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Data Science for Beginners | Google Advanced Data Analytics Certificate — Transcript

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0:00if you've ever used an app to find the
0:02best way through traffic or received
0:04product recommendations while shopping
0:06online you're already familiar with data
0:08analytics from the consumer end
0:11according to some estimates each person
0:13creates at least 1.7 megabytes of data
0:17per second on average that roughly
0:20translates into over 2.5 quintilian
0:23megabytes of data being produced every
0:26single day
0:27worldwide as such there is a huge huge
0:30demand now and for the foreseeable
0:32future for people who can organize data
0:35and interpret the stories locked within
0:38as you approach this career pathway
0:40you're likely to bring practical
0:42experience in problem solving
0:44decision-making allocating resources
0:46time management and many other skills
0:48that are particularly suited for the job
0:50of a data
0:52professional many companies are
0:53searching for candidates to fill
0:55positions in this fast growing
0:57high-paying field my name is Cassie and
1:00I've been a data scientist since before
1:01we called it data science I lead
1:04decision intelligence here at Google and
1:06I'll be your instructor for the first
1:07course of this certificate program
1:10before I became Google Cloud's Chief
1:11decision scientist I worked as a data
1:14scientist in Google research where I was
1:16involved with over 400 projects all
1:19across Google one of my favorite things
1:21about the data science career is the
1:23tremendous variety especially if like me
1:26you're a naturally curious person there
1:28are so many different Flav flavors of
1:30project and challenge some of us choose
1:32to work on one project for years others
1:35get involved with several new projects
1:37every week the possibilities are endless
1:41a data professional is a term used to
1:43describe any individual who works with
1:45data and or has data skills at a minimum
1:49a data professional is capable of
1:51exploring cleaning selecting analyzing
1:54and visualizing data they may also be
1:57comfortable with writing code and have
1:59some familiarity with the techniques
2:01used by statisticians and machine
2:02learning Engineers including building
2:05models developing algorithmic thinking
2:08and or building machine learning models
2:11machine learning is an alternative
2:12approach to automation expressing the
2:15way you want a task done by using data
2:18instead of explicit instructions machine
2:20learning is an important component of
2:22the modern data professionals toolkit to
2:25train a machine learning model
2:26Specialists put a bunch of potential
2:28data inputs through algorithms tweak the
2:30settings and keep iterating until
2:33promising outputs are produced but
2:35training a model is only one small step
2:37in the professional machine Learning
2:38Journey machine learning techniques can
2:41also be used for data analytics and
2:43Exploration with far fewer steps and
2:46that's what you'll learn in this program
2:49you'll discover additional opportunities
2:51to explore machine learning through the
2:52course resources so do check those out
2:54data professional work spans a wide
2:57range of Industries and impact effects a
3:00multitude of products and
3:01services as we'll discuss later there
3:04are also lots of different roles and
3:06titles that focus on data professional
3:08work think of them as data detectives
3:11analyzing and interpreting their
3:13findings to reveal the stories within
3:16I'm excited for you to get to meet some
3:18of them in this
3:20program Google career certificates are
3:22designed by industry professionals with
3:25Decades of experience here at Google
3:27you'll have a different expert from
3:28Google to guide you through each course
3:30in this program we'll share our
3:33knowledge in videos help you practice
3:35with Hands-On activities and guide you
3:37through scenarios that you might
3:38encounter on the job if you work on the
3:40certificate part-time so this program is
3:43really flexible you can complete all of
3:45the courses on your own terms and at
3:47your own pace throughout the program
3:50we'll give you resources that will
3:51prepare you to advance in your career as
3:53a data professional as you progress
3:56you'll also build a repository of
3:58portfolio projects and a comprehensive
4:00Capstone project that will showcase your
4:02abilities beyond your resume you'll also
4:05have a supportive network of peer
4:06Learners taking the certificate with you
4:09and you can connect with them in the
4:10discussion forums this program is
4:13designed to give you experience by
4:15building upon the Knowledge and Skills
4:16that you've developed to this point
4:18regardless of your experience with data
4:20and analytics as you begin the program
4:22you'll learn about different experiences
4:24that are relevant and helpful for
4:26starting or advancing your career in
4:28addition to building your skill set will
4:30examine how teams of data professionals
4:32collaborate and contribute in the
4:34workplace by the end of this program
4:37you'll be ready to pursue a position in
4:39the data career Space by completing a
4:42Google career certificate like this one
4:45you'll develop the skills and knowledge
4:47necessary for a job in this expanding
4:49career field once you graduate you can
4:52connect with hundreds of employers who
4:53are interested in hiring Google career
4:56certificate graduates whether you're
4:58seeking to switch careers level up your
5:00skills or start your own business the
5:03Google career certificates can help you
5:05take that next step throughout this
5:08first course I'll be here to help you
5:09gain foundational knowledge needed to
5:12succeed in the field again I'm so glad
5:15you're here I'm excited for you to take
5:17these next steps forward in your career
5:19I'll see you in the next
5:22video now that you have a general
5:25understanding about this program overall
5:27let's talk a bit more specifically about
5:29what you can expect in this course we'll
5:32start with the basics and a little
5:33background on what the data field offers
5:36while you may already be familiar with
5:38work within the data field I'm excited
5:40to dig deep into some history that shows
5:43us where we've been where we are and
5:46where we're going in this field these
5:48key developments and applications will
5:50really showcase all the opportunities
5:52this program will help prepare you for
5:54in this first course we'll discuss the
5:57specific skills and characteristics that
5:59organization ations look for in future
6:01employees you'll develop the core skills
6:03necessary to advance on your journey as
6:06a data professional and integrate these
6:08skills with your own pre-existing
6:10abilities we'll focus specifically on
6:13Technical and workplace expectations
6:16there will also be plenty of chances to
6:18practice along the way then we'll
6:20explore your job market opportunities
6:22you'll learn about the variety of roles
6:24and positions that match your skill set
6:26you'll also investigate the
6:28responsibilities and ethics that
6:29underpin all roles within the data
6:31career space with the increasing number
6:34of industries that are turning to the
6:36data professions you're bound to find a
6:38role that fits your interests and we'll
6:40also explore some of these industries so
6:42you can see where data professionals are
6:45employed to make more informed decisions
6:48then we'll examine how larger
6:49organizations create teams of data
6:52professionals to approach larger scale
6:54projects we'll also peek into the future
6:56of data careers and the trajectory of
6:58the field engineer General so that you
7:00have a good sense of what the future
7:02holds for you after completing this
7:05program we'll also investigate Elements
7:08of Effective communication and discover
7:10how it can Empower you as a data
7:13professional throughout this course and
7:15the whole program you will see how
7:17effective communication can Elevate
7:19productivity and promote General
7:21understanding during the data analytics
7:23process as you progress you will gain
7:26Hands-On data analysis experience
7:28through your portfolio your projects
7:30beginning with the first one in this
7:32course we've designed a few different
7:34options for you to apply your data
7:36skills to actual scenarios with data
7:39shared by our industry partners and you
7:42can use these projects to Showcase your
7:44skills to Future
7:46employers lastly our instructors will
7:48provide a few career tips and tactics to
7:51guide you on your journey that's a short
7:53preview of what to expect later in this
7:55course next you'll have the opportunity
7:58to go over some resources that will help
8:00you get the most out of this program
8:03I'll see you in the next
8:06video hello again let's discuss some of
8:08the course items you'll encounter in
8:11your Learning Journey in this program
8:13you'll code in Python discover the
8:15stories that data holds develop data
8:18visuals use statistical tools build
8:20models and even dabble with some machine
8:23learning along the way you'll build a
8:25portfolio full of data projects in
8:27addition to this program's Capstone
8:30whether you're looking to switch careers
8:32start a new career improve your skills
8:35or Advance beyond your current role in a
8:37company the Google career certificates
8:39can help guide you as you take steps
8:42towards New
8:43Opportunities we've gathered some
8:45amazing instructors to support you on
8:47your journey and they'd like to
8:49introduce themselves now hello I'm
8:52Carter and I'm an developer Advocate at
8:54Google together we will explore one of
8:56the fastest growing programming
8:57languages python you'll learn the basics
9:00which will help you write scripts that
9:02perform a number of key mathematical
9:04operations on data sets all designed to
9:06help you unlock the stories within data
9:09hi there I'm Rob I'm a consumer product
9:12leader I work on marketing projects here
9:14at Google I'm excited to talk to you
9:17about how to tell stories using data
9:20we'll discuss the six practices of
9:22exploratory data analysis and how to
9:24identify the trends and patterns in it
9:27we will also learn about the importance
9:30of Designing and presenting data
9:32visualizations using Python and Tableau
9:35which can help you understand your data
9:37and convey it to others hello my name is
9:41Evan I'm an economist and I consult with
9:43various teams across Google statistics
9:46helps you generate more complex ideas
9:48from the data itself in our time
9:50together you'll discover how you can
9:52generate insights draw conclusions make
9:55inferences create estimates and make
9:57predictions
9:59hello I'm Tiffany and I'm a marketing
10:02science lead and I work with marketing
10:03data here at Google I will guide you
10:06through the process of modeling
10:07relationships between variables together
10:11we'll explore different regression
10:12models and hypothesis tests we'll also
10:15talk about model assumptions
10:17construction evaluation and
10:19interpretation as the means for
10:21answering datadriven questions hello I'm
10:25sushila I'm a data scientist and I work
10:27on projects for YouTube here at Google
10:29I'll guide you through Building Systems
10:31that can learn and adapt without a
10:33specific set of instructions we'll
10:35discuss how machine learning is
10:37transforming the process of data
10:38analysis as you construct your own
10:41models hello I'm Tiffany and I lead
10:44teams focused on building AI responsibly
10:46here at Google I'll introduce you to
10:48career resources and portfolio projects
10:50and guide you through the Capstone
10:52course at the end of the program I'll
10:54assist you with different opportunities
10:55and tools that will set you up for
10:57Success on the job market
10:59and of course you know I'll be guiding
11:02you through course one this is such a
11:04great time to grow and Advance your
11:06career as a data professional your path
11:08to a career full of New Opportunities
11:13awaits earlier I reference the
11:16tremendous amount of data generated each
11:18day it has become a byproduct of Modern
11:20Life for companies and organizations all
11:23this data can provide insight into the
11:25ways they operate and ultimately
11:27interact with their users and consumers
11:30to obtain these insights organizations
11:32need people with the ability to access
11:34interpret and share the stories within
11:36their data organizations understand that
11:39data can inform decision-making and
11:41explain consumer Trends and user
11:43Behavior data professionals use Data
11:45Insights to optimize products or
11:48Services there's a common phrase in
11:50datadriven decision-making that
11:51references the untapped potential of
11:53data the phrase is imagine if we knew
11:57what we know basically it's a way of
12:00asking the following question how can we
12:02take all that data that may already
12:04exist and translate it into meaningful
12:06and actionable insights to gain insights
12:09businesses rely on data professionals to
12:11acquire organize and interpret data
12:14which helps inform internal projects and
12:16processes businesses seek those who can
12:18access data and understand its metrics
12:21as a reminder metrics are methods and
