
10:29:55go up here and actually copy the link a
10:29:58usable link for others whether they have
10:30:00Excel or not to actually go in and then
10:30:03manipulate your dashboards that you have
10:30:05so you may be wondering why the heck are
10:30:07we not doing this with this second Excel
10:30:10file that we created with all of our
10:30:11analysis and then sharing it via this
10:30:13method well if you're called back to
10:30:15this handy Dan table of the different
10:30:17Microsoft versions and the different
10:30:19skills or basically Technologies within
10:30:21Excel that it uses Microsoft online
10:30:24which where we hosted that first project
10:30:26at doesn't have the capabilities of
10:30:29power query or power pivot because of
10:30:32that I could go through the process of
10:30:33adding the second project to this which
10:30:36it's this file right here I'll open it
10:30:38up then actually investigating it well
10:30:41it does if you investigate all the
10:30:42different sheets does go through and
10:30:44actually show the analysis that we did
10:30:48but if you actually get into
10:30:49manipulating it like in this case let's
10:30:51say I wanted to see what are the top
10:30:52skills of data analyst you're going to
10:30:54get this popup right here that says this
10:30:56workbook contains external data
10:30:57connections or bi features that are not
10:30:59supported basically power pivot and
10:31:01power query aren't supported it can't
10:31:03actually query the data it's just
10:31:05showing the basic last snapshot of the
10:31:07data right here and you can't manipulate
10:31:09it so in this case Microsoft online
10:31:12becomes pretty useless so that's why I'm
10:31:14recommending sharing it via GitHub as
10:31:17you can share all the associated files
10:31:19with this if somebody want to they could
10:31:20come in here and download it along with
10:31:22going through and actually detailing
10:31:24what you actually did so basically
10:31:26controlling the story line and sharing
10:31:29what the different analysis or insights
10:31:31that you actually found now this what
10:31:33you're reading right now is a read me
10:31:35and it requires understanding markdown
10:31:38and how to write and markdown so we're
10:31:40going to be covering that more in depth
10:31:41in the next video when we get into
10:31:44markdown and creating the read me this
10:31:46video is going to be primarily focused
10:31:48on just getting this project into GitHub
10:31:51so what are we going to be doing for
10:31:52this well we have five major steps we
10:31:54need to get through the first thing is
10:31:55installing git which is the core
10:31:57technology used behind GitHub we'll
10:32:00explain more in a bit second and third
10:32:02we'll be going through actually setting
10:32:03up our GitHub account and then
10:32:05installing GitHub desktop to then manage
10:32:08with Git our different folders and
10:32:10projects and then fourth and fifth we'll
10:32:13be basically initializing the repository
10:32:15which is a fancy term for a folder and
10:32:17from they are getting that folder
10:32:19repository onto GitHub to then share so
10:32:23before we install it what the heck is
10:32:25git well similar to how they have track
10:32:28changes and stuff like word and
10:32:30PowerPoint git does this it's a Version
10:32:33Control System it tracks changes in not
10:32:35only files but also code and because of
10:32:39all this it allows you also to
10:32:40collaborate with others when working on
10:32:42a project git is the core technology
10:32:46behind maap managing all these different
10:32:49things going on on your own local
10:32:51computer and then whenever you make any
10:32:54of these changes get Hub is where it
10:32:56keeps track of these final changes if
10:32:58you will and then displays it for the
10:33:00world to see and also pull those changes
10:33:02so here's my Excel di analytics course
10:33:05right here on GitHub and I have the same
10:33:07folders or repository on my own local
10:33:11computer now there's actually hidden
10:33:14folders or git folders in here managing
10:33:16this and I can do a shortcut on Mac of
10:33:19command shift period to show that but
10:33:21anyway I wanted to mainly show this of
10:33:23this dogit folder in here and this thing
10:33:26I don't necessarily touch this at all or
10:33:27work inside of it this.get folder
10:33:30contains all the different revisions and
10:33:33tracks all the different changes within
10:33:35my project so in order to get this git
10:33:38folder inside your project and then also
