Hello and welcome to this new section, which is data analysis using Python.
So in this section we will learn how to perform data analysis on a set of data.
So this is an intro lecture when we will learn what is data analysis and why Python is actually used
to perform data analysis.
So let's first go ahead and understand what actually data analysis is.
So the simple definition of data analysis is analyzing data from multiple sources to extract meaningful
information.
So in data analysis, what we do is that we determine some data source, which we have to analyze.
That is, let's say you have a website or a e-commerce website which is going to generate tons of data
on a daily basis.
So if you want to analyze that data, you simply extract data from that source, you perform data analysis
on it using some programming, using some language like Python.
And then after doing data analysis, you get some meaningful information which you could use to make
better decisions.
So that is the basic and the most simplest definition of data analysis.
And obviously we could go into more complicated stuff.
But for now this is what data analysis is.
Now let's learn why data analysis is required.
So the first and foremost reason is that it helps us make better decisions.
So a lot of organizations like, let's say, the e-commerce website Amazon has to make business decisions
on a daily basis.
So these organizations actually rely upon the data which they get.
So for example, if you have a e-commerce website like Amazon, there are going to be a tons of users
visiting your website on a daily basis.
And what they are going to do is that they are going to generate a large amount of fraud data.
Now this raw data can actually be analyzed and information could be drawn out from that data and that
helps Amazon or any e-commerce website for that matter, to make better decisions.
And the next most important thing is best use of available data.
Now, not only Amazon, but any data intensive organization is going to produce a large number of data
in the back end, and throwing away that data does not make any sense.
And that's why this data is actually analyzed so that some conclusions could be drawn from that data
which could be more valuable to the organization.
So this is the main purpose of data analysis and there could be a lot more.
But the basic purpose of a data analysis or the basic significance of data analysis is to help make
better decisions for organizations as well as to make the best use of the available data.
So why data analysis is the future?
So what I've done here is that I've laid out the basic setup of an e-commerce company, or you could
say a e-commerce website, and we will learn how data is used by this organization.
So as you could see, we have the e-commerce website at the center, and on the left hand side you could
see a user.
So what this user actually does is that he visits the e-commerce website and he basically browse through
or goes through the e-commerce website.
He might view certain products, he might buy certain products and he might carry out a bunch of activities
on the e-commerce website.
So what the e-commerce website actually does is that it generates raw user data that is nothing but
the user's browsing history.
It also stores the products viewed by the user.
It also stores a bunch of things like which particular product the user bought and everything like that.
So every time a user visits on a e-commerce website, raw data is generated at the back end.
And just imagine this thing with a big site like Amazon.
So there are going to be a million users visiting per day on Amazon who generate a raw data.
And with this raw data, what we could do is that we could perform data analysis on this raw data and
we could find certain things like which product is being sold in which category, what are actually
the most viewed products by the user, which are the trending products, which products the user are
not buying and everything like that.
So you could use that information.
That is, let's assume that from the data analysis we came to know that Apple products are being sold
most in the mobile category.
So this information can be used to make decisions like as the users are buying Apple products in the
mobile category, let's place that product at the top and let's place the other products at the bottom.
So in such a way that raw data can be converted into useful information using data analysis and later
that information can be used to make logical conclusions.
And depending upon those conclusions, we could optimize our e-commerce website.
Now, this was one of the most simplest example of data analysis, and obviously I have oversimplified
things here.
A lot of them things actually happen in the back end.
But this is what generally happens when you perform data analysis on the raw data.
So now the next thing which we will learn is that why Python for data analysis?
So actually there are a lot of options to perform data analysis.
You could use a tons of tools or a tons of or other languages as well.
But the reason for using Python for data analysis is that it's faster as compared to other alternatives.
Now, when you talk about data, one obvious program.
Which comes into mind is Excel.
And Excel is actually good at analyzing data, but Excel is only suited when the data is very less.
So let's say if you have 100 rows of data, that's okay.
You could analyze that with Excel, but when you have a million user data like Amazon or any other e-commerce
website for that matter, then using Excel makes no sense because it's obviously too slow and there
are not a lot of tools in Excel which which you could use to perform meaningful data analysis.
So Python is comparatively much faster as compared to Excel or other alternatives.
And that's why Python is actually used to perform data analysis.
The next most important thing about Python is that it has simple syntax.
Now, even if someone is not a programmer or if he's not from the computer science background, even
then he could learn Python because it has a simpler syntax.
And that's another reason why most people prefer Python for performing data analysis.
And the most important and the last reason for using Python for data analysis is that it has multiple
libraries for data analysis.
So Python actually has a bunch of tools or a bunch of options for data analysis like numpy, pandas
and everything like that.
So we are actually going to learn about numpy and pandas in the upcoming lectures as well.
So this is the main reason why we choose Python mostly for data analysis.
So that's it for this lecture.
And hopefully you guys were able to understand what data analysis is, how data analysis is performed
and what is the significance of a data analysis, and also why Python is chosen as the best language
for analyzing data.
So thank you very much for watching and I'll see you guys next time.
Thank you.