12:23criteria used to evaluate data both are
12:26necessary before creating predictive
12:28models that can identify Trends and
12:30inform best practices and that's where
12:32you'll come in the combination of all
12:34these skills from statistics and
12:36scientific methods to data analysis and
12:38artificial intelligence all fall within
12:40the category of data science data
12:43science is the discipline of making data
12:45useful to me the idea of usefulness is
12:48tightly coupled with influencing real
12:50world actions some individuals with
12:52these skills may work on developing
12:54business insights and supporting
12:56strategic decision makers others may use
12:58data skills to fuel automation testing
13:01and analytic tool development still
13:03Others May focus on the analytic process
13:05Itself by adapting modeling approaches
13:08to incorporate new and emerging
13:10Technologies data is the foundation for
13:12making future decisions it's through our
13:15actions and decisions that we affect the
13:18world around us businesses and
13:20organizations need people like you who
13:22can think critically and analytically
13:25about how to directly address challenges
13:27and opportunities through data Focus
13:29focused projects the work of data
13:31professionals can provide businesses and
13:33organizations with details about their
13:36practices that can promote new
13:38approaches and Innovation this might
13:40make a little more sense if we take a
13:42closer look at an example a global
13:44delivery company for instance generally
13:47speaking delivery companies are
13:49responsible for transporting goods to
13:50Consumers a company as complexes this is
13:54going to have a number of different data
13:56inputs of streams that influence impact
14:00and affect the ways the business
14:01operates these data inputs and streams
14:04may include but are not limited to
14:07weather and traffic patterns which
14:09affect when deliveries are projected to
14:10arrive gas price trends and fuel economy
14:14which affect shipping costs and profit
14:16margins truck loading times in relation
14:18to the number of workers available which
14:20affects the time it takes for the
14:22delivery to reach its final destination
14:24how users interact with the company's
14:26app to track their packages which
14:28affects the customer experience and the
14:30company's ratings and whether users
14:33engage with marketing emails sent after
14:35they make specific purchases which
14:37impacts future and repeat sales my point
14:40is each of these variables affects the
14:43way organizations harness data to
14:45transform decisions automating and
14:47adapting machine learning were
14:49applicable the ability to unlock
14:51transformative information within data
14:54is a skill that businesses seek as you
14:56progress within this program you'll
14:58discover how data professionals can make
15:00meaningful contributions to almost any
15:03organization by finding action-oriented
15:06Solutions within
15:09data all professions require a certain
15:12set of tools for success and datadriven
15:14work is no different in this video we'll
15:17open our analytics toolbox and look at
15:19some of the most common items before we
15:22begin I want to emphasize that each of
15:24the items serves their own individual
15:26purpose however when used together they
15:29help build and tell stories with data
15:32which can then inform influence and
15:34impact business decisions programming
15:36languages are the first tools we'll
15:38investigate they allow data
15:40professionals to work efficiently within
15:43and dissect large data
15:45sets most languages have been developed
15:48over time and each data professional has
15:50their own preferences we'll mention two
15:52in this video that have become very
15:54popular for data analysis the r
15:56programming language and python r is a
15:59programming language that's used
16:00extensively by researchers and academics
16:03it was my primary language during
16:05Graduate Studies in statistics and some
16:07people say that R captures the
16:09statistician mindset I'd say there's
16:11something to that sentiment if you're
16:13after implementations of the latest
16:16statistical breakthroughs R is a great
16:18place to look but it's used for more
16:20than statistics you'll find many new
16:22technologies and ideas programmed with
16:24it one of the best features of R is that
16:27you can create complex statistics iCal
16:29models from just a few lines of code if
16:31you're curious about r or need a
16:33refresher be sure to check out our
16:35Google data analytics certificate also
16:37offered here on this platform this
16:40program teaches the Python programming
16:43language it's a great choice for a few
16:45reasons first of all it emphasizes
16:47readability making it one of the easiest
16:50programming languages to learn and write
16:52second unlike R python wasn't born in
16:55the data Community while this might
16:57sound like a minus
16:59it can also be a huge Plus in the modern
17:02world data is used in increasingly
17:04creative ways so is a massive advantage
17:06to learning a programming language
17:08that's capable not only of handling the
17:10data side of things but can also be used
17:12to build and deploy the applications
17:14that data will be fueling although R was
17:17my first love these days I find that I
17:19lean more heavily on python because of
17:21its flexibility python can perform a
17:24wide variety of data related tasks which
17:27makes it very popular among data
17:29professionals if you're a novice or new
17:31to coding completely python is a very
17:34approachable language its formatting is
17:36visually uncluttered it's one of the
17:38most beginner-friendly languages and it
17:40has enormous online communities and
17:42plenty of resources to help you if you
17:44get stuck we will interact with python
17:46within a web-based Computing platform
17:49also called jupyter notebooks which
17:50allows you to run code in real time and
17:53helps identify errors easily to
17:55visualize the stories in the data we're
17:58going to teach you how to share complex
18:01data through a graphical interface those
18:03who experienced our data analytics
18:05program will be familiar with a platform
18:07called Tableau in this program we'll
18:09take a more detailed look at how this
18:11powerful tool can help others understand
18:13the results of your
18:15analysis additionally we'll look at
18:17effective communication in datadriven
18:20careers at first glance it might seem
18:23like less of a concern but describing
18:25the sometimes complex processes of data
18:27analytics to non-technical stakeholders
18:30may be one of the most important skills
18:32a data professional can have since
18:35communication is something we all do
18:36regularly it's easy to forget about the
18:38importance of how data professionals
18:40share and process data stories our goal
18:43here is to strengthen the communicative
18:45skills that you already possess so that
18:48you can leave this program equipped to
18:50excel in this course specifically and
18:53across other segments of this program
18:55communication will be a key component
18:57that is directly tied to the work you'll
18:58do as a data professional programming
19:01languages allow data professionals to
19:03interact with and interpret data visual
19:06data tools like Tableau enrich the
19:08stories within data with visual elements
19:11that bring attention to specific details
19:14but the most important element of any
19:17story is the Storyteller that's you your
19:21prior experiences and knowledge inform
19:23your storytelling abilities and your
19:25distinct background is what will set you
19:27apart from from others in these roles
19:30regardless of your eventual career path
19:33remaining determined and developing the
19:35proper skills is essential to personal
19:38and professional
19:39transformation and the tools we're
19:41offering you in this program will also
19:43help you along the way I'm thrilled to
19:46continue alongside you in your journey
19:48The Best Is Yet To Come I'll see you
19:52soon congratulations on completing the
19:55first part of this program you've
19:57officially begun your journey to new
20:00opportunities in the data field let's
20:02revisit what we've covered so far first
20:06we covered the basic Logistics of what's
20:08included in the certificate you met each
20:10of your instructors and we previewed
20:12some of the different course topics
20:14you'll encounter next we looked at the
20:16basics of data focused work and some of
20:19the industries that are incorporating
20:20Data Insights we also discuss the future
20:23of datadriven careers in addition to
20:26exploring what it's like to work in the
20:28data field we also discussed the data
20:31professionals toolbox and the skills
20:33this program will help you develop
20:35congratulations on your progress so far
20:38I can't wait to see you in the next
20:42video as you know this program asks you
20:45to complete a graded assessment at the
20:47end of each section and course and Now's
20:50the Time to prepare for your first one
20:53this assessment will effectively verify
20:55your understanding of key data analytics
20:57Concepts it will also help build
20:59confidence in your understanding of data
21:02analysis while identifying any areas
21:04where you can continue to improve
21:07assessments can sometimes feel
21:08overwhelming but approaching them with a
21:10strategy makes them more manageable
21:12here's a list of tips to set yourself up
21:15for Success before taking an assessment
21:18review your notes and the videos
21:20readings and the most recent glossery to
21:22refresh yourself on the content during
21:25the assessment take your time view the
21:28whole test before filling in any answers
21:32then answer the easy questions skip the
21:34ones you don't know the answer to right
21:35away for multiple choice questions focus
21:38on eliminating the wrong answers first
21:41also it's a good idea to read each
21:43question twice there often clues that
21:45you might miss the first time if you
21:47start to feel anxious calm yourself with
21:50some mental exercises one way to do that
21:53is by completing a simple math problem
21:54in your head or spelling your name
21:57backwards this also helps you recall
21:59information more easily before you
22:02submit the assessment check your work
22:04but be confident sometimes people change
22:07an answer because it feels wrong but
22:09it's actually correct your first
22:11instinct is often the best one finally
22:15remember to trust yourself often people
22:18know a lot more than they give
22:19themselves credit for everyone learns at
22:22different speeds and in different ways
22:24but it's important to maintain your
22:26momentum so take the time you need and
22:29when you feel ready keep moving ahead
22:31you've got
22:33this great to have you back I'm so
22:35delighted to be your guide as you
22:37continue on your way to becoming a data
22:39professional as you've been discovering
22:42data can be used in many ways but no
22:44matter how it's applied the key thing to
22:46keep in mind is that knowledge is power
22:48the power to improve your business your
22:50work your personal life and the world
22:53around you with all of the data
22:55surrounding us there's just so much
22:57potential waiting to be unlocked I'm so
23:00glad that you've chosen to learn more
23:02and to be a part of it and speaking of
23:04your Learning Journey in this section of
23:07the course we'll start by finding out
23:09more about data careers we'll explore
23:12different Industries and examine some
23:14direct applications of datadriven work
23:17and we'll investigate where data careers
23:19are headed in the future it's such an
23:22impactful and rewarding field and it
23:25just keeps getting better I'm so excited
23:27for you to get started started let's
23:30go data professionals are so valuable to
23:34their companies they determine which
23:36data streams are most important to
23:38specific business projects challenges
23:39and initiatives they identify key goals
23:42for the future and they give their
23:44organizations the ability to take
23:45meaningful action by reimagining
23:48processes and improving operations to do
23:51all of this data teams require
23:53individuals with diverse skills
23:55knowledge and interests therefore they
23:58countless different data focused roles
24:00responsibilities and project types which
24:03are further differentiated by the
24:04industries and businesses they support
24:07among all of these possibilities data
24:09careers can be Loosely categorized into
24:11two complimentary types of work
24:13Technical and strategic in this video
24:16we'll investigate both first let's find
24:19out about the folks whose work requires
24:21a heavy emphasis on technical skills
24:24some examples of these professionals are
24:26machine learning engineers and
24:27statisticians
24:29these people provide High effort
24:31solutions to specific problems through
24:34their expertise in mathematics
24:36statistics and Computing they build
24:38models and make predictions using tools
24:41such as r& python they help their teams
24:43extract value from business data sets
24:46the result is a solution that has a
24:48direct positive impact another highly
24:51technical role is the expert data
24:53analyst whose work involves exploring
24:56vast and complex data sets to ident ify
