 Yes, so Prabhu sir is already here. So Prabhu is basically the person behind a lot of the courses that you have, the modules that you attended on UX as well as the platform itself. I won't take up much time. I'll let him take over and interact with you folks. Hi everyone, this is Prabhu here. So I hope you've been enjoying the course so far. So much sir, so much. Very glad to be here. We started with your lecture sir on the UX. Yeah, so hopefully that was the boring part. I think your Django trainers are very experienced. So you should definitely ask them all the questions that you have. Sir, you are also explaining all the things very comfortably. I was easily so much happy without any... Glad to hear that. Thank you. I'm already in my advanced pipeline but still I've got to know many new things from your videos. So it was an experience. Very good. Thank you so much. So my purpose here is not to do anything technical, but okay first thank you very much for your very kind comments. I just wanted to say hello to everyone and answer any general questions, not necessarily Django specific because I think that the actual Django experts here are you know Anketh and Prathamesh and others. I don't know Aditya is also teaching but they're all Django experts. They use it day to day. So very good at that. So I will not field any questions about Django per se. What if you have general questions on Python or... Sir, somebody who is only 15 days old in Python and then this workshop occurred. What are the ways in which we can advance our knowledge? Sorry, so let me rephrase the question just to make sure that I understood your question. You're asking if you're only 15 days into Python, how can you learn more? Is that correct? Yes, because they are so... It's just like you are in crossing and there are so many ways. Yeah, it is a little daunting. It is a little scary because there are so many things to learn, so many libraries and every day there are new libraries. Yes, so that is a little daunting. Yes, we want to know what is the exact path which we follow to learn Python gracefully. Okay, yeah, that's a very good question. See, my own entry into Python and Niman programming is more from an application point of view. That is, I needed to do scientific computing and I like programming a little bit, so quite a bit. So I got into it that way. So my suggestion honestly is that you find something to do with the language because it is just like any other, to some extent it is like any other language. Even if you say, let's say you want to learn Bengali or something. And if you don't have any Bengali friends, you don't read Bengali literature, you don't see Bengali comics or you don't see Bengali movies or Bengali songs. There's very little chance for you to really grow in the language. So the best way is to interact with people in the language sense. So in computer programming, the best way is to really try to do something with the language. So now that you've learned the basics, maybe pick a task, depending on what your background is, you may or may not have time. So if you have the time, if you spend maybe every week, you spend an hour or two hours trying to work on some project that you want to do. Usually that's a really good starting point and that's how even I started. I was trying to do something and then I just got caught up in it and then I was like, oh, I can do it like this and then try to see how you can do it better. And you will then realize that it's the nice thing about these open source programming languages is you can learn it by yourself largely once you get started and you have the overall ideas. You can learn it by yourself, you can learn more by yourself and a lot of information is available online. The problem though is even if you start with nothing, no clarity, it's too many things. There are hundreds and thousands of libraries, so you won't know where to start. So honestly, my suggestion is you know something, you want to do something specific and you want to use Python for that, start doing something that you would like. And again, you don't need to ask somebody else for a project. You will always have your own things that you want to do. So for example, you want to build a small website with somebody, somebody can log in, then you can use Jambo. If you want to say, no, I want to do some scientific computing, then you can learn scientific computing specific tools. If you want to do some data analysis, then you can learn, you know, data analysis specific tools. But the instant you do that, then you're motivated. So, and then you can keep it as a small weekly task or something like that that you spend say, okay, Saturday afternoon, let's say you're free. You can just spend a couple of hours and say, okay, I want to like work on my project. You'll have fun because it's your project. It's not somebody else's project, you know, and you'll learn because you probably won't know all the libraries. So you search, you may need a starting point as to what to use, you know, what libraries to use or something like that. But usually the best choices are also kind of well-known. So if you say, look, I want to do data analysis in Python, you'll find 30 articles about it. And you pick some article and say, hey, this library is great. Somebody will say, okay, fine, you check it out, read it. And then you try those things, then you go read more about the library. This is the best way in my honest opinion to learn. Because unlike, you know, something like a theory subject, you know, it's really what you do that drives this rather than... See, it's not like mathematics or something where, you know, you need to work a lot to understand a huge amount of basics. And then you can start doing research or, you know, do some interesting things. Here it is like, you know, the language is typically fairly simple. And then it's the ecosystem around that language that takes some time learning. So that's my advice. Sir, but according to you, what are the basic libraries whose knowledge... I mean, nobody can call themselves a Python expert today. No, they can't. So what are the basic libraries that you need to know if you're a Python developer or Python faculty? So even in this, so I will answer in... If you don't mind, I'll answer in English simply because I don't know if everybody here is comfortable with Hindi. I can answer in Hindi. My Hindi is not great, but I can still... I don't mind speaking. No, sir. But if there's somebody from Tamil Nadu or somebody like that, I don't want them to feel left out. Is it okay if I answer in Hindi? Sir, can you speak in English? Yes, sir. Okay, fine. I'll keep it in English just so that everybody gets it. It's unfortunate that we need to speak in a foreign language so everybody can understand, but it's okay. See, this is again the same thing, you know, because the number of libraries is so