10:33:42get it into GitHub we need to actually
10:33:43install git
10:33:48so navigate over to the git website into
10:33:50their downloads select your operating
10:33:52system Choice whether Mac OS or Windows
10:33:54I want a Windows machine right here and
10:33:56from there I'm going to select the
10:33:5864-bit version for Windows and click
10:34:00here to download Once download I'm going
10:34:02to open the file as do I want to allow
10:34:04this to make changes in my device yes I
10:34:06do and then it's going to walk you
10:34:08through the setup process for git all of
10:34:12these things are going to be left as
10:34:14default so feel free to just go through
10:34:16and select it all after I've left all
10:34:18the default settings as is and selected
10:34:20that it then gets into the actual
10:34:22install itself looks like it installed
10:34:24properly we'll go ahead and click finish
10:34:26we can confirm it's installed by opening
10:34:28something like terminal and you should
10:34:30have a terminal app installed this is
10:34:32just confirming it you don't necessarily
10:34:33have to do this anyway mine opens in a
10:34:35Powershell and you can just type
10:34:36something like get and it shouldn't give
10:34:39you an error message it should instead
10:34:42give you how you could go about using
10:34:44git via the command line in terminal
10:34:46don't worry don't be AF of this we're
10:34:48not going to be using git via the
10:34:49command line although I may need to make
10:34:51a separate course on that instead we're
10:34:53going to be using GitHub desktop to
10:34:55manage
10:34:59git so in order to use GitHub you need
10:35:01to have an account if you already have
10:35:03an account you can feel free to just
10:35:04sign right on in but if you don't go
10:35:07through the whole process of entering
10:35:08your email providing your different
10:35:10credentials and then getting logged in
10:35:12once logged in it should direct you to
10:35:14your homepage if it doesn't you can come
10:35:15up here to this icon at the top and from
10:35:18there just select your profile I would
10:35:20go through at this point and actually
10:35:22customize your profile specifically
10:35:24adding a picture your name a little
10:35:27description and any social media links
10:35:30over here on the right hand side of on
10:35:31my homepage I have some different pinned
10:35:34repositories because you just set it up
10:35:36you probably have none but this is where
10:35:37we're going to be putting your Excel
10:35:40project when you're complete so that way
10:35:41if people navigate to your profile they
10:35:43can see it now that we have this account
10:35:45we need to actually get our project or
10:35:48our repository onto GitHub but
10:35:52unfortunately there's not really an easy
10:35:55method I've found with actually using
10:35:57the UI from the website to do this and
10:36:00that's mainly because there's a lot of
10:36:02technical things going behind the scenes
10:36:04and managing
10:36:08git instead I'm going to recommend
10:36:10downloading github's application to
10:36:13install on your computer they have it
10:36:15for both Mac and windows navigate to
10:36:17this link here and for this we I'm going
10:36:20to go ahead and just download the 64-bit
10:36:23version of this application this one's a
10:36:25lot easier to install than get from here
10:36:27once we have it downloaded I'm going to
10:36:29open the file the installer should open
10:36:31this window for you to next sign into
10:36:34GitHub once you've enter your
10:36:36credentials for GitHub you'll use this
10:36:38to configure git and for this you're
10:36:41going to basically say hey I want to use
10:36:42GitHub account and name and email
10:36:44address to manage all this and click
10:36:46finish now it should navigate you to the
10:36:49let's go started screen anyway it has
10:36:51methods for you to go through and create
10:36:53a tutorial repository if you want to
10:36:55we're going to be doing that and it has
10:36:57some different options for this that you
10:36:58can also select via the file menu such
10:37:00as a new repository add local repository
10:37:03or clone repository we're going to be
10:37:05creating a new repository and as a
10:37:08reminder repository it's basically a
10:37:10fancy name for a folder but it's a way
10:37:12for us to maintain and collect all of
10:37:15our different files and not for what
10:37:18we're using in our project so for this
10:37:20we need to give it a name so I'm going
10:37:22to give it some descriptive like Excel