24:58directions worth pursuing in the first
25:01place they ensure that an organization's
25:04data science efforts are directed as
25:06efficiently as possible Bridging the Gap
25:08between other technical data
25:10professionals and the Strategic work
25:12we'll cover shortly like most technical
25:14data professionals you'll learn how to
25:16acquire scale organize structure and
25:19manipulate data so that it's packaged in
25:21a way that others can work with in other
25:23words you'll know how to transform raw
25:26data into something you useful for
25:29decision making okay now let's consider
25:32data professionals on the more strategic
25:34side these people include business
25:37intelligence professionals and Technical
25:39project managers to name a couple
25:41strategic data professionals use their
25:43skills to interpret information that
25:45affects an organization's operations
25:47Finance research and development and so
25:50much more their work aligns closely to
25:52the overall business strategy and
25:54involve seeking solutions to problems
25:56through data analytics in short
25:58strategic data professionals maximize
26:01information to guide how a business
26:03works sometimes you'll find a company
26:06has roles that blend specialist
26:07technical knowledge with strategic data
26:10expertise often in unusual and very
26:12creative ways soon we'll learn more
26:15about some of these opportunities as
26:17well as the more specialized Technical
26:19and strategic roles and of course we'll
26:22discover some proven ways to tap into
26:24them as a data professional lots more to
26:28come
26:31nowadays it seems that data is generated
26:34everywhere computer functions have been
26:36integrated into a multitude of everyday
26:39Technologies smart home voice assistants
26:41step trackers TV streaming apps even
26:44some coffee makers are connected all of
26:46these touch points create data that
26:48businesses can use to understand Trends
26:50and Advance their products and services
26:53and with every update the ability to
26:56gather environmental and user data
26:58expands even further within this massive
27:01reserve of data is a wealth of untapped
27:03potential awaiting data professionals
27:06and the organizations they
27:08support that's why these people are so
27:11in demand companies need them to use
27:13data to refine business strategies meet
27:16consumer preferences react to emerging
27:18Trends and realign internal efforts
27:21let's consider some examples of how data
27:24professionals use their expertise to
27:26transform Industries first the world of
27:29big Finance was an early adopter of the
27:31power of data science and with the way
27:34information drives this industry it's
27:36easy to understand why data
27:38professionals help financial
27:40institutions assess investment risks
27:42monitor market trends detect anomalies
27:44to reduce fraud and create a more stable
27:47Financial system overall hundreds of
27:50millions of financial transactions occur
27:52in the financial World each day and data
27:55is part of each and every one of them
27:58as another example data analytics is key
28:01in healthcare and here the data benefits
28:04can actually be life-saving for instance
28:07the information collected by smart
28:09watches is making a huge difference in
28:11the lives of many people sensors in
28:13these devices record biodata such as
28:16heart rate and oxygen levels and of
28:18course all this information can be
28:20shared with Healthcare professionals
28:22together the patient and the physician
28:24can better understand sleep Trends
28:26stress levels and much more more then
28:29individualized Wellness plans can be
28:31created and modified for the patient
28:33well-being Plus on a larger scale data
28:36analytics is helping Healthcare
28:38organizations process large amounts of
28:40clinical data which supports the early
28:43detection of a health condition and
28:44leads to more precise
28:46diagnosis thirdly data has a big impact
28:50in manufacturing data professionals
28:52predict when to perform preventative
28:54maintenance to avoid production line
28:56breakdowns use data to maximize quality
28:59assurance and defect tracking and
29:01artificial intelligence models help
29:03respond to logistical issues and reduce
29:05delivery truck miles on the road
29:07advancing key sustainability goals and
29:10in a time when Supply chains reach every
29:12corner of the world data enables clear
29:14and near real-time communication with
29:16suppliers retailers and other network
29:19Partners it also helps Supply chains
29:21maintain optimal levels of inventory to
29:24avoid stockouts and empty retail shelves
29:27data professionals are also advancing
29:29approaches to agriculture with Data
29:32Insights Farmers develop new ways to
29:34approach Crop Production livestock care
29:37forestry and
29:38aquaculture the inclusion of autonomous
29:41Machinery tractors and irrigation
29:43systems is improving harvesting
29:45Technologies as well if you'd like to
29:48keep learning about how various
29:49industries use data analytics refer to
29:52the course resources on this
29:54topic and here's a little piece of
29:56advice for me don't miss an opportunity
29:58to learn from someone in real life I
30:01love asking business owners store
30:03managers and client support
30:04professionals about how they use data
30:07each and every day who knows one of
30:09these conversations could open a door to
30:11a future opportunity for
30:15you recently you've been learning about
30:17how businesses use data to guide
30:19decision-making answer questions and
30:21solve problems in this video we'll
30:24investigate how nonprofits use data
30:26analysis to pursue their unique
30:29goals nonprofit groups are created to
30:32further a social cause or provide
30:34benefit to the public as the name
30:36suggests their main purpose is not about
30:39profit but to foster a collective public
30:42or social Advantage there are some very
30:44rewarding and inspiring opportunities
30:46for data Professionals in the nonprofit
30:48sector in particular data can be applied
30:51in order to help these groups more
30:53effectively anticipate and respond to
30:56the greatest areas of need
30:58for instance maybe a US charity that
31:01provides bicycles for children would
31:03like to determine which neighborhoods
31:05are most in need they could ask their
31:07data professional to access the US
31:09Census Bureau the professional would use
31:12their talents to navigate the census
31:14database identify key metrics and
31:16summarize findings with analyses and
31:18data
31:19visualizations this report would
31:21highlight where there are larger numbers
31:23of school-aged children in need who
31:25would benefit from the resources of this
31:27program
31:28and there you go Data Insights led to
31:30informed decisions about where this
31:32nonprofit can do the most good now
31:35nonprofits do more than use data many of
31:38them also collect it as you likely know
31:41public entities and government agencies
31:43can be excellent resources for useful
31:46data and much of it is open data that's
31:48available for General use as you likely
31:51know open data is data that is available
31:54to the public it's free to use and
31:57guidance is provided to help navigate
31:59the data sets and acknowledge the source
32:02while sourcing open data is a good way
32:04to interact with data on your own there
32:06are other opportunities that enable you
32:08to refine your skills while helping
32:10others data volunteers contribute to
32:12many projects that help nonprofits
32:14benefit communities all over the world
32:17to find out more here are some
32:19organizations to check out first the
32:23data science for social good foundation
32:25was launched at the University of
32:27Chicago back in 2013 in 2020 they joined
32:31forces with UNICEF to analyze various
32:34aspects of air pollution around the
32:36world to help monitor Children's Health
32:39datakind launched in 2011 in New York
32:41City with chapters in the United Kingdom
32:44bangaluru San Francisco Singapore and
32:46Washington DC this organization analyzes
32:49the cost of environmental cleanup in
32:51different underserved communities to
32:54guide restorative efforts you can view
32:56both foundations latest efforts through
32:59the links in the transcript for this
33:00video another option for putting your
33:02data skills to good use are hackathons a
33:05hackathon is an event where data
33:07professionals and programmers come
33:09together and collaborate on a particular
33:11project the goal is to create a solution
33:13to an existing problem using technology
33:16some examples include developing better
33:18tools for predicting extreme weather
33:20events creating Tech to help elementary
33:22school kids learn important reading
33:24skills and identifying ways that
33:26Community Development groups can use use
33:28their data to advance home accessibility
33:30and
33:31affordability volunteering your data
33:33skills to public projects is an
33:35excellent way to contribute to the
33:37greater good while gaining experience
33:39and networking with others in your field
33:42coming up we'll take a deeper look at
33:45some data oriented projects in the
33:47public sector and how they're making an
33:49impact around the
33:52world one of the most important
33:54responsibilities for those of us in data
33:56centered career
33:58involves how we protect our
34:00organizations manage and protect data
34:04this has a lot to do with communication
34:06exchanges between a company and its
34:08customers as you've been learning almost
34:10all communication generates data whether
34:13it's a shopping receipt confirmation of
34:15an order or even earning customer
34:17loyalty points and businesses have a big
34:21responsibility to their customers
34:23especially when it comes to maintaining
34:25and protecting user privacy
34:28any data gathered from individuals or
34:30consumers is referred to as personally
34:32identifiable information or pii pii
34:36permits the identity of an individual to
34:38be inferred by either direct or indirect
34:41means this includes things like
34:43biometric records usern names and Social
34:46Security or national identification
34:48numbers because this information is
34:50often associated with medical financial
34:52and employment records pii is sensitive
34:56and must be managed with great care
34:58after all when someone's personal data
35:00is improperly handled they become
35:02vulnerable to identity theft fraud and
35:05other issues recently there have been
35:08great efforts to take a wider view of
35:10data collection practices and protect
35:13individuals Industries are trending
35:15towards aggregate
35:17information this is data from a
35:19significant number of users that has
35:21eliminated personal information by
35:24aggregating the data and removing pii
35:26this protects people and gives them more
35:29control over their own data similarly as
35:32more Industries become interconnected
35:34the amount of data available to them
35:36increases and just as with aggregate
35:39information the more data collected the
35:41more likely it is that it will be
35:42representative of a wider population
35:45rather than a single user a key thing to
35:48keep in mind is that data Gathering is a
35:50task managed by
35:52humans and that process can be informed
35:55by different backgrounds experiences
35:57beliefs and
35:58worldviews these and other types of
36:01biases can affect the way the data is
36:03communicated and how the results are
36:05shared which in turn can have an impact
36:07on business decisions effective data
36:10professionals know that whether
36:11collecting analyzing interpreting or
36:13communicating sensitive data bias should
36:17always be considered so be very careful
36:21when interpreting data where there is a
36:23clear source of bias and be on the
36:26lookout for subtle biases as well in
36:29addition to thinking through bias in the
36:31data data professionals should also try
36:34to emphasize that there can be a
36:36multitude of possible interpretations
36:38for every data Insight so the main trick
36:42is avoid jumping to conclusions until
36:45you've really done your homework one
36:48method of addressing bias is to make
36:50sure that the data that you're working
36:53with has the same characteristics as the
36:55greater population that you're
36:56interested in
36:58in data analytics this is called a
37:00sample a good sample is a segment of a
37:04population that is representative of
37:07that entire population here's an example
37:10a clothing company is analyzing sales in
37:12their highest growth Market they want to
37:15determine what color shirts will be most
37:18popular in the upcoming season one
37:20person notes that red and blue shirts
37:22accounted for 80% of their sales in this
37:24market over the past 3 months this is is
37:27a big number so they suggest ordering
37:29lots of red and blue shirts but another
37:32person points out that the local sports
37:34teams colors are red and blue and this
37:37team had recently won a championship
37:39it's very likely that sales of red and
37:41blue shirts will have spiked as
37:43consumers purchase te's to support the
37:45local team plus they note that although
37:48this Market is high growth it only
37:50represents 40% of the retailers total
37:53sales with all this information in mind
37:56decision makers at this retailer instead
37:58choose to evaluate color popularity over
