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#1 Introduction to Python for Data Science | Python for Data Science — Transcript

by NPTEL-NOC IITM · 2,353 words · 339 segments · language en · Watch on YouTube

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0:02[Music]
0:04you
0:06[Music]
0:14welcome to this course on Python for
0:18data science this is a four week course
0:21we are going to teach you some very
0:25basic programming aspects in Python and
0:29since this is a course that is geared
0:32towards data science towards n another
0:36course based on what has been taught in
0:39the course we will also show you two
0:42different case studies one is what we
0:45call as a function approximation case
0:47study another one a classification case
0:49study and then tell you how to solve
0:52those case studies using the programming
0:54platform that you have learned so in
0:56this first introductory lecture I am
0:58just going to talk about why are we
1:03looking at Python for data science so to
1:08look at that first we are going to look
1:10at what data science is this is
1:12something that you would have seen in
1:15other videos of courses in the NPTEL in
1:19other places data science is basically
1:22the science of analyzing raw data and
1:25deriving insights from this data and you
1:29could use multiple techniques to derive
1:31insights you could use simple
1:33statistical techniques to derive
1:35insights you could use more complicated
1:39and more sophisticated machine learning
1:41techniques to derive insights and so on
1:43nonetheless the key focus of data
1:46science is in actually deriving these
1:48insights using whatever techniques that
1:50you want to use now there's a lot of
1:52excitement about data science and this
1:54excitement comes because it's been shown
1:57that you can get very valuable insights
2:00from large data and you can get insights
2:03about how different variables change
2:07together how one variable affects
2:09another variable and so on with large
2:11data which is not very easy to simply
2:14see by very simple computation so you
2:17need to invest some time and energy into
2:19understanding how you could look at this
2:22data and derive these insights from data
2:24and from you
2:27latarian viewpoint if you look at data
2:30science in industries if you do proper
2:33data science it allows these industries
2:35to make better decisions these decisions
2:39could be in multiple fields for example
2:42companies could make better purchasing
2:45decisions better hiring decisions better
2:48decisions in terms of how to operate
2:50their processes and so on so when we
2:53talk about decisions the decisions could
2:55be across multiple verticals in an
2:58industry and data science is not only
3:02useful from an industrial perspective it
3:04is also useful in actual science as
3:07themselves so where you look at lots of
3:10data to model your system or test your
3:14hypotheses or theories about systems and
3:16so on so when we talk about data science
3:20we start by assuming that we have a
3:24large amount of data for the problem of
3:27interest and we are going to basically
3:29look at this data we're going to inspect
3:31the data we're going to clean and curate
3:34the data then we will do some
3:36transformation of the data modeling and
3:38so on before we can derive insights that
3:42are valuable to the organization are to
3:45test a theory and so on now coming to a
3:48more practical viewpoint of what we do
3:52once we have data I have these four
3:56bullet points which roughly tell you
3:59supposing you were solving a data
4:02science problem what are the steps you
4:05will do so you'll start with just having
4:08data someone gives you data and you are
4:12trying to derive insights from this data
4:14so the very first step is really to
4:16bring this data into your system so you
4:19have to read the data so that the data
4:21comes into this programming platform so
4:23that you can use this data now data
4:26could be in multiple formats so you
4:30could have data in a simple excel sheet
4:32or some other format so we will teach
4:35you how to pull data in to your
4:37programming platform from multiple data
4:40formats so
4:41that's a first step really if you think
4:42about how you're going to solve a
4:43problem these steps would be first to
4:46simply read the data and then once you
4:50read the data many times you have to do
4:53some processing with this data you could
4:55have data that that is not correct for
5:00example we all know that if you have
5:04your mobile numbers there are 10 numbers
5:06in a mobile number and if there is a
5:08column of mobile numbers and then say
5:11there is a 1 row where there are just
5:14five numbers then you know there is
5:16something wrong okay so this is a very