10:37:23project data analytics and for
10:37:26description I'll just give the simple
10:37:27one of my project Dem maturing my Excel
10:37:29skills for the local path we need to
10:37:31actually point it to the folder that has
10:37:33this so mine is inside my documents
10:37:35folder and real quick inside that folder
10:37:38itself right now I would expect you to
10:37:41have the project one and project two I
10:37:44also going to be putting all the
10:37:45different files that I have for the
10:37:48different Excel workbooks that we work
10:37:50through in the lesson if you don't have
10:37:51them don't feel like you need it the
10:37:53main important thing is that you have
10:37:54both project one and project 2 in there
10:37:56and I have them conveniently located in
10:37:59different folders inside of here never
10:38:01getting out of that so I can select this
10:38:03Excel project. analytics folder I'm
10:38:05going to select this folder it's going
10:38:07to ask if I want to initialize this
10:38:08repository with a read me I do as far as
10:38:11the get ignore I'll put none and license
10:38:13none as well and we'll create the
10:38:14repository so now you're going to be
10:38:16navigated to this screen here here which
10:38:17is basically the default screen of
10:38:19GitHub desktop it allows you to select
10:38:22different repositories right now I have
10:38:24only the Excel project analytics one it
10:38:27allows you to select different branches
10:38:28we're going to say on one shifting to
10:38:30another Branch beyond the scope of this
10:38:31course then up here at the top it has
10:38:33something like publish repository which
10:38:35we want to do but one quick thing real
10:38:37quick I can actually investigate what
10:38:40files are going to be pushed up to
10:38:43GitHub by going here into history and
10:38:47right now it's just one I selected that
10:38:49box for read me so the readme is in
10:38:51there and the other one's just do get
10:38:53attributes the other ones aren't in
10:38:55there and I'm doing this on a Windows
10:38:57machine well if I navigate back to the
10:38:59folder that contains my project so here
10:39:02I have Excel project. analytics which I
10:39:04selected two from the GitHub desktop
10:39:07whenever I go into it it actually
10:39:09created another folder inside of it and
10:39:14that has theget attributes and read me
10:39:16that it's talking about about now I've
10:39:18done this on both Windows and Mac and
10:39:21Mac doesn't cause this issue of putting
10:39:23another folder inside your other folder
10:39:26so for Mac users you may not have this
10:39:28problem so completely ignore this but
10:39:29for Windows user this is a problem
10:39:32because this right here is the project
10:39:34or the folder was going to get uploaded
10:39:36to GitHub so what we need to do is take
10:39:38all the contents of this by selecting it
10:39:41all and just pressing control to select
10:39:43it all and then dragging it into that
10:39:46folder so a little confusing but if we
10:39:48go back to the documents we have our
10:39:50Excel project. analytics folder then
10:39:52inside of that we have our GitHub repo
10:39:54and then now navigating back into GitHub
10:39:59desktop I go over here and I see changes
10:40:02we have 85 of 8 five different files and
10:40:04folders within there it's actually
10:40:06picking up on all those different files
10:40:09that I have in there once again if
10:40:11you're on a Mac you may not see this
10:40:12because it's already in there in history
10:40:15and you can see it's actually within the
10:40:17this portion of the guy anyway the thing
10:40:20now is if we go ahead and publish this
10:40:23repository to GitHub it's only going to
10:40:25have what's inside of our history right
10:40:28now under this what we're calling a
10:40:30commit and a commit is a snapshot of
10:40:34your
10:40:35repository at the time that you're
10:40:37basically committing it so we need to do
10:40:40a commit in order to get all these
10:40:42different changes into a repository cuz
10:40:45technically right now they're in an area
10:40:47called a staging area or the working
10:40:49area anyway we need to provide a summary
10:40:51that's required and I'm going to add
10:40:53something simple like add all Excel
10:40:55files doesn't need to be super
10:40:56descriptive and from there I'm going to
10:40:58click commit to main now if I go into
10:41:01history I have this initial commit that
10:41:03it did but then that add all Excel files
10:41:07it's going to then have in all those
