 Students during last module, we talked about many libraries, like the scientific, numerical and visualization libraries. Now I will take you through one by one like I'll take a high level overview of different libraries, which you will be using in your data science journey. And the first one is scipy. Scipy as the name suggests, this is scientific Python. So scipy will be used for your scientific calculations and so many other situations where you will be able to use it. So scipy is basically for your statistical modeling, for your application of algebra and different scientific formulas, wherever you will have to use some statistical models, you will be using scipy. So scipy as we say, scipy is basically using numpy at the back end. As I mentioned earlier, all these libraries, most of them, they are inspired by one, like every library they have certain limitations or some advantages. So once someone started using a library's pose, if someone has used numpy, then they realize there are certain limitations with the numpy, so they introduce scipy. So this is how from one library, the next library or another library or another set of tools has been created. So scipy is fundamentally working on top of numpy. As I was just saying, this particular library is used for optimization. Optimization means when we are like, suppose we are training our data or we are testing different statistical models and we select which one is the best fit, so that is optimization. You can understand this in a way that if you plan a root, if you remember in statistical inference, we discussed about the journey planning, the root optimization. So one model suggests you or one calculation or one set of data suggests, okay, you should go at this time from point A to B, another model suggests something different and eventually with your judgment or with the help of artificial intelligence, one can decide which model is the most or the best fit. So that is what optimization is. So then there is linear algebra integration. These are all whoever has studied, I am sure if you have not studied mathematics, you can go through and these are very basic or, you know, entry level, like at your undergrad level, you must be studying some mathematics courses as well along with statistics or maybe in statistics, so there you will, you know, like the calculus, algebra, geometry, trigonometry, all these things you will touch whatever is required or the basic knowledge of all these, you know, calculus methods or the statistical methods or mathematical methods, whatever you will be using and again it is a learning curve. You start with one, two, three and keep on learning. This is how you will develop your skills in mathematics and statistics and of course the other areas which are required for the career of a data scientist. Interpolation again, this is again a specific mathematical method and special functions, these are, again, you know, as I shared with you while we were talking about or going through SQL, all these SQL databases, they have so many functions available. Most of your statistical functions or your arithmetic functions, they are built in your database so you should not really worry about these functions. Similarly, other common tasks required for the scientific and engineering like, you know, where you have to do something iterative, one by one or different and it will, again, depend on your project and the nature of the data and the nature of organization where you are working, but these are most of the things which you will be normally handling through your, you know, sci-pi library. So, with this you can, you know, there are different situations like K-means or the conversion factors or the algorithm, transform algorithms, your discrete transformations, numerical integrations, interpolation tools. So, there are basically some package or as I said, you can call them objects or a library within a library. So, within sci-pi, there are certain built-in objects or the features which you can call. Basically, they work in a way that during course of your, you know, code development, when you write a code, suppose you are on a certain line of your code, here you can call any function, any, you know, sub-function or a library or something like this. So, this is how it works, right? Then again, some of the other functions, IO, LINE, Selenius, all these are basically different functions which you will be using again at different, you know, situations. As I mentioned, you don't have to learn all these features by heart, right? Like this one is orthogonal distance regression classes and algorithms. So, regression, you know, we have studied what is regression, we know what are different algorithms where we can be measuring or applying regression model and things like this. So, various functions of multi-dimensional image processing. So, this one will be used for the image processing. So, we know that image processing is something totally different from where we are, you know, analyzing your text or processing text or the other, you know, like JSON and XML data. The image processing is totally different. So, there are special features available for image processing in your sci-fi library and then there is another library called PyTorch. It has also similar functions which you can use. Signals, spars, parsing and, you know, is something very, very important in this. Then, we've, you know, the statistical functions, special functions, specials, spars, signals. These are basically, as I said, you don't have to remember all this by heart. Once you reach in your learning curve on a certain stage, then you will be using these functions or the objects one by one. So, you don't have to worry or remember all these by heart but you should know the most important thing at this point is you should know that sci-fi is a library which can be used to perform different scientific functions of the methods. And that is, and then you can go, you know, when you are supposed working on a project or maybe doing some labs or some exercises, then you can refer back to your notes and go through this and see which is more, you know, suitable for your working.