38:01a full year and across all markets this
38:04will provide a much more complete
38:06picture we'll investigate more about
38:08bias later in this program and as you
38:11progress you'll discover many more
38:13strategies for ensuring that you're
38:15aware of bias and proactively working to
38:18counter it in all of your data
38:22work when investigating a possible new
38:25career path one of the most important
38:26things to consider consider is its
38:28Outlook and potential for growth
38:30predictions about careers related to
38:32data analysis show that there is no
38:34shortage of Need for Professionals in
38:36this field over the last decade data
38:39focused careers have surged according to
38:42estimates by LinkedIn the data science
38:44field grew by over
38:46650 between 2012 and
38:492017 many experts believe that we have
38:52not yet seen the full potential of these
38:54careers in fact the US Bureau of Labor
38:57statistics stated that data science is
39:00one of the fastest growing career fields
39:02in the United States projecting a 30%
39:05increase over the next decade among the
39:08data science professions one of the
39:10fastest growing is artificial
39:12intelligence and machine learning and
39:14we've seen significant advances in these
39:16areas in recent years at its core
39:19artificial intelligence or AI is the
39:22development of computer systems able to
39:24perform tasks that normally require
39:27human intelligence thanks to growth in
39:29the data Sciences AI is now becoming
39:32more commonplace these technologies will
39:35continue to evolve and provide more
39:37accurate results and richer insights and
39:40as AI increasingly becomes an essential
39:43component of data work it's important to
39:45be aware of the human bias that can be
39:47imprinted within your work to counter
39:50this organizations benefit most from
39:52building diverse teams of professionals
39:55from different backgrounds and different
39:57life experiences incorporating a wide
39:59range of perspectives and worldviews
40:02promotes wider representation and yields
40:04more accurate results as we study the
40:07future of the data professions I want to
40:10emphasize that data professionals have
40:12yet to realize the full potential of
40:15artificial intelligence as these types
40:18of technological innovations continue to
40:20evolve we can expect that organizations
40:22will grow and adapt their business
40:24practices
40:25accordingly with wider and wider
40:27adoption of data analysis techniques the
40:30most likely area for growth is in
40:32specialization and we expect to see
40:34further subdivision of roles within data
40:37focused teams ultimately what I want you
40:40to keep in mind is this the world is
40:43generating more and more data every year
40:46so it's reasonable to expect labor that
40:48extracts business value from it to be
40:50able to earn its keep more data means
40:53more demand for the three main
40:55activities covered by the data
40:56professions
40:57statistical inference machine learning
41:00and data analytics so those skills will
41:03stay very relevant though their names
41:05might evolve over time in addition
41:08constant innovation in the field offers
41:10you the opportunity for Perpetual
41:12learning growth and
41:14development as you may already know
41:17being a data professional means that
41:18your growth and success in this field
41:20depend on a desire to keep learning in
41:23fact that just might be the reason you
41:25enrolled in this program and for that
41:28I'm so proud of you continue to explore
41:31opportunities to evolve throughout your
41:33career be proactive in acquiring new
41:35skills keep growing and you will always
41:38be ready for the
41:40future in this section of the course we
41:43explored many different facets of data
41:46careers you learned that data
41:49professional is a broad term that
41:50encompasses different roles and
41:52responsibilities in the data space you
41:56discovered that the work we do in in
41:57this field involves countless
41:59possibilities such as determining
42:01important data streams identifying and
42:03focusing on future business goals and
42:06reimagining internal and external
42:08processes you also thought about some of
42:11the ways that organizations are being
42:12transformed by data professionals and
42:15how these talented individuals use their
42:17skills to positively impact communities
42:20around the world you've come so far
42:22already but there's so much more to
42:25learn thanks for joining me on on this
42:27exciting exploration and I'll catch up
42:30with you again
42:33soon welcome back I'm really excited to
42:36share this section with you we'll start
42:39by looking at some of the skills needed
42:40in the data field today we'll look at
42:43what happens after you land a data
42:45position and what to expect in those
42:47first few weeks on the job we'll
42:49investigate some of the roles within the
42:51data professions and take a closer look
42:54at the general categories that you'll
42:55encounter then we'll explore the
42:58importance of networking and building
43:00relationships within an organization
43:02we'll also look at the responsibilities
43:05of different data professionals so let's
43:07get started see you in the next
43:12video all data professionals share a
43:15love of data and a desire to solve
43:18problems while wearing their analytics
43:21hat data professionals lay out the story
43:23that they're tempted to tell and then
43:25they poke it from several angles with
43:27follow-up investigations to see if it
43:30holds water before bringing it to their
43:32decision makers in doing so they rely on
43:35their programming and investigative
43:37skills to guide others towards informed
43:40decisions data professionals also
43:43combine a knowledge about how to do
43:44practical tasks with an awareness of
43:47what makes communication and
43:49collaboration successful later we'll dig
43:52deeper into the elements of
43:54communication and discuss the ways
43:56communication enhances and structures
43:59your work as a data professional for now
44:02let's examine some skills and attributes
44:04that are applicable across datadriven
44:07careers working in data analytics
44:09requires a mix of business sense and
44:12knowledge in gathering manipulating and
44:14analyzing data our goal is to prepare
44:17you to develop the competencies needed
44:19to succeed let's start by discussing
44:22some interpersonal skills often these
44:25are referred to as people skills skills
44:27they focus on communicating and building
44:30relationships interpersonal skills are
44:33critical in this field there's a high
44:35degree of interaction between
44:37stakeholders this is especially relevant
44:39now with team members often working
44:42collaboratively across the globe very
44:45often work conversations are the
44:47starting point and the fuel that drives
44:49projects and because of the cyclical
44:51processes within data analysis
44:54communication is always ongoing and
44:57another important skill is active
44:59listening this means allowing team
45:01members bosses and other collaborative
45:03stakeholders to share their own points
45:06of view before offering responses so
45:09that each exchange improves Mutual
45:12understanding you can actually practice
45:14active listening next time you speak
45:16with someone put extra effort into
45:18listening beyond their words focus on
45:22what they're trying to communicate your
45:24listening and communication skills will
45:26play a huge role in helping you capture
45:29effective insights and informed
45:32decisions we'll take a closer look at
45:34communication a little later in this
45:36course there are other things you'll
45:38need to consider as a data professional
45:40you'll search for information hidden
45:42within large amounts of data by applying
45:45critical thinking skills along the way
45:47you'll investigate the connections
45:49between a variety of different data
45:51sources as you search for Trends and
45:53indicators think of yourself as a data
45:55detective project data can come directly
45:58from your organization or from other
46:00sources you might be lucky and receive a
46:03well-formatted spreadsheet or database
46:05but quite often you will need to prepare
46:07the data to get started this process is
46:10known as data cleaning this is where the
46:13data is reorganized and reformatted the
46:16goal is to remove anything that could
46:18create an error during
46:20analysis this process
46:22includes tagging and consolidating
46:25duplicates irrelevant entries structural
46:28errors and empty space once you have
46:31everything in the proper format you can
46:34then filter out unwanted material now
46:37your data is ready to be
46:39analyzed it's time to look for Trends
46:42and tendencies often it's very helpful
46:45to render the data visually to reveal
46:47additional insights through charts
46:49dashboards and reports graphic tools
46:52will be very useful in identifying
46:53patterns as well as in sharing
46:55information with others
46:58you will explore this in Greater detail
46:59later and have opportunities to practice
47:02compiling visualizations too you'll also
47:04learn about more advanced skills like
47:06building models and machine learning
47:08algorithms these tools will help you and
47:11other data professionals assess
47:13information accuracy analyze specific
47:15data segments and predict future
47:18business outcomes your hard work will
47:21assist leaders and other decision makers
47:23in your company providing them access to
47:25a rich variety of perspectives
47:27on different sets of information with
47:29demand for data analytics increasing
47:31across all types of companies and
47:33businesses you will likely find
47:35opportunities in an industry that you
47:38are personally interested in next we'll
47:42take a look at working in the data
47:46field you can learn a lot about a career
47:49by looking at job postings if you've
47:52searched for opportunities in the data
47:54space you may have noticed different
47:56data related job titles with similar
47:58responsibilities or postings with
48:00similar titles listing different
48:02responsibilities here's an example at
48:04one company the role of data analyst
48:07will focus on using statistics and
48:09models to craft insights that inform
48:11business decisions another job with the
48:13same title at a different company May
48:15focus on optimizing the tools and
48:16products that automate analytical
48:19processes one reason for these
48:21inconsistencies is that data tasks and
48:23responsibilities are dependent on an
48:25organization's data team structure and
48:28how they make use of insights and
48:30analytics as such some organizations
48:33choose to be very specific with
48:35responsibilities others leave job tasks
48:38quite Broad in scope that's why this
48:40program refers to the field as a career
48:43space with that said when you're
48:45comparing positions that have similar
48:47titles I encourage you to classify them
48:49based on the skills used in their
48:51day-to-day activities two of the most
48:54common titles are data analyst and and
48:57data scientist these can cover a wide
48:59range of job responsibilities many of
49:02which you'll gain experience with in
49:04this program traditionally a data
49:07scientist was expected to be a
49:09three-in-one expert in data analytics
49:12statistics and machine learning but not
49:14all employers use these conventions when
49:16writing their job descriptions generally
49:19any role that includes analytics expects
49:22candidates to be able to function as
49:24technically skilled social scientists
49:26looking for patterns and identifying
49:28Trends within big data sets also they
49:31develop new inquiries and questions as
49:33they uncover the stories inside their
49:35data their hard work can help steer a
49:38company's future actions and guide
49:41decision-making they allow their
49:42organizations to keep a finger on the
49:45pulse of what's going on in the business
49:47interpreting and translating key
49:48information into visualizations such as
49:51graphs and charts allowing every
49:53stakeholder to understand their findings
49:56at times they may be tusked with
49:57creating computer code and models to
50:00recognize patterns in the data and make
50:02predictions when you investigate job
50:04postings you'll encounter other titles
50:06with similar responsibilities for
50:08example Junior data scientist data
50:11scientist entry level associate data
50:13scientist or data science associate all
50:16of these roles include a mix of
50:18Technical and strategic skills to help
50:20others make informed decisions in your
50:23career you might encounter other
50:25professionals enroll that use data and
50:27analytical skills some of these May
50:29overlap with the skills you will learn
50:31but these roles are specific to certain
50:34tasks or our supervisory positions let's
50:37take a look at a few data scientists
50:39depend on systems within their companies
50:41to collect organize and convert raw data
50:44designing and maintaining these
50:46processes are some of the most important
50:48responsibilities of a data engineer
50:51their goal is to make data accessible so
50:53that it can be used for analysis they
50:56also ensure that the company's data
50:58ecosystem is healthy and produces
51:00reliable
51:01results these positions are highly