10:41:08different Excel files that I added into
10:41:13it so now that our local repository on
10:41:16your machine is is up to date we need to
10:41:18then publish this repository to GitHub
10:41:21and we can either click this button or
10:41:22this button here for this we're going to
10:41:23keep the same name and description that
10:41:25we have before we don't want to keep
10:41:27this code private so we're going to
10:41:28uncheck that box and then from there
10:41:30we're going to click publish repository
10:41:33so my repository has quite a bit of
10:41:35Excel files and the memory size of it is
10:41:37pretty large so it is taking a little
10:41:39bit of time to do this so now we've
10:41:42completed pushing our local repository
10:41:44to our remote repository on GitHub so
10:41:46inside of GitHub I can navigate up here
10:41:49to the right hand side and I go to your
10:41:52repositories and here it is the Excel
10:41:55project data analytics that we made
10:41:57public and it's all in here so now
10:42:00somebody can come in here and see our
10:42:02different work in this case our project
10:42:04One dashboard is inside of here we have
10:42:06our Excel file in there and Bam we've
10:42:09set up git and also GitHub and that was
10:42:13a push so now we need to demonstrate
10:42:15what is a pull
10:42:20and so in order to do that a pull
10:42:22request we need to actually make changes
10:42:24on our remote repository so that on
10:42:27GitHub and then pull it into our local
10:42:31repository so here's what we can do for
10:42:33that I'm going to just go in and we
10:42:35created this read me. markdown file upon
10:42:39creation because we selected that
10:42:40checkbox you can actually come in here
10:42:43and edit this read me by clicking the
10:42:45edit file button and and I'm just going
10:42:47to come in here and I'm just going to
10:42:48say hey I added this on github.com
10:42:52adding it in the bottom now we're going
10:42:54to go into markdown formats and stuff as
10:42:56you can see we have this hashtag here
10:42:58we're going to go all that in the next
10:43:00lesson but anyway I made this changes to
10:43:02here so we need to like we did on our
10:43:04local repository and making a change we
10:43:06need to commit those changes here and
10:43:09conveniently it just gives us a commit
10:43:11message of update read me confirm the
10:43:13correct email and it conects directly to
10:43:15the main branch we're just staying on
10:43:17that Branch we're not shifting for this
10:43:18course at all from there I'm going to
10:43:20commit changes so now if I go back into
10:43:23the project itself scroll on down to see
10:43:25the read me I can see that I have I
10:43:27added this on GitHub whereas on my local
10:43:30machine if I go into look at the readme
10:43:34markdown it doesn't have that addition
10:43:36that I added to the readme file so we
10:43:39need to pull those changes going back to
10:43:42the GitHub desktop app I'm going to come
10:43:44up here and you notice that it says
10:43:46fetch or this isn't going to do anything
10:43:48this is just going to fetch origin
10:43:49basically the main branch and Pull It in
10:43:52this isn't going to make any changes to
10:43:53your file it's just going to update it
10:43:55of what's on GitHub and we can see based
10:43:58on this that we have basically one
10:44:01change here by this one and this down
10:44:04Mark and so in order to get these
10:44:06changes we need to pull the origin pull
10:44:08it and so I'm just going to click it to
10:44:10pull and now when we go into the history
10:44:13we now have this new one of update read
10:44:15me we can see that this readme has this
10:44:18addition because it's in green of I edit
10:44:20this on github.com and then inspecting
10:44:22this in the readme itself it now updated
10:44:25to say hey I added this on github.com so
10:44:27bam we just demonstrated how to push and
10:44:30also pull from our local repository and
10:44:33machine to our remote
10:44:35repository so now that we have GitHub
10:44:38and git all set up we now need to get in
10:44:40to actually building out those readms
10:44:43and explaining what we did in our
10:44:45project and demonstrating those skills
10:44:47that we gained in this course so that's
10:44:49what we'll be doing in the next lesson
10:44:51if you're getting stuck at any point
10:44:53during the way I highly recommend that
10:44:54you take use of something like chat gbt
10:44:57or even gemini or whatnot and actually
10:44:59paste in your error code and it will
10:45:02help you with troubleshooting it it's a
