 Students, welcome to the next module of your Introduction to Data Science course. As you are aware that for the last couple of modules, we have been covering Python, one of the most used programming language and one of the favorite for the data scientist. Now, you know, we have covered like installation, Python community and the overall environment, the difference between Python or integrated development environment and in development and learning environment, right? Now, we'll cover some of the libraries. You know, like for every technology, there are certain good things about it. And the best thing about Python is or the strength of Python is its libraries. As I have already shared with you, there's a huge, very big community which is supporting Python and that is the strength. So many people, they are, you know, contributing towards Python and from one library, they are developing another so on and so forth. At the moment, there are hundreds and hundreds of libraries available, but I'll share with you one of the few of the most, you know, commonly used, you can say, or the preferred or the most widely used libraries of Python. So basically, we can categorize these libraries in three different categories. First is the numerical as you can understand from the name that numerical means, which are handling numerics, data and things like this, right? And the next is your computational where we do a lot of calculations and computations. And third one is visualizations. So visualizations, there is a complete section dedicated to visualization where we'll cover all that and so many other things about visualization or the data visualization. But in this area or in this, we will just focus on the libraries which are related to numeric and computational libraries. So these are some of the libraries which are the, you know, top most used numerical libraries. So as from the name specifies, the first one is scipy. It is basically scientific Python. This is used where we are using a lot of scientific formulas at the models. The next one is pandas. Pandas are basically where we can store arrays of data, different matrices of data. And then there are so many other uses. The panda is one of the most used even among others. We were talking about scipy, numpy, and you know, other libraries with pandas is something which you will mostly use. Then ipython is again an integrated python environment or interactive python. This is a shell command which you will learn also. Shell, the concept of shell is very, very important and you will understand this soon. And as soon as you, you know, come with your, go along with your learning and you learn some other languages. Shell command or the concept of the shell, it is like an object and you will soon understand that how efficiently you can use it and how effective it is for your programming and not only in python but so many other, you know, languages and environments. This is basically a concept which we can use in so many applications. And then numpy again as I earlier said, this is numerical and scientific and then natural language processing toolkit. This is a very, very powerful library as the name suggests natural language. It can be used for your text because when we are talking about data science we have already discussed about structure data, unstructured data and the text is unstructured data. And there are some other forms also of unstructured data which is image processing and the whole they are, you know, stored in the data. So this toolkit is very, very useful again in these areas and in certain circumstances where we have to do, especially, you know, this, I'll talk about this when I talk about, you know, especially we covered this a little bit about this library. Actually, I will cover all these libraries one by one, but on a very, very high level. Then the computational libraries, as I mentioned, these are for computation for your scientific or the statistical data modeling and things like this. So we have PyTorch, TensorFlow, Scikit-learn, Dinos and Keras. These are all your N and another very important thing is that these are just few of the libraries but at the same time these are most preferred or most commonly used libraries. So once you start using one library or the one set of library or one type of library, you will learn about the other libraries, right? So this is like your toolkit, you know, where you have so many libraries available and you will be using them one by one. And if you remember in data wrangling and at some other, you know, discussions when we're talking about SQL and so many other things, the one thing which I discussed so many times that is data pipeline from source to processing, enrichment and till visualization. So you will, once you understand the whole concept of the data pipeline, I'm sure up till now you have developed a quite, you know, good understanding about the data pipeline. So you can use different libraries at different locations of your data pipeline or different stages of your data science process, whatever way you look at it, right? So this is how you can make use of different libraries. Then the third type of library is the visualization libraries. These libraries are used for the data visualization, right? So data visualization is basically representing the data in such a way where one can see and just can make some opinion about the data. So we understand that initially there are libraries where we are getting data from the source systems, then it comes into your integrated data science environment. We do some processing, we apply different mechanism or different statistical models, enrichment, you know, there are so many things which you will be doing during this process, like from the source till the data visualization. So data visualization, again, there's a very large number of libraries available. Some libraries which are very, we can say just belong to Python, which will be used in Python or using Python language. And there are so many other languages which can be again used along with your Python libraries or Java or C++ or other programming languages.