51:03Technical and typically deal with the
51:05infrastructure for data usually across
51:08an entire Enterprise you also need to
51:10have the ability to get data before it
51:12even makes sense to talk about data
51:14analysis most of the technical work
51:16leading up to the birthing of the data
51:19May comfortably be called Data
51:21engineering and everything done once
51:23some data have arrived is data science
51:26similar to how a data engineer oversees
51:28the data infrastructure there are data
51:30roles that manage all aspects of data
51:33analytics projects for a company
51:34insights managers or analytics team
51:37managers often supervise the analytical
51:39strategy of the team or of the
51:41organization as a whole as a data
51:43analyst you will likely report to
51:45someone working in this capacity they
51:47are often responsible for managing
51:49multiple groups of customers and
51:51stakeholders and they're often a hybrid
51:53between the data scientist and the
51:55decision maker since this combination of
51:57skills is rare these positions are often
52:00more difficult to fill this role can
52:02have other titles like analytics team
52:04director head of data or data science
52:07director you may encounter another job
52:10role in your scan of job postings
52:12business intelligence engineer or
52:13business analyst this role is highly
52:16strategic focused on organizing
52:18information and making it accessible bi
52:20analysts synthesize data build
52:23dashboards and prepare reports to
52:25address specific needs for a business or
52:28requests from leadership if you're
52:30interested in learning more about
52:32business intelligence and its
52:33opportunities I encourage you to look
52:35into the Google business intelligence
52:37certificate now that you have some idea
52:39of the roles found within the data
52:41analytics career space we will begin to
52:43take a closer look at how data
52:45professionals function within their
52:47larger
52:50organizations compared with many other
52:53professions the data career space is
52:55relatively Young
52:57this application of datadriven work in
52:59organizations has grown exponentially in
53:01the last several decades which means
53:03there are many different opportunities
53:06and much job security for you in the
53:08future now that organizations have the
53:11technical capacity to take on their own
53:13data focused work they're looking for
53:15people like you with the right skills to
53:18fill these jobs traditionally companies
53:21have filled jobs in the data career
53:23space with those from computer
53:25engineering backgrounds or from
53:28statistics increasingly there's been a
53:30shift towards deemphasizing engineering
53:33and instead promoting analytical skills
53:36these skills can be learned in different
53:38forums like the program that you're
53:40currently enrolled in let's look at a
53:42scenario let's say that an enthusiastic
53:44and enterprising person that's you is
53:47starting a new position at a company as
53:49a data professional your company is a
53:52recognized leader in its industry its
53:54Workforce spans the globe and you are
53:57its newest member it's your first day on
53:59the job and you are ready to start
54:01working during your orientation your
54:04company grants you systems access and
54:06onboarding
54:07documentation you're starting to have a
54:09clearer picture of how information is
54:11generally shared with employees you
54:13still have many questions about the
54:15responsibilities of the position later
54:18you watch a video from the quarterly
54:20review meeting led by a company
54:22executive watching the presentation you
54:24get insight into the quarterly budget
54:26recent client interactions and some
54:28general information on an upcoming
54:30project you now have a broad
54:33understanding of the company at this
54:35point you still lack details about your
54:37specific
54:38responsibilities during your first week
54:41you're invited to a virtual meeting of
54:43the data professionals involved on the
54:45project that you've been assigned to as
54:48each data professional outlines their
54:50job responsibilities you take note of
54:52the differences among them after each
54:54participant speaks you begin to realize
54:57that not all data tasks are Universal
55:00and that many data professionals end up
55:02adapting to meet the needs of the
55:04current project and the needs of the
55:06data when you're new to a job I would
55:09discourage you from over specializing
55:11immediately instead taking on a variety
55:15of tasks within a project is a great way
55:17for newer data professionals to continue
55:19developing their skill set as a member
55:21of a larger group of data professionals
55:23you're able to observe and learn from
55:25your team members members once the
55:27analytical process is complete the
55:30results of the project will need to be
55:31shared allowing everyone in the
55:33organization to have access to the
55:35information this includes building
55:37user-friendly interfaces and
55:38communicating the findings to different
55:40departments working for a large company
55:42means there's a good chance that you
55:44will be dealing with vast amounts of
55:47information this will require more work
55:49than a single data professional can
55:51reasonably provide because of this you
55:53might encounter scenarios where
55:55organizations have created Creed teams
55:57of data professionals throughout the
55:59rest of this section you'll take a
56:01closer look at how complex organizations
56:03are incorporating data professionals
56:06through data teams and the division of
56:08responsibilities within these
56:12teams hi I'm Tiffany and I lead teams
56:15focus on building AI responsibly here at
56:18Google I've served in the United States
56:20Army worked as a consultant and have
56:22worked as a program manager in privacy
56:24and machine learning fairness data and
56:27having a rich understanding of data has
56:30always been an important part of my job
56:32today we have more data available to us
56:34than ever before and it's important to
56:37be able to derive insights to help
56:39decision makers make the best possible
56:41decisions I'm so glad you're here and I
56:44really hope this program is giving you
56:46all kinds of new possibilities to think
56:48about you've already learned so much
56:51we've covered the basics of datadriven
56:52fields and looked at career roles how
56:55data professionals are being used by
56:57different Industries and how those in
56:59the field can make a valuable
57:00contribution you're gaining a vast range
57:03of Knowledge and Skills which is going
57:05to be extremely valuable as you prepare
57:07to join us in the amazing field of
57:09datadriven careers at this point in the
57:12program I encourage you to take some
57:14time to reflect on how your experiences
57:17so far are setting you up for a great
57:19career and one way to do that is by
57:22enhancing your current online presence
57:24in the Google data analytics C ific we
57:26covered numerous job related materials
57:29including how to create an effective
57:30resume and Linkedin profile this video
57:34is about improving your existing career
57:36assets those of us who were involved in
57:39the Google data analysis certificate
57:41always love receiving learner feedback
57:43especially when it has to do with
57:44someone else's professional success I
57:47remember one person who took the
57:48initiative to refine her LinkedIn
57:50profile as soon as she began the program
57:53she noted that she was currently working
57:54through her program and she added to her
57:57profile many of the Technologies she had
57:59become familiar with well not long after
58:03she saw an advertisement for her dream
58:04job even though she was early on in her
58:07da education she decided to apply for it
58:10and she got it the hiring manager told
58:12her that the fact that she had
58:14familiarity with those data tools really
58:16set her apart from other candidates
58:19there are tons of stories just like this
58:21one that prove the value of having a
58:23compelling and professional LinkedIn
58:25presence
58:26so let's get into that now a
58:29professional online presence enables you
58:31to better connect with others in the
58:32field you can share ideas ask questions
58:36or provide links to a useful website or
58:39an interesting article in the news these
58:41are great ways to meet other people who
58:43are passionate about data focused jobs
58:46even if you're already part of the
58:47community strengthening your network
58:49makes it even more Dynamic LinkedIn is
58:52an amazing way to follow industry Trends
58:55learn from thought leaders and stay
58:57engaged with the global data analytics
58:59community and of course it has job
59:01boards and recruiters who are actively
59:04looking for data Professionals for all
59:06sorts of organizations and industries so
59:09it's a good idea to always keep your
59:10profile up to dat and to be sure to
59:12include a professional photo beyond that
59:15consider including a link to some of the
59:17relevant projects you've done in data
59:18analytics such as the portfolio project
59:21you'll work on during this program as
59:23you continue expanding your online
59:25presence to represent the work you're
59:27doing in data analytics the connections
59:29you make will be an important part of
59:31having a truly fulfilling networking
59:33experience plus there are also many
59:36rewarding in-person networking
59:37opportunities which we'll explore soon
59:40see you
59:42then recently you learned about the
59:45value of maintaining a professional
59:47online presence and connecting with
59:49others in the data field as I noted
59:51there are many professional networking
59:53sites such as LinkedIn that are well
59:55worth your time time and involvement but
59:58here's something that many people don't
59:59realize some of the best opportunities
1:00:01are never actually shared on a
1:00:03networking site sometimes there are
1:00:06professional opportunities that are not
1:00:08publicly advertised by the employer
1:00:11there are lots of reasons why some
1:00:13positions are not posted maybe an
1:00:15employer is concerned about revealing
1:00:17details about confidential projects to
1:00:19its competitors through a job posting or
1:00:21perhaps the company HR department
1:00:23doesn't have the resources to review a
1:00:26flood of applications often a business
1:00:28may choose to use a recruiter instead of
1:00:30posting jobs so let's start exploring
1:00:33how you can increase your visibility to
1:00:36access more opportunities through
1:00:38building valuable relationships after
1:00:40all the more people you connect with
1:00:42professionally the greater your chances
1:00:44are for being referred be sure to follow
1:00:47best-in-class organizations and
1:00:49Visionary Business Leaders on Twitter
1:00:51Facebook and Instagram interact with
1:00:54them and share their content if there's
1:00:56a post you like consider commenting with
1:00:58a response or a thank you you can also
1:01:01search for data field webinars featuring
1:01:04interesting speakers and many of these
1:01:05events are free this is another
1:01:08fascinating way to learn while
1:01:10connecting with peers colleagues and
1:01:13experts and there are also lots of blogs
1:01:15and online communities that focus on
1:01:18data fields data and tech companies will
1:01:20often talk about what's new and
1:01:22important from their point of view but
1:01:24there's a growing community of bloggers
1:01:26and podcasters who offer great
1:01:28perspectives of their own now let's move
1:01:31to in-person networking opportunities
1:01:34the easiest way to find events is by
1:01:36simply searching for data science or
1:01:38data analytics events in your area
1:01:40you'll likely discover a wide range of
1:01:42Engagement opportunities from more
1:01:44formal conferences and seminars to
1:01:46Casual meetups and get togethers
1:01:49nonprofit associations are also
1:01:51wonderful resources and may offer free
1:01:53or reduced rate memberships for students
1:01:57in addition to networking learning from
1:01:58a mentor can positively influence your
1:02:01career and life as you may know a mentor
1:02:04is someone who shares knowledge skills
1:02:07and experience to help you grow both
1:02:09professionally and
1:02:11personally mentors are trusted advisors
1:02:14and valuable resources the first step in
1:02:16finding a mentor is to determine what
1:02:18you're looking for to narrow down your
1:02:21potential list think about any
1:02:23challenges you face or foresee and how
1:02:25to address them in order to advance
1:02:27professionally then consider who can
1:02:30help you grow in these areas as well as
1:02:32fortify your existing
1:02:33strengths share these things openly when
1:02:36you formally ask someone to be your
1:02:37Mentor it's also helpful to know any
1:02:40common experiences perhaps you grew up
1:02:42in the same city maybe you both worked
1:02:44in the same industry your Mentor doesn't
1:02:47have to be someone you work with
1:02:48currently many people find mentors on
1:02:50LinkedIn an association mentorship
1:02:53program or at a mentor matching event
1:02:56this really taught me the value of
1:02:58mentorship I also learned that having a
1:03:01successful mentorship experience
1:03:02requires effort and investment in time
1:03:05whether you're preparing to ask the
1:03:06right questions internalizing the