10:45:04lot quicker than posting a comment in
10:45:06here saying that you had an issue all
10:45:08right with that see you in the next one
10:45:09we're getting into the Remy see you
10:45:14there welcome to the last video in this
10:45:18course and in this we're going to be
10:45:19going over how we're going to actually
10:45:22document all the different work that you
10:45:24did for project one and for project two
10:45:27we're going to putting this into our
10:45:29markdown file or our read me and then
10:45:32from there getting it onto GitHub and
10:45:34then finally going through how to share
10:45:36it on LinkedIn so right now navigating
10:45:38to our GitHub repo with our project in
10:45:41it you should have at least two folders
10:45:44in there one for your project One
10:45:45dashboard and one for your project 2 if
10:45:47you have your other folders for all the
10:45:49work that you did for all the other
10:45:50lessons in this course that's awesome
10:45:53too but not required mainly just have
10:45:55your project work in there anyway we
10:45:57have this read me for the entire project
10:46:00itself and right now it's pretty Bare
10:46:02Bones and if we navigate into that
10:46:04project One dashboard right now you
10:46:05should have only have a file in there
10:46:08specifically that Excel file but we need
10:46:10also a readme in here as well so we can
10:46:12description add a description of what we
10:46:14did in that dashboard similarly project
10:46:172 doesn't have a read me as
10:46:20well now we have demonstrated in that
10:46:23last lesson how we can actually go into
10:46:25something like the readme and then from
10:46:28there edit it inside of your web browser
10:46:30by just clicking this edit this file
10:46:33icon it shows not only the edits for you
10:46:36to actually go through and maybe type
10:46:38something but also the preview itself
10:46:41itself of what the file is going to look
10:46:42like don't worry we're going to be going
10:46:44over markdown syntax in a little bit but
10:46:46anyway that's how we're going to be
10:46:47doing all these different changes to the
10:46:50files for this I'm not going to do these
10:46:52changes I'm actually going to cancel
10:46:53these changes now an alternate option to
10:46:56making edits to something like a readme
10:46:58is using a text editer or IDE integrated
10:47:02development environment such as
10:47:04something as Visual Studio code which is
10:47:06completely free and is I have it
10:47:08launched here in my app um is an app
10:47:10that I use in order to edit and manage
10:47:13my different files I can also go through
10:47:16if I'm editing the read me itself I can
10:47:18type inside of here and edit it but also
10:47:21during that I can actually go in and
10:47:24view what's going on with the actual
10:47:27read me itself off to the side while I'm
10:47:30typing here in this other window anyway
10:47:32I just want to make you aware of this
10:47:33that is an option for you to go through
10:47:36but it does take some experience with
10:47:38knowing how to use vs code setting this
10:47:40all up so based on the complexity we've
10:47:43already built up already we're going to
10:47:44stick to just editing our readms inside
10:47:47of github.com
10:47:50so before we get into building our
10:47:53project readms we need to understand
10:47:55some syntax here specifically if you
10:47:58notice this Excel project analytics is
10:48:00capitalized and everything else is
10:48:02lowercased and if we actually go in and
10:48:04edit the file we can see that we have
10:48:06this hashtag at the front which
10:48:08translates this into a heading so they
10:48:11have special characters that you can
10:48:13actually use in front or around text to
10:48:15manipulate text
10:48:17and the team that created markdown
10:48:18conveniently created this cheat sheet
10:48:20which I'll link here and it shows all
10:48:23the different methods that you can use
10:48:26to actually manipulate and make
10:48:28different things happen inside your
10:48:30markdown file so let's actually look at
10:48:32a few here I have a heading one heading
10:48:34two and heading three denoted by how
10:48:36many hashtags and a space and then if I
10:48:38preview this heading one heading two and
10:48:40heading three next we can either bold or
10:48:43italicize text by surrounding it either
10:48:46double asteris or single asteris and the
10:48:50final results right here is bold and
10:48:52italicized notice how the Bold text and
10:48:54italicize are on the same line it's