5:18simple check I am talking about in real
5:20data processing this gets much more
5:22complicated so once you bring the data
5:25in when you try to process this data you
5:29are going to get errors such as this so
5:32how do you remove such errors how do you
5:35clean the data is one activity that that
5:37usually precedes doing you more useful
5:42stuff with the data this is not the only
5:45issue that we look at there could be
5:49data that is missing so for example
5:51there is a variable for which you get a
5:54value in multiple situations but in some
5:56situations the value is missing so what
5:58do you do with this data do you throw
6:00the record away or you do something to
6:03fill the data and so on so these are all
6:06data processing cleaning steps so in
6:08this course we will tell you the tools
6:11that are available in Python so that you
6:12can do this data processing cleaning and
6:14so on now what you have done at this
6:18point is you have been able to get the
6:21data into the system you've been able to
6:24process and clean the data and get to a
6:26certain data file or data structure that
6:30is reasonably complete so that you think
6:32you can work with this data set at which
6:34point what you will do is you will try
6:36to summarize this data and usually
6:39summarization of this data a very simple
6:41technique would be very very simple
6:43statistical measures that you will
6:46compute you could for example computer
6:48median mode mean of a particular column
6:51okay so those are simple ideas or
6:54summarize
6:55the data you could compute variance and
6:57so on so we are going to teach you how
6:59to use this notions of statistical
7:02quantities that you can use to summarize
7:05the data once you summarize the data
7:07then another activity which is usually
7:10taken up is what is called visualization
7:13right so visualization means you look at
7:15this data and more pictorially to get
7:18insights about the data before you bring
7:20in heavy duty algorithms to bear on this
7:24data and this is a creative aspect of
7:28data science the same data could be
7:31visualized by multiple people in
7:33multiple ways and some visualizations
7:35are not only ie caching but are also
7:38much more informative than other types
7:41of visualization so this notion of
7:44plotting this data so that some of the
7:47attributes are aspects of the data are
7:51made apparent is this notion of
7:53visualization and there are tools in
7:56Python that will teach you in terms of
7:59how you visualize this data so at this
8:01point you have taken the data you've
8:03cleaned the data got a set of data
8:05points or data structure that you can
8:07work with you have done some basic
8:10summary of this data that gives you some
8:12insights you also looked at it more
8:15visually and you've got some more
8:17insights but when you have large amount
8:19of data big data the last step is really
8:21deriving those insights which are not
8:23readily apparent either through
8:25visualization or through simple summary
8:28of data so how do we then go and look at
8:30more sophisticated analytics or analysis
8:34of data so that these insights come out
8:38and that's where machine learning comes
8:40and as a part of this course when you
8:43see the progress of this course you will
8:45notice that you will go through all of
8:47this so that you are ready to look at
8:49data science problems in a structured
8:51format and then use Python as a tool to
8:54solve some of these problems
8:56now why Python for doing all of this the
9:01number one reason is that there are
9:04these Python libraries which already are
9:07geared towards
9:08doing many of the things that we talked
9:10about so that it becomes easy for one to
9:14program and very quickly you can get
9:17some interesting outcomes out of what we
9:20are trying to do so there are as we
9:23talked about in the previous slide you
9:25need to do data manipulation and
9:27pre-processing there are lots of
9:30functions libraries in Python where you
9:33can do data wrangling manipulation and
9:36so on from a data summary viewpoint
9:39there are many of these statistical
9:42calculations that you want to do are
9:45already pre-programmed and you have to
9:47simply invoke them with your data to be
9:49able to show data summary the next step
9:52we talked about visualization there are
9:54libraries in Python which can be used to
9:58do the visualization and finally for the
10:01more sophisticated analysis that we
10:03talked about all kinds of machine
10:07learning algorithms are already pre
10:09coded available as libraries in Python
10:12so again once you understand some some
10:15bit about these functions and once you
10:18get comfortable working in Python then