1:03:09feedback or scheduling follow-up
1:03:11sessions but it's well worth it always
1:03:14be open to connecting with new people
1:03:16you never know where a single
1:03:18conversation will
1:03:22lead congrats on your progress so far
1:03:25and on taking me meaningful action to
1:03:26advance your career I wanted to let you
1:03:28know about some of the great career
1:03:30building activities and resources you'll
1:03:32encounter in the rest of this program in
1:03:35the next course and those that come
1:03:37after it you'll have the chance to
1:03:38complete a number of Hands-On activities
1:03:40Based on data driven scenarios they'll
1:03:43let you put what you're learning into
1:03:44practice and help you discuss your
1:03:46skills with hiring managers in a
1:03:49concrete way be sure to save your work
1:03:51from these activities they'll be useful
1:03:53to you as you near the end of the
1:03:55program and start thinking about the
1:03:56next stage of your data driven career
1:03:59when you get to the last course in the
1:04:01program we'll go indepth on preparing
1:04:03for a job search we'll cover how to find
1:04:06and apply for jobs that interest you
1:04:08I'll also share some tips to help you
1:04:10prepare for the interview process so
1:04:12you'll know what to expect going in
1:04:14you'll learn how to put together an
1:04:16online portfolio that will help you
1:04:17demonstrate your knowledge and
1:04:19experience you'll also complete a
1:04:21scenario-based project from beginning to
1:04:23end that you can put in your portfolio
1:04:25and and Ed to present your working
1:04:27process to potential employers with your
1:04:30past working and educational experiences
1:04:32your career Journey will be unique to
1:04:34you but whatever path you choose the
1:04:37knowledge and resources you gain from
1:04:39this program will give you a strong
1:04:41start you've accomplished so much
1:04:43already and there's so much more to come
1:04:46good luck on the next part of your
1:04:47journey I'm excited to meet up with you
1:04:50again
1:04:52soon as you approach the end of this
1:04:55section let's take a few moments to
1:04:56review some key Concepts before moving
1:04:58ahead we saw that the data career space
1:05:02has experienced amazing growth over the
1:05:04last decade future predictions indicate
1:05:07that this should
1:05:09continue you discovered that all kinds
1:05:11of companies and organizations are
1:05:13looking to drive their future decisions
1:05:15with data which is creating
1:05:17opportunities for data
1:05:19professionals additionally we saw that
1:05:21Innovation is the engine driving this
1:05:23continued growth you also discovered
1:05:26that data skills and tools are becoming
1:05:27more Universal at the same time experts
1:05:30foresee a continued specialization of
1:05:32roles within the different fields we
1:05:35were introduced to artificial
1:05:36intelligence and saw how it has become
1:05:38an important tool for data professionals
1:05:41we also took a brief look at some of the
1:05:43common roles and responsibilities in
1:05:45today's data professions while we
1:05:47reflect on the information we've covered
1:05:49so far please remember that you're not
1:05:50taking these steps of personal and
1:05:52professional growth alone one of the
1:05:55most beneficial official resources
1:05:56available to you is our discussion board
1:05:59take advantage of the opportunity to
1:06:01interact with like-minded Learners and
1:06:03gain knowledge from their experience
1:06:05coming up we'll take a closer look at
1:06:07the skills needed by data professionals
1:06:10and we'll investigate how larger
1:06:12organizations incorporate data analysis
1:06:15I'm looking forward to joining you as we
1:06:17continue your journey I'll see you in
1:06:19the next
1:06:22video we began our journey into the data
1:06:25profession profal World by covering the
1:06:27basics of data science Exploring Careers
1:06:30And discussing skills needed to succeed
1:06:33in this section we'll investigate the
1:06:35workflow within datadriven careers
1:06:37you'll be introduced to a helpful
1:06:39organizational tool called the pace
1:06:41model that can provide structure while
1:06:43allowing great flexibility when working
1:06:45on projects we'll explore the elements
1:06:47of effective communication and some best
1:06:49practices when sharing your ideas with
1:06:52others all of these skills and tools
1:06:54will help you prepare for your upcoming
1:06:56portfolio project I know you're excited
1:06:58to continue so let's get
1:07:03started the most important part of any
1:07:05project is preparation that involves
1:07:09thinking through all the necessary steps
1:07:11and anticipated tasks let's say you were
1:07:14planning a dinner party you would start
1:07:16by planning the theme menu the guest
1:07:18list and all the other details next you
1:07:21might check the reservation list dietary
1:07:23restrictions or take a trip to the
1:07:24grocery store store for ingredients
1:07:27afterward you would return home to
1:07:28prepare your dishes clean your space set
1:07:31up the table and get ready because you
1:07:33set up so much beforehand the evening
1:07:36will be awesome while this scenario may
1:07:38be fictional or not it offers some wise
1:07:42advice as a data professional being able
1:07:45to visualize data predict outcomes and
1:07:47quickly pivot away from obstacles makes
1:07:49you a problem solver and a great asset
1:07:52to any
1:07:53organization Benjamin Franklin once said
1:07:55by failing to prepare you are preparing
1:07:58to fail after almost three centuries it
1:08:00still applies to something as simple as
1:08:03a dinner party or as complex as a deep
1:08:05space
1:08:07mission regardless of the project having
1:08:09a structural framework in place for how
1:08:11to get that work done can be immensely
1:08:14beneficial I've done a lot of data
1:08:16science Consulting in my time and one of
1:08:18the most common problems I've seen is
1:08:20teams coming to me thinking that they
1:08:21need advice on which tool or equation to
1:08:23use and all of us find out during the
1:08:26meeting that they're solving entirely
1:08:28the wrong problem all the math in the
1:08:30universe won't help you if you don't
1:08:32point it in the right direction but it's
1:08:34easy to get excited about those
1:08:36nitty-gritty details and Rush headlong
1:08:38into a waste of time the best teams I've
1:08:41worked with have adopted a framework to
1:08:42help them focus on the most impactful
1:08:44actions in the most efficient order and
1:08:47they've had the discipline to use it to
1:08:49stay on track instead of running off
1:08:51into the weeds for those of you who
1:08:53completed the Google data analytics
1:08:54certificate you'll recall a structure
1:08:56for the data workflow that was divided
1:08:58into six phases ask prepare process
1:09:02analyze share and
1:09:04act this framework is quite useful for a
1:09:07multitude of projects but often with
1:09:10bigger data sets you need more freedom
1:09:12and flexibility the pace framework
1:09:14designed for this program offers the
1:09:16same workflow and structure but in a
1:09:18simpler way pace is a framework
1:09:21developed with input and feedback from
1:09:22our team of data professionals the
1:09:24intent of pace is to provide an initial
1:09:27structure that will help guide you
1:09:28through projects the goal is to lay a
1:09:31foundation upon which you will develop
1:09:33your own workflow practices the pace
1:09:36framework helps you solve problems and
1:09:39make judgments quickly and efficiently
1:09:42pace is an acronym each one of the
1:09:45letters represents an actionable stage
1:09:47in a project plan analyze construct and
1:09:51execute in the plan stage you'll Define
1:09:54the scope of your project you'll begin
1:09:57by identifying the informational needs
1:09:59of your
1:10:00organization this is where you'll want
1:10:02to ask yourself questions like the
1:10:04following what are the goals of the
1:10:06project what strategies will be needed
1:10:08what will be the business or operational
1:10:10impacts of this plan taking inventory of
1:10:13the project and the tasks required will
1:10:16help you get a better understanding of
1:10:18the context of your work and prepare for
1:10:21Success during analyze you'll engage
1:10:24with the data you'll start by preparing
1:10:26it for your project here you'll acquire
1:10:28the necessary data from primary and
1:10:30secondary sources and then you'll clean
1:10:33it reorganize it and transform it for
1:10:36analysis then you'll conduct a
1:10:38methodological examination of the data
1:10:41you'll also engage in exploratory data
1:10:43analysis or Eda for short this will
1:10:47involve converting the data into usable
1:10:49formats assessing the quality of the
1:10:51data and then diving into the data to
1:10:55find as many potentially useful insights
1:10:58and directions as
1:11:00possible you'll then work with your
1:11:03stakeholders to see which of those areas
1:11:07are worth pursuing in more detail which
1:11:09brings us to the construct stage where
1:11:11you're going to pursue a limited subset
1:11:14of all those potential insights that
1:11:16looked interesting to you in your Eda
1:11:19and here is when you will work with
1:11:22other data professionals potentially
1:11:24statistician and machine learning
1:11:26Engineers to do things like building
1:11:28machine learning models and revising
1:11:30those uncovering relationships within
1:11:33your data and doing statistical
1:11:36inference about those relationships
1:11:39finally in the execute stage you will
1:11:41share the result of your analysis and
1:11:44your collaboration with your
1:11:45stakeholders as well as the value that
1:11:49you've unlocked from your data here you
1:11:52will present findings to internal and
1:11:54external stat stakeholders answering
1:11:56questions and considering different
1:11:58viewpoints you'll also have an
1:12:00opportunity to present recommendations
1:12:02based on what you found in the data you
1:12:05may discover that you revisit the
1:12:07planning and Analysis stages as you
1:12:10refine models and incorporate feedback a
1:12:13good way to visualize the pace framework
1:12:15is as a completed circuit each of the
1:12:18four stages must be engaged for it to
1:12:20function correctly the electricity or
1:12:22flow of energy in the pace circuit is
1:12:25the communication between you your team
1:12:27and all the other stakeholders and
1:12:29collaborators involved when you look at
1:12:32the stages in this manner you might
1:12:35think that communication only flows One
1:12:38Way well you always do start with
1:12:40planning but don't be afraid to go back
1:12:43and iterate with Pace new information
1:12:46and feedback can be incorporated in any
1:12:49part of the process you might need to
1:12:51return to analyze to clarify some aspect
1:12:54of the data and then jump back to
1:12:56execute to present this aspect to your
1:12:58stakeholders without the need to
1:13:01construct new models or dashboards along
1:13:04the way you'll see how the pace
1:13:07framework can be scaled to fit within
1:13:09the scope of any project the model's
1:13:11adaptability will prepare you for a
1:13:13dynamic profession that requires a high
1:13:15degree of professional flexibility and
1:13:18communication regardless of where your
1:13:20career takes you the pace framework is a
1:13:22tool that provides a clear foundation
1:13:24and and structure through the continued
1:13:27application of the pace framework you'll
1:13:29prepare for each course's portfolio
1:13:31project then you'll have the opportunity
1:13:34to practice your evolving skills each
1:13:37portfolio project will introduce
1:13:38opportunities to develop and strengthen
1:13:41your organizational methods as you
1:13:44develop your own intuitive workflow the
1:13:46pace framework can be a great
1:13:48organizational tool next we'll look at
1:13:51how communication is so crucial
1:13:56when I was a kid I used to love to take
1:13:58things apart so that I could see how
1:13:59everything came together on the inside
1:14:01to make something work I actually still
1:14:03love doing that today and this is so
1:14:05much easier to do with tangible things
1:14:07that you can hold and look at like a pen
1:14:09for example I can twist the tip to pull
1:14:13each section apart from there I can pull
1:14:16the ink cartridge out and check out the
1:14:18way that the spring works in tandem with
1:14:20the top part of the pen to make it click
1:14:22studying the way things work becomes
1:14:24much harder when we start thinking about
1:14:26abstract Concepts like
1:14:28communication and you're probably
1:14:30wondering why it's important to talk
1:14:32about it in the first place in your role
1:14:34as a data professional you are the
1:14:36direct connection between the
1:14:38information inside the data and other
1:14:40project stakeholders so let's take a
1:14:43closer look at some key considerations
1:14:45when crafting a message all the
1:14:48communicative exchanges have three key
1:14:51elements that we need to keep in mind
1:14:53purpose receiver and sander when we
1:14:57think about purpose I want you to think
1:14:59about the reason why the communication
1:15:01is taking place in analytically oriented
1:15:04settings you might find yourself in
1:15:06situations where technical pockets of
1:15:07information require analysis or
1:15:10reporting on the other hand there may be