10:48:57important that after you go to a new
10:48:59line you actually put two spaces in
10:49:02there now that I have that in there it
10:49:05will actually shift it to the next line
10:49:07we can also do things like an ordered
10:49:09list or an unordered list which would be
10:49:11like bullet points and it conveniently
10:49:13indents that and makes it look a lot
10:49:15nicer we can o surround something by a
10:49:17back tick which is located up at the top
10:49:20of your keyboard or you could do triple
10:49:22back ticks at the top and bottom for if
10:49:25you have multiple lines of code and if
10:49:27we actually go to preview this we can
10:49:29see that the single line of code was
10:49:31just surrounded whereas a multiline
10:49:33creates this entire coding block the
10:49:35final two worth mentioning are links and
10:49:38also images for the link for the text
10:49:41that you wanted to appear for the link
10:49:42you'll put in square brackets and then
10:49:44for the hyperlink itself you're going to
10:49:46put that inside a parentheses right next
10:49:48to it and then actually changing this to
10:49:50a real world example of something like
10:49:52google.com if I go to preview and then I
10:49:55click this link it's going to ask me if
10:49:57I want to leave site and go to Google
10:49:59I'm not going to do it because it's
10:50:00going to mess up all my changes but you
10:50:02get the point for images is very similar
10:50:05but the text you provide in the square
10:50:06brackets is just your alternate text so
10:50:08whenever you scroll over it what the
10:50:09text is displays and then from there is
10:50:11the actual image location however this
10:50:14isn't an actual image location so I have
10:50:17this eror message that goes on with this
10:50:19alt text hence this broken file you're
10:50:22going to notice that if any of your
10:50:23files for your images are broken anyway
10:50:26github.com actually makes it pretty easy
10:50:28to get images in in this case I have a
10:50:30gif of the dashboard you could also use
10:50:32an image file but all I have to do is
10:50:34take it and drag it into here and if you
10:50:36notice it automatically formatted it
10:50:38with alt text and then the actual link
10:50:40location itself so saving the file
10:50:42itself and it puts that exclamation
10:50:44point at the front signifying that it's
10:50:46an image or in this case GIF if I go to
10:50:48preview scrolling down we can see that
10:50:51we have our image once again you need to
10:50:54put spaces after that other one to make
10:50:56sure that you're not having it all in
10:50:57the same line but you get the
10:51:00point anyway let's actually get into
10:51:03creating this read me that's on the
10:51:05homepage if you will of our actual
10:51:08project and the main point of this one
10:51:09is I want people to be navigated to the
10:51:12appropriate project depending on what
10:51:14they're looking for so I went ahead and
10:51:15put in some text already for how I want
10:51:17to break this down I'll break uh I'll
10:51:19shift over to preview and I'm going have
10:51:22a title such as my excel. analytics
10:51:24projects from there we're going to have
10:51:26the salary dashboard project and the
10:51:27salary analysis right now the image that
10:51:30I have for the dashboard is in the wrong
10:51:33location actually shift that up now I
10:51:35went ahead and added the images also for
10:51:37our salary analysis while cleaning up
10:51:39where the salary dashboard is which I
10:51:41included only just two graphs here but I
10:51:43just want to give a sneak peek of what's
10:51:45going to be inside of those other readms
10:51:47that were about to build out now you may
10:51:49be wondering how the heck do I get
10:51:51screenshots of graphs in my different
10:51:54dashboard well depending on if you're
10:51:55using Mac or Windows they have software
10:51:58installed already and so these shortcuts
10:52:01should work for you in order to perform
10:52:03your appropriate screen capture I
10:52:06primarily use on a Mac command Shift 4
10:52:08to select a certain area and it allows
10:52:11me to basically just hover over
10:52:13something and snapshot it this same
10:52:15thing can be done on a window Windows
10:52:16machine you're just going to press
10:52:18Windows shift plus s so I went through
10:52:21also and just added a quick description
10:52:23to each section I'm go into preview
10:52:24because it's a little bit easier to read
10:52:25there anyway underneath this I just
10:52:27detail hey this contains all my Excel