10:20applying certain machine learning
10:22algorithms for these problems become
10:23trivial so you simply call these
10:25libraries and then run these algorithms
10:28at a higher level so in the previous
10:31slide we we talked about a flow process
10:35for how I get the data in clean it and
10:38all the way up to insights and then
10:40parallely we said why Python makes it
10:43easy for us to do all of this if you if
10:46you go back if you go forward a little
10:49more and then ask in terms of the other
10:53advantages of Python which are little
10:56more than just very simple data science
10:59activities Python provides you several
11:04libraries and it's being continuously
11:07improved so anytime there is a new
11:09algorithm those are coming into the set
11:14of libraries so in that sense it's very
11:16varied and there is also a good user
11:20community so if there are some issues
11:22with new libraries and so on and those
11:24are fixed so that you get robust library
11:27to work with and we talk about data and
11:31data can be of different scale so the
11:35examples that you will see in this
11:36course are data of reasonably small size
11:40but in real life problems you're going
11:42to look at data which is much larger
11:44which we call as big data so python has
11:48an ability to integrate with big data
11:51frameworks like Hadoop SPARC and so on
11:53and Python also allows you to do more
11:57sophisticated programming
11:58object-oriented programming and
12:01functional programming Python with all
12:05of this sophisticated tools and
12:08abilities is still reasonably a simple
12:12language to learn it's reasonably fast
12:13to prototype and it also gives you the
12:16ability to work with data which is in
12:18your local machine or in a cloud and so
12:21on so these are all things that one
12:24looks for when one looks at a
12:27programming platform which is capable of
12:31solving problems in in in real life
12:35right so these are real problems that
12:37you can solve these are not only toy
12:39examples but real applications that you
12:43can build data science applications that
12:45you can build with Python and just as
12:50another pointer in terms of ye I we
12:54believe that Python is something that a
12:57lot of our students and professionals in
12:59India should learn as you know there are
13:03tools which are paid tools for machine
13:06learning with all of these libraries and
13:08so on and there are also open source
13:10tools and in India based on a survey
13:14most people of course
13:18prefer open-source tools for a variety
13:21of reasons cause being one because it's
13:23free to use but also if it's just free
13:27to use but it doesn't have a robust user
13:29community then it's not really very
13:31useful that's where Python really scores
13:33in terms of a robust user community
13:35which can help with people working in
13:39Python so it's both open-source and
13:41there is a robust user community both of
13:44which are advantageous for Python and if
13:48you think of other competing languages
13:52for machine learning if you look at this
13:54chart in India about 44% of the people
13:58who were surveyed said they use Python
14:02or they prefer Python and of course a
14:04close second is art in fact our was much
14:07more preferred a few years back but over
14:10the last few years in India a python is
14:13starting to become the programming
14:15platform of choice so in that sense it's
14:18a good language to learn because the
14:20opportunities for jobs and so on lot
14:25more when when you're comfortable with
14:27Python as a language so with this I will
14:31stop this brief introduction on why
14:33Python for data science I hope I've
14:36given you an idea of the fact that while
14:40we are going to teach you
14:41Python as a programming language please
14:44keep in mind that each module that we
14:46teach in this is actually geared towards
14:50data science so as we teach Python we
14:53will make the connections to how you
14:55will use some of the things that you're
14:57seeing in data science and all of this
14:59will culminate with these two case
15:02studies that will bring all of these
15:04ideas together in terms of both giving
15:08you an idea and an understanding of how
15:10the data science problem will be solved
15:12and also how it will be solved in Python
15:15which is a program of choice currently
15:17in India so I hope this short four-week
15:21course helps you quickly get on to this
15:25programming platform and then learn data
15:29science and then you can enhance you
15:31skills with much more detailed
15:35understanding of both the programming
15:36language and data science techniques
15:38thank you
15:40[Music]
15:56you
15:57[Music]

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