1:15:12contexts that depend on strategic
1:15:14insights which will be used to direct a
1:15:16company's Financial or organizational
1:15:19efforts the receiver is your audience
1:15:23when you think about the receivers of
1:15:24your message
1:15:25I want you to think about who you are
1:15:28talking to it's helpful when crafting
1:15:30Communications to ask what does my
1:15:33audience already know what do they need
1:15:34to know and it's important to keep in
1:15:37mind that every exchange can cause a
1:15:39rippled chain of events as a data
1:15:42professional you're often working as
1:15:44part of a distributed team across an
1:15:46organization and that's why a message
1:15:48shared with one receiver may be used for
1:15:51reporting with or two others the sender
1:15:55is the person responsible for crafting
1:15:56that message or communication yes you
1:16:00you're the sender the sender is a
1:16:02crucial part of any communicative
1:16:04exchange as the sender I encourage you
1:16:07to think about the following what's your
1:16:09relationship to the receivers what's
1:16:11your role in this exchange are you
1:16:13reporting insights are you pitching
1:16:15ideas or are you identifying potential
1:16:18data inputs also what personal biases
1:16:21might affect the message you're trying
1:16:23to share at the heart part of the
1:16:25relationship between purpose receiver
1:16:27and sender is the message or
1:16:29communication that you intend to share
1:16:31which is impacted by all three elements
1:16:34for this reason the same message might
1:16:36be shared in dramatically different ways
1:16:39when the purpose and the receivers
1:16:40change from Scenario to scenario for
1:16:44example you might know a data
1:16:46professional working on a complex
1:16:47project they've been involved since the
1:16:50project was sparked by an idea in a
1:16:52meeting the way they articulate the
1:16:54project pitch structure research models
1:16:57and findings will change depending on
1:17:00whom they're talking to with
1:17:02non-technical audiences they're much
1:17:05less likely to focus on details about
1:17:06the code used to program the model and
1:17:09instead they'll focus on the impact of
1:17:11the project when the audience shifts to
1:17:13other colleagues your friend May instead
1:17:15choose to be very detailed about the
1:17:17code and project Logistics in both cases
1:17:21the overall message exchanged that is
1:17:23information about the project was the
1:17:25same how it was crafted the details
1:17:28included and the way the information was
1:17:30organized that's what was different now
1:17:33that we've examined the key elements of
1:17:35communication we can really start to
1:17:37think about some of the best practices
1:17:39that will set you up for Success when
1:17:41you communicate in your future
1:17:46job have you ever stopped to think about
1:17:49what makes communication successful
1:17:51surely you can think of a time when you
1:17:53witnessed a communication failure I know
1:17:55I can communication is one of the most
1:17:58essential components in the exchange of
1:17:59ideas and Concepts it's a large part of
1:18:03everyday life and the foundation of all
1:18:05human interaction it's also an important
1:18:07element of analytically focused work
1:18:10that isn't discussed as often as
1:18:11technical
1:18:12skills in this video I'm going to share
1:18:15a few tips for successful communication
1:18:18My Hope Is that you'll rely on this
1:18:20advice in your future role as a data
1:18:22professional having a Clear Vision of
1:18:24why you're communicating is the first
1:18:26thing you need to consider your why
1:18:28depends on the context set by the
1:18:30business or organization that you work
1:18:31for as we discussed in earlier videos
1:18:35your why is also informed by the
1:18:37Project's mission statement that is the
1:18:39goals orienting the project and when
1:18:42crafting any form of communication use
1:18:44your why to guide the main ideas so that
1:18:47your audience can identify how to act or
1:18:50respond with purpose time is a currency
1:18:53in the professional world it's very
1:18:55important to be efficient as you draft
1:18:58or prepare to communicate I encourage
1:19:00you to use direct language minimize
1:19:02wordiness and avoid unnecessary details
1:19:05you should always strive for clarity use
1:19:08proper grammar and punctuation regarding
1:19:10vocabulary keep it simple choosing
1:19:13specific terms and avoiding technical
1:19:15language where possible break complex
1:19:18ideas into shorter sentences especially
1:19:20when addressing a diverse audience of
1:19:22professionals from outside data analyst
1:19:24itics this will make the points within
1:19:26your communication easier to understand
1:19:29and remember you may recall from earlier
1:19:32videos that I mentioned the three key
1:19:34elements of
1:19:36communication these are the sender
1:19:38receiver and purpose now I want to add
1:19:41to that list and consider the setting
1:19:43for your communication as well are you
1:19:46having a discussion at lunch with a
1:19:48coworker are you creating an email to
1:19:50update all the stakeholders about your
1:19:52progress or presenting the results of
1:19:54your analysis to a boardroom of
1:19:56Executives where and how you'll be
1:19:58delivering your message will have a
1:20:00direct impact on the way you shape it
1:20:03let's use a simple example to help
1:20:05illustrate effective communication like
1:20:07using a hose to water a plant the
1:20:09straighter the hose the better the water
1:20:11flows now bend the hose or tie a knot
1:20:14into it and the flow of water is
1:20:16interrupted in this analogy the water is
1:20:19like the intended message the hose is
1:20:21whatever method you've selected to
1:20:22deliver your message knots in the hose
1:20:25symbolize misunderstandings within the
1:20:27mind of your audience when the knots are
1:20:30created the free flow of information
1:20:32stops even if you continue to deliver
1:20:34information as a data professional you
1:20:37may find that a significant aspect of
1:20:39your work involves your ability to
1:20:40communicate with non-technical audiences
1:20:43your goal should be to meet your
1:20:45audience where they are what I mean by
1:20:48that is you want to be efficient in your
1:20:50communication and tailor it to the
1:20:52people you're sharing information with
1:20:55depending on how familiar they are with
1:20:57what you're sharing you likely want to
1:20:59break down technical Concepts into
1:21:01simpler terms avoid jargon acronyms and
1:21:04Technical buzzwords when everyone
1:21:06understands the intended message you can
1:21:08avoid the knots and the flow of ideas
1:21:10can continue carry these practices
1:21:13through all types of interactions email
1:21:15messaging and both virtual and inperson
1:21:19Communications as you continue
1:21:21throughout the program consider ways
1:21:23that you would communicate the technical
1:21:25concept you're acquiring to different
1:21:27audiences also remain focused on the
1:21:30purpose and setting of your
1:21:31communication all these factors add up
1:21:34and make a big difference to how
1:21:36effectively you communicate as a data
1:21:41professional previously you were
1:21:43introduced to the PACE framework as a
1:21:46reminder Pace has four stages plan
1:21:49analyze construct and execute we also
1:21:51examin the rationale and key elements
1:21:54involved in effective communication now
1:21:56let's investigate how they can be
1:21:58integrated together within data
1:22:01projects at first glance the pace
1:22:04framework easily enables a wide view of
1:22:06an entire Project's workflow that is
1:22:09each stage from planning to executing is
1:22:12logically sequenced with the beginning
1:22:13middle and end of a data project as you
1:22:16may recall though pace is incredibly
1:22:18flexible and it doesn't need to be
1:22:20employed in a linear progression often
1:22:23iteration makes sense in projects so
1:22:26let's unpack that with an example for
1:22:28reference we can compare it to the
1:22:29construction of a building first a
1:22:31blueprint is created this is where the
1:22:34planning happens next We Lay the
1:22:36foundation we can do this once we've
1:22:38analyzed the lot space and considered
1:22:40other variables like cost then we add
1:22:43the frame after that the roof can be
1:22:45installed by the time the structure is
1:22:47ready for move in the Builder will have
1:22:49progressed through the entire workflow
1:22:51planning analyzing constructing and
1:22:53executing the client's Vision in
1:22:56practice the pace workflow is meant to
1:22:58serve as a navigational tool we created
1:23:01it with the goal of helping you
1:23:02understand the data professionals
1:23:03workflow and as an aid you can consult
1:23:06in your future role now let's return to
1:23:09the example with the roof completed that
1:23:12makes it possible to begin the inside
1:23:14construction like installing drywall
1:23:16electrical components and Plumbing
1:23:18Systems each of these jobs also has its
1:23:20own pace workflow demonstrated on its
1:23:23own planning docum doent within the
1:23:25larger blueprint each requiring planning
1:23:28analyzing constructing and executing
1:23:30just like the building example we've
1:23:32been discussing as data projects move
1:23:35into the construct and execute stages on
1:23:37the global level you may need to return
1:23:39to the earlier stages to incorporate
1:23:41additional data or feedback from other
1:23:44stakeholders even while the global
1:23:46project is transitioning into a new
1:23:48phase of pace there can still be
1:23:50upcoming tasks that are just beginning
1:23:52their Pace Cycles regardless of where
1:23:54you might be in the pace workflow
1:23:57communication is what drives the
1:23:59framework to the realization of the
1:24:01project at each stage within the
1:24:02framework there will always be a need
1:24:04for communication to improve the
1:24:07workflow this could be asking questions
1:24:09about your data Gathering additional
1:24:12sources updating stakeholders on
1:24:14progress or presenting findings and
1:24:17receiving feedback one of the most
1:24:19important considerations behind the
1:24:21development of pace was providing a
1:24:24flexible structure that allows you to
1:24:26adjust changes within a project let's
1:24:29return to our building example let's say
1:24:32that during the installation of the
1:24:33electrical system the property owner has
1:24:35communicated that they wish to have an
1:24:37additional charging port for electrical
1:24:39Vehicles added to the plan to facilitate
1:24:42the change you would revisit the pace
1:24:44framework to plan analyze construct and
1:24:47execute this new request just like in
1:24:49this example requests from other
1:24:51stakeholders can come in at any time
1:24:53regardless of the timing of an
1:24:55additional request or task data
1:24:57professionals need to be available and
1:24:59accessible throughout the entire project
1:25:02cycle sometimes you may need to speak at
1:25:04a meeting or participate in a progress
1:25:06update Additionally you may update your
1:25:09progress within a tracking system email
1:25:11conversations and chat discussions will
1:25:13keep others involved and up to dat with
1:25:16where you are in your workflow I'm
1:25:18excited for you to develop some hands-on
1:25:20experience practicing different
1:25:22communication strategies through each
1:25:24stage of pace you'll have opportunities
1:25:27to do this later in the program for now
1:25:30I want you to remember that a good data
1:25:31professional is a proactive Communicator
1:25:34who responds to questions in a timely
1:25:36fashion keeping other stakeholders up to
1:25:39date with clear explanations can make
1:25:41you the most effective data professional
1:25:44that you can
1:25:46be you're nearing the end of your first
1:25:49course and we've discussed some key
1:25:51Concepts especially in the areas of
1:25:53community communication and data
1:25:55workflow through the pace framework as
1:25:58you're preparing for your first
1:25:59portfolio project I'd like to point out
1:26:02a vital consideration when using the
1:26:04pace framework each stage of the
1:26:06workflow is not mutually exclusive
1:26:09different roles teams projects and
1:26:11workflows may put emphasis on different
1:26:13stages of the pace framework so although
1:26:16you may be working on a task that is
1:26:18primarily focused on for example
1:26:20analyzing data there will still be
1:26:22elements from the other stages of the
1:26:24pace framework that will affect your
1:26:26work as well let's take a look at the
1:26:28way this program applies the pace
1:26:31framework we can take a Global
1:26:33Perspective and say that the earlier
1:26:35courses are heavier on planning and
1:26:37Analysis and the later courses are for
1:26:40construction and execution but that's a
1:26:42very wide view if you take a closer look
1:26:45you'll discover that each course is
1:26:47operating within its own pace framework
1:26:50in your python course you'll be
1:26:51acquiring knowledge to enable you to use
1:26:53the language for data analysis because
1:26:56you're acquiring new skills much of the
1:26:58course may be classified in the planning
1:27:00stage of pace As you move on to that
1:27:03course's portfolio project you'll shift
1:27:05into
1:27:06analysis but this will have elements of