10:52:30files to follow along in my case my free
10:52:32course of Excel for data analytics I
10:52:34would word it differently for you of
10:52:36that you're actually providing all your
10:52:38different Project work in this
10:52:39repository additionally I provide a
10:52:41short description for the first project
10:52:44and then also a short description for
10:52:45the second project make sure in this
10:52:47case you actually are putting spaces
10:52:50after those lines so you don't have
10:52:52those images overlay on top of it now
10:52:54the last thing I would do as you see
10:52:56here I link to my course but I think
10:52:58more importantly what you need to do is
10:53:00actually link to the appropriate files
10:53:03within this repository so people can
10:53:05quickly get to the salary dashboard or
10:53:07the salary analysis and so I'm going to
10:53:09add this link of connecting to that
10:53:12appropriate project by first adding this
10:53:14text of check out my work here
10:53:17and then inside parentheses I'm going to
10:53:19list the folder of project One dashboard
10:53:24you have to make sure you spell it
10:53:25exactly like the folder that is inside
10:53:28of your repository or the Link's not
10:53:30going to work I'm going to do the same
10:53:31with the project two dashboard as well
10:53:33and going to preview it I can see it's
10:53:36all there I probably want some spaces in
10:53:38between
10:53:40this and so just put an extra enter in
10:53:42there okay that's good enough I'm going
10:53:44to get into committing the changes this
10:53:46is update my readme that sounds good I'm
10:53:49going to commit them so now on our home
10:53:52folder of our repository of excel
10:53:54project. analytics scrolling down I have
10:53:56my read me here it tells me about it and
10:53:58then for the salary dashboard it says
10:53:59hey check out my work here when I click
10:54:01on it it navigates me into this folder
10:54:04for the salary dashboard which you need
10:54:05to now create a readme 4 also it's just
10:54:08good practice to make sure that you
10:54:09check to make sure that other link works
10:54:11as well and in this case it didn't it's
10:54:13a good thing we checked it I had project
10:54:152 dashboard and instead it was actually
10:54:17project 2 analysis I'm going to commit
10:54:20changes and then now when I actually try
10:54:22it out bam navigates me to the right
10:54:24location so now you have now the basics
10:54:26to go through you understand markdown
10:54:28enough to edit it I'm going to walk
10:54:30through how I built out the project one
10:54:33read me and also the project 2 read me
10:54:35so that way you have some understanding
10:54:37of what you should do going forward with
10:54:39the project one I recommend including a
10:54:41picture of the dashboard to start and
10:54:43then a brief intro detailing why you
10:54:45wanted to do this project underneath
10:54:47this make sure you include a link to the
10:54:49file itself which is conveniently right
10:54:51here and then inside of here detailing
10:54:54the different skills that you use with
10:54:56building this is really important for
10:54:58job Seekers that way if a recruiter
10:55:01comes and looks at this they see what
10:55:02the skills are you used in this and then
10:55:04from there I talk about the data set
10:55:06itself talking about what we were trying
10:55:08to get or extract out of the data so
10:55:11basically all the foundation they need
10:55:12in the introduction portion this portion
10:55:15I recommend keep being the similar
10:55:17format the next portion you can feel
10:55:19free to go about however you want
10:55:21specifically I go into the dashboard
10:55:23build breaking it down into three main
10:55:25areas of focus on first is the charts
10:55:29itself I highlight the different median
10:55:31salaries all of the different job titles
10:55:33themselves I go into some insights from
10:55:35that I also talk about the country map
10:55:38and the insights from this as well next
10:55:40after charts I move into functions and
10:55:42formulas detailing one of the key
10:55:44functions that we used using median and
10:55:46then an if statement in order to build
10:55:48out an array formula so not only
10:55:50breaking it down but also explaining
10:55:52what insights we're able to get with
10:55:54this formula and then the third skill I
10:55:56talk about is data validation talking
10:55:59about why it's used a gif of How It's
10:56:02actually applicable or how it's actually
10:56:04visually seen in Excel and then finally
