Hello, guys, and welcome back to another class of our course about the complete introduction to data
science.
So we saw many things in the past few classes.
And today, of course, we are going to talk about data analysis and about Banda's.
So basically we are going into this class.
More specifically, we are going to have a complete introduction to Pender's and talk about what is
it, what is data analysis, the concept of data analysis, and make a quick recap of what we can do
with Biton.
And we are going to have in this part of the course, not in this class, but in the few next classes,
we're going to learn how to use Banda's like we did for the past.
Based on what?
For the past.
Based on program that we learned a little bit before.
All right.
So let's start.
So basically, it's important to understand the basics of Python before jumping inside of Pendas.
And for that, we will make a quick recap of what we saw at the beginning of the course.
So basically, Python can have many applications, such as web development, simple programming for
different programs, for example.
It could be for machine learning.
It could be, for example, for Aronow in many fields, it could be in security, it could be in finances.
It could be in many different fields, fields, but it can also be in research and analysis.
So basically in data analysis and this is exactly what we are going to talk about in this part of the
course.
And we are going to learn some tools that can be really, really useful for this world, for this to
be able to do this part of the course.
Basically to be able to do data analysis.
So here are some some things that you guys can do with Python.
So as you can see, development, research and analysis, data science and many others, there is no
security here.
So there are many other things that you can do with Python.
All right.
So understanding data analysis.
So basically, the first thing that we need to talk about when we are talking about data analysis,
the data lifecycle, since data analysis is part of the data lifecycle.
So what you will see in the for example, you guys are looking for the data lifecycle is that you will
see plenty of different things like this that will show you different data, life cycles, they're all
correct.
So there is no correct or incorrect thing that represents, well, data lifecycle.
Some of them will have seven like this one right here.
Others will have six.
Some of them will have five.
But at the end of the day, it's pretty much the same thing, just with different names and different
different things inside of it.
So basically it's just different names.
But at the end of the day, it's the same thing.
A word that will always come inside of those representations will be the analyzing parts, since the
analysis of the data is one of the most important things that you guys can do.
Because when you analyze that, you understand you transform does that.
So you take raw data and you will transform it because the analysis path is where the data is transformed.
So you take everything.
Well, you you can you are able to take all the raw data and transform it into data that you can use
later to answer different questions or for research purposes or for any other purposes that you can
that you want, basically.
So it's pretty much explained here.
So you have your data, you need your analysis, and at the end you are able to answer to your basic
question questions.
So your first question or simply make some discoveries, even if it doesn't answer your question, you
are able to make some discoveries just by analyzing this data.
So basically, what is data analysis?
So it's set to be able to perform data analysis with Python.
It's really important to use, as I explained data analysis a little bit before, now it's important
to understand how we can perform it with Python.
So be able to be able to perform that analysis.
With Python, we will use a special module called Banda's, and this is exactly what we'll talk about
right now.
So basically, Panders is one of few models that, well, you of many models that exists with Python.
And basically it's a software that is used for that.
I'm going to end the analysis.
It's up that is built on the different the other applications, such as No Escape by Muttalib.
So as I explained you guys a little bit before, matplotlib is used for data visualization.
So basically to represent charts and graphs and to represent many other things.
So basically to represent your data.
And it's also used and not by and SkyBitz are are the fundamental packages.
A scientific manipulation, so scientific computing, sorry.
So basically it's important to understand that PANDAS is really a whole works with all those applications
and there are plenty of benefits of using pandas.
So basically, some of here you can find some of them.
And as I said, really, there is a lot of different things that you guys can do with by using pandas.
So the first one right here, as you can see, will be more done for less coding.
So basically, you don't have to do that much with pandas to be able to write down the exact same thing.
So basically, there is less coding.
It's really created to work with Python.
So it's not only create work with Python, it's made for Python.
So this is another really good advantage of it.
There is an extensive set, an extensive set of features.
So basically it's really powerful and it will provide you with a huge set of commands and features that
can be used to easily analyze your data, which is pretty amazing.
It can also handle really large data and very efficiently.
So basically, when you guys work with really huge databases, this is the perfect tool to work with
it.
And finally, there is a great data representation.
So basically it will represent the data really in a great way.
So it will offer you really streamlined forms of data representation and that there are many other things
that, well, there are many other advantages of using Pandora.
I just didn't route them here.
All right.
So right now, guys, you have a small, small introduction to pandas.
So basically, you know that panda is a tool that works well.
Basically, it's a software that that works with Python.
So basically it's a main application inside of Python and it's really used for data analysis.
And in this part of the course, we are going to learn how to use it.
So this week, you guys will have an introduction and will be able to work in the future with pandas.
So that's it for this.
Guys, guys, into our next class.