1:27:09planning constructing and even execution
1:27:12as you create the final product next
1:27:14you'll learn how to prepare data to
1:27:16reveal the stories within here you'll
1:27:19lean into the analyze stage as you
1:27:22become familiar with the foundation of
1:27:24Statistics you'll continue analyzing
1:27:26data and you'll construct some new tools
1:27:29for your toolbox that you'll apply in
1:27:31your portfolio project while you're
1:27:34exploring regression you'll extend your
1:27:36experience in the analysis stage and put
1:27:38it into practice by constructing data
1:27:41models you'll also practice executing by
1:27:44summarizing the results and insights to
1:27:46provide value to your
1:27:48stakeholders Advanced modeling will
1:27:50further expand your analysis and
1:27:52construction skills Within that
1:27:54particular portfolio project and even
1:27:56while you're expanding your knowledge of
1:27:58career resources you'll be operating in
1:28:00the planning stage as you collect
1:28:02information the analysis stage while
1:28:04processing information about the job
1:28:06market and the constructing stage while
1:28:08assembling your resume and portfolio
1:28:10you'll have an opportunity to bring all
1:28:12your newly acquired Knowledge and Skills
1:28:14together in the Capstone project here
1:28:17you'll have the chance to use the entire
1:28:19Pace framework through scenarios and
1:28:21data provided by our industry partners
1:28:24even though some stages are more
1:28:26prominent than others in each course
1:28:28you'll still see evidence of all the
1:28:30stages throughout you could look at PACE
1:28:32as the scaffold that surrounds the
1:28:34exterior of a building with this
1:28:36scaffold in place different tasks within
1:28:38the building can use the same workflow
1:28:39structure without disrupting the entire
1:28:42project as you can see the pace
1:28:44framework is an excellent way to help
1:28:46guide you as you gain knowledge and
1:28:48confidence understanding how to classify
1:28:51tasks and proactively interact will help
1:28:53you you develop good habits and
1:28:55eventually develop your own professional
1:29:00workflow you've reached the end of the
1:29:03data workflow overview you've covered a
1:29:06lot of material and Concepts great job
1:29:09you saw how the pace framework can be
1:29:11used to help structure and guide you
1:29:13through projects next we learned about
1:29:15effective communication and how it can
1:29:17be used within the pace framework we
1:29:20looked at the external factors that can
1:29:21have an impact on data analysis we also
1:29:24looked at the external factors that can
1:29:26have an impact on data analysis like
1:29:28bias within data as you're preparing for
1:29:31the weekly challenge don't forget that
1:29:33you can review any of the materials we
1:29:35covered in the next section you'll begin
1:29:37working on your first portfolio project
1:29:40I wish you the best of
1:29:43luck attending a class watching an
1:29:46instructional video or reading
1:29:48information are all great ways to gain
1:29:50new knowledge however there's nothing
1:29:52like applying that knowledge when you
1:29:55actually do something this really helps
1:29:58confirm that you understand what you've
1:30:00learned this concept is called
1:30:02experiential learning which simply means
1:30:05understanding through doing it involves
1:30:08immersing yourself in a situation where
1:30:10you can practice what you've learned
1:30:12further develop your skills and reflect
1:30:14on your education experiential learning
1:30:17gives a broader view of the world
1:30:19provides valuable insights into your
1:30:21particular interests and passions and
1:30:23helps build build
1:30:25self-confidence in the context of this
1:30:27program experiential learning will give
1:30:29you the opportunity to discover how
1:30:31organizations use data analysis every
1:30:34day this type of activity can help you
1:30:37identify the specific types of
1:30:38Industries and projects that are most
1:30:40interesting and gain the confidence
1:30:42necessary to discuss them with potential
1:30:45employers this can really help you stand
1:30:47out during a job search soon you'll put
1:30:51experiential learning into practice by
1:30:53working on a portfolio project a
1:30:55portfolio is a collection of materials
1:30:57that can be shared with potential
1:30:59employers portfolios can be stored
1:31:01either on a public or personal website
1:31:03they can be linked within your digital
1:31:05resume or any online professional
1:31:07presence you may have such as your
1:31:09LinkedIn your portfolio project for this
1:31:12course will involve using the pace model
1:31:14to set up tasks of a project creating a
1:31:17portfolio project is a useful
1:31:19opportunity since companies will often
1:31:21ask you to complete some type of project
1:31:23during the the interview process
1:31:25employers commonly use this method to
1:31:27assess you as a candidate and gain
1:31:29insight into how you approach common
1:31:31business
1:31:32challenges completing this portfolio
1:31:34will prepare you if you encounter the
1:31:37situation when applying for data Focus
1:31:39jobs coming up you'll be introduced to
1:31:41the specifics involving your portfolio
1:31:43project you'll also receive clear
1:31:46instructions to follow as you begin
1:31:48working consider the Knowledge and
1:31:50Skills you've acquired in this course
1:31:52and how they can be applied to your
1:31:53project
1:31:54within each portfolio project you'll
1:31:56prepare a pace strategy document this
1:31:59will help you identify key points within
1:32:00each project to share with the hiring
1:32:02manager such as the many transferable
1:32:04skills you've gained a transferable
1:32:07skill is a capability or proficiency
1:32:09that can be applied from one job to
1:32:12another highlighting your transferable
1:32:14skills is especially important when
1:32:16changing jobs or Industries for instance
1:32:19if you learned how to solve customer
1:32:21complaints while working as a host at a
1:32:23restaurant you could emphasize the
1:32:25transferable skill of problem solving
1:32:27when applying for a job in the data
1:32:29field or maybe you learned how to meet
1:32:31deadlines take notes and follow
1:32:33instructions while working in
1:32:35administration at a nonprofit
1:32:37organization you could discuss how your
1:32:39organizational skills are transferable
1:32:41to the data analysis field the point is
1:32:44you've developed the ability to problem
1:32:45solve or keep things organized in one
1:32:47role you can apply that knowledge
1:32:50anywhere there are all kinds of
1:32:52transferable skills that that you can
1:32:54add to your resume reflecting on your
1:32:56transferable skills and the notes you
1:32:58take in your pace strategy document will
1:33:01help you consider how to convey
1:33:02technical Concepts clearly this will
1:33:05also help you demonstrate how you would
1:33:06apply your expertise across all kinds of
1:33:09tools and scenarios in the data career
1:33:11space and by the time you're done you'll
1:33:14not only have a very useful data
1:33:15analysis process document but also a
1:33:18comprehensive set of artifacts for your
1:33:20portfolio sounds exciting doesn't it
1:33:23let's get going
1:33:26when I interview people for jobs here at
1:33:28Google I love checking out their online
1:33:30portfolios I find that I feel more
1:33:32confident in candidates who can
1:33:34demonstrate their knowledge in a clear
1:33:36and compelling format having a portfolio
1:33:39has become incredibly common in the data
1:33:41field during a job hunt it's so valuable
1:33:44to Showcase your ability to understand
1:33:46business scenarios communicate
1:33:48effectively and use tools to solve
1:33:50complex problems your portfolio can
1:33:53really help you stand out from other
1:33:55candidates so far in this course youve
1:33:57gained lots of knowledge and job ready
1:33:59skills to help you Excel you've
1:34:01discovered the role of data
1:34:02professionals within an organization and
1:34:04typical career paths you've explored
1:34:05core analysis practices and tools and
1:34:08witnessed how data professionals use
1:34:10them to make a positive impact all of
1:34:12these things will help you successfully
1:34:14complete your portfolio
1:34:16project in addition you will apply what
1:34:18you've learned about team members
1:34:20stakeholders and clients such as their
1:34:22particular roles or priorities you'll
1:34:25begin by reading the specific project
1:34:27you'll be working on this reading will
1:34:29describe the type of organization you're
1:34:30working with the people involved the
1:34:32business problem to be solved and other
1:34:34key
1:34:35details this will enable you to further
1:34:38Define the project understand the
1:34:39stakeholders and consider key questions
1:34:42to answer in order to achieve a
1:34:44successful result then you will create a
1:34:46pace strategy document outlining the
1:34:48Project's purpose stakeholders
1:34:51deliverables and much more in this
1:34:54document you will begin to integrate the
1:34:56pace model to identify steps at each
1:34:59stage in the project for each portfolio
1:35:02project you will continue to use the
1:35:04pace model to guide you by completing
1:35:07each Pace strategy document you will be
1:35:09well on your way to developing your own
1:35:11data analytic workflow then in later
1:35:14courses you will continue working on
1:35:16your portfolio project and continue
1:35:18using the pce model to guide your
1:35:20process and by the time you're done you
1:35:23have designed something that you can use
1:35:25to really impress hiring managers plus
1:35:28you'll have a dynamic example of your
1:35:30data analytics skills demonstrating your
1:35:32thought process approach to the problems
1:35:35the key skills you've gained and lots
1:35:37more these are all great things to talk
1:35:39about during an interview all right
1:35:42let's get started it's time to discover
1:35:44how you will help an organization
1:35:46Advance through the exciting world of
1:35:51data you've completed the first
1:35:53portfolio project congrats planning and
1:35:56documenting your approach to the example
1:35:58situation is a useful experience as you
1:36:00begin thinking about your future job
1:36:02hunt soon you'll be able to impress
1:36:05hiring managers by discussing your data
1:36:06professional experience including
1:36:08understanding stakeholder requests
1:36:10establishing a clear and straightforward
1:36:12project plan and completing an effective
1:36:14strategy document in addition you'll
1:36:17understand how to plan out and organize
1:36:19the necessary workflow this is a big
1:36:22part of a data professional's process
1:36:24process also as you've learned it's
1:36:26helpful to communicate about your
1:36:28transferable skills with potential
1:36:30employers the information you've added
1:36:32to your pace strategy document will be
1:36:34valuable during job interviews there
1:36:37will be another opportunity to complete
1:36:38a portfolio project at the end of the
1:36:40next course at that point you will use
1:36:43the pace strategy document that you
1:36:45started here to continue developing your
1:36:47skills as a data professional then at
1:36:50the end of the program you'll bring
1:36:52everything together in order to finalize
1:36:54your unique reproach to this example
1:36:57situation the goal is to have a great
1:36:59example of your work that clearly
1:37:00demonstrates your skills to potential
1:37:02employers congratulations again and I
1:37:05hope you have a really rewarding
1:37:07experience as you continue working on
1:37:09your portfolio project throughout this
1:37:13program congratulations on finishing
1:37:16this first course you've already learned
1:37:18so much and now you're ready to take
1:37:20your new Knowledge and Skills and keep
1:37:22moving forward
1:37:24but remember if one day you feel like
1:37:26you need a refresher or just some extra
1:37:28practice these videos readings and
1:37:30activities will still be here whenever
1:37:31you need them now I'm excited for you to
1:37:34begin working with the instructor for
1:37:36the next course they're ready to help
1:37:38you take your next step towards
1:37:39finishing this program and continuing
1:37:42your journey as a data professional this
1:37:45course builds directly on all the
1:37:47interesting topics you've learned so far
1:37:49it will give you more insight into the
1:37:51Python programming language and how can
1:37:53be used to enable data
1:37:55analytics as you progress you'll
1:37:57continue building your data professional
1:37:59skill set and by the end of the course
1:38:02you'll be prepared to take the next step
1:38:04towards your portfolio project before
1:38:06you get started though I'd like to thank
1:38:08you for joining me in this course and
1:38:10choosing to pursue this exciting
1:38:12learning opportunity I strongly believe
1:38:15that education is a lifelong journey and
1:38:17I've no doubt that all the time and
1:38:19effort you put into experiences like
1:38:21this one will better equip you for
1:38:23anything you choose to pursue you've
1:38:25come a long way so take a moment to
1:38:27celebrate everything that you've already
1:38:29accomplished and then when you're ready
1:38:32head on over to the next course
1:38:35[Music]
1:38:45[Music]

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