10:56:06I just wrap it up with a conclusion so
10:56:09to recap for the first project you need
10:56:11an intro statement describing what we're
10:56:13doing and why you did it and what skills
10:56:15you used then then from there on the
10:56:16build itself explaining what you
10:56:19actually built how you use those skills
10:56:21and what insights you got out of it and
10:56:23then finally wrap it up with a
10:56:24conclusion for the second project mine
10:56:27is very similar formatted in that I have
10:56:29an introduction Excel skills used the
10:56:32data set and then since this one was
10:56:34primarily focused on analysis I included
10:56:37the four questions that we went through
10:56:39and actually answered for our analysis
10:56:42so then with the template of these four
10:56:44questions I broke each one of those down
10:56:47with those questions primarily focusing
10:56:49on one what skill did I use to help
10:56:52answer that question and then two what
10:56:56is the analysis insights I got out of
10:56:59answering that question I repeat the
10:57:01same thing for the second question
10:57:02specifying the skills that we use for
10:57:04this and then the analysis or what
10:57:06insights we got out of it after going
10:57:08through questions three and four we then
10:57:10get to our final thing of a conclusion
10:57:12of what you actually learn and extracted
10:57:14from insights for this so it's really
10:57:16good to put all this stuff in it I
10:57:18wouldn't be overwhelmed and think you
10:57:20need to include everything in it think
10:57:22about a job recruiter themselves they
10:57:23don't have a lot of time so keeping it
10:57:26as short and to the point as possible is
10:57:28going to be best for
10:57:31you once you're done actually gone
10:57:34through and built out your repo with all
10:57:36its Associated read me it's time to get
10:57:37into actually sharing this on social
10:57:40media via LinkedIn I recommend the same
10:57:42approach that we used back in Project
10:57:44one of listing this down in your project
10:57:47section by going through and actually
10:57:49clicking the add icon and adding the
10:57:51projects if you did go through and
10:57:53actually add that salary dashboard
10:57:54already I would just focus this one on
10:57:57the salary analysis so I'd put in
10:57:59something like a name of the data
10:58:01science job analysis a description add
10:58:03any appropriate skills there's a ton of
10:58:05different skills you actually select for
10:58:07what you use I would focus on primarily
10:58:09these of Microsoft Excel power query
10:58:12data modeling ETL and pivot tables for
10:58:15the media in this case I would include a
10:58:18link to your repo and paste it on into
10:58:21here and click add it will then provide
10:58:24this snapshot thumbnail of what's going
10:58:25on here and a title I like it all I'll
10:58:28click apply now if you recall back from
10:58:30that first project we tried to provide
10:58:32the link of that one drive link for
10:58:34Excel and it didn't work so if you have
10:58:36that project on LinkedIn I would go
10:58:38through and also attach this link as
10:58:40well to that so that way they know how
10:58:42to navigate to it finally select your
10:58:44start and stop date if you have any
10:58:46contributors are associated with I don't
10:58:47have in this case and then from there
10:58:48save it the last thing I recommend doing
10:58:51is making a post telling others about
10:58:53your project so they can come in and see
10:58:54it in it I would definitely include
10:58:56something like a link and feel free to
10:58:58tag Kelly or myself in it I love
10:59:00checking out your projects and seeing
10:59:01the different work that you've done for
10:59:03it so once again congratulations for
10:59:06finishing this course been nothing short
10:59:08of your hard work Excel was the first
10:59:10skill or main skill that I learned in
10:59:12helping me land my first data analytics
10:59:15opportunity so I feel the same can go
10:59:17for you as well now after you taking a
10:59:19short break and you're ready to get back
10:59:20into learning more skills I do have a
10:59:23squel course that I recommend you taking
10:59:26as you've learned from analyzing this
10:59:27data Excel and SQL are two of the most
10:59:30top skills of data analyst so it pays to
10:59:33know it and you can basically learn it
10:59:35in a weekend all right with that I'll
10:59:37see you then either in the next video or
10:59:39in the next course see you there

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