large, I can't say there is like a baseline set. So supposing you're coming at this purely from a web development perspective, then in fact, you don't need to know Nampai. You don't need Pandas. You don't need this, that, the other. There you'll need to learn requests. You'll need to learn, you know, Web APIs. How do you write, you know, a REST-based API? You may need to look at Flask or Bottle or, you know, then look at Django, of course. But so there are... So it depends on what area you want to focus on. And you will find this very commonly in the Python environment itself. You'll find somebody who's an expert in Django, but they will know something else in some other domain. Like you may ask me something, I know a lot about something in say, in one area, in say scientific computing with Python. But you ask me something else in Python, I may not know. So it is the same thing here. So you need to first pick what you're interested in. Then the basic libraries can be set, you know, you can tell you. So you say, data analysis, I would suggest start with Pandas. Right? If it's a web development, you need to understand something about web APIs. It helps to have a lower level understanding of the basic protocol. What is the HTTP protocol? How does it work? How does the website work? You understand that and say, okay, fine. Maybe it's how does security work on this? Then what is Django? Where in this entire web development area does Django fit? For example, there's client side, there's server side. Well, client side is very different. Then it's not even Python. Well, now it's changing, but the dominant thing now is Python. Sorry, it's JavaScript in the front-end side. But even that, there are options now slowly starting to come up in Python as well. So it really depends on the area. So if you give me a specific area, I can give you some answers if I know them. If I don't, we will ask somebody else. Like data analytics, data analytics. So data analytics, my suggestions would be Pandas would definitely be the first piece you need to learn how to use Pandas. You learn how to use Pandas well. There's a lot of documentation. There are lots of tutorials online. You may even find tutorial videos. So for example, if you go to SciPy, if you go to YouTube, there's a SciPy channel where they have like a four-hour tutorial on Pandas. Somebody would have done it. So you need to know NumPy also if you want to do Pandas. So no, learn how to use NumPy well. Learn Pandas. Then you can learn Matplotlib slash C-bond for plotting, whatever it is that you would like. There are a whole bunch of plotting utilities. So that's an old universe by itself. But Pandas itself will expose some set of plotting UIs. So that's like a very good starting point. If you're really good at Pandas, you can do a lot of stuff. But then there's the other angle of data analysis is statistics. And that has two components. So you need to learn like decent amount of statistics from the math side and understand data analysis from that point of view. And then depending on whether you're doing frequentist or Bayesian, you can choose SciPy has some things that you can do for some frequentist approaches. But if you want to look at Bayesian, there is something called PyMC4 or PyMC3, which does MCMC sampling for Bayesian analysis. There are a bunch of Bayesian related tools. There are even good books that you can find free on them. So it really depends again. There also depends on which kind of area you're trying to specialize in. There are also some good books if you want to do data analysis. There's a nice book by Eileen Downey. He has a book on think Python. He also has think stats. It's a really nice book. He also has a book on think Bayes, which is Bayesian statistics with Python. But he doesn't use the libraries. He focuses more on the basic concept so you can learn things from there. So again, if you want to really get good at something, you need to read a lot and do a lot. So that's what it takes. So that's the reason I'm suggesting you find something that's of interest to you. And you spend time on that. That's the best way because then you have the motivation. Otherwise if somebody says, look, you have to spend, you know, 100-200 hours before you get to become an expert. It's like a lot of work. That is very interesting in the area. It makes it easy. Sir, area is complex, right? Because Python is being used everywhere. At this date. Yeah. It's being used everywhere. Yes. So yeah, so there's a lot. Was there another question you asked or was that... Sir, I have a question, sir. Yes, please. I'm interested in web development and I have just finished with the NodeJS and JQRI. But I'm in dynamic programming in web development. So, sir, with Django, what are the other things that I need to learn? So I'm capable of becoming a dynamic web developer. I don't know what you mean by dynamic web developer. I mean, you mean to say very good developer. Is that web developer? Means basically, sir, like news, news, news portals basically changing on the day-to-day basis on our... So I think Django was initially built for some news kind of platforms. Yeah. So Django is definitely something you need to learn. But I mean, this is just a workshop that gets you started. So what you need to do is do more, learn more, read all the documentation. See if you can find a tutorial online as well. Read that. There's also books on Django. So you can pick up a book and learn that as well. So they'll typically do a project in a book. They'll take a large project and explain various aspects. Now, if you're building a large website, you may have other kind of things to worry about as well. So if you're looking at DevOps, how do you make a website that's going to scale? That's like an entirely different set of tools and infrastructure. There's nothing to do with Python. I mean, you may find Python-based tool somewhere there, but that's not Python. That's DevOps. So that's a separate site. If you're doing front-end, then you need to learn master JavaScript. Whatever front-end you're going to work on, each of them is like a separate universe of its own. How do you write good tests, good quality code? So basically, it's a constant drive by yourself to do more. I don't know there's something. Ankit, do you think there's something else that needs to be learned? Oh, yes. Yeah, I mean, if by dynamic web development, maybe the person was talking about these more snazier front-end driven websites. Yeah. So those will be heavily, then you'll have to learn some framework like, you know, I don't know, React or whatever new fashion there is in JavaScript today. You'll have to learn those and then learn a lot of good CSS as well. Yes. Yeah, so let's sort whatever new flavor there is today. So yeah. Yeah. Hi, Sabha. So I'm a student of artificial intelligence background. I'm also interested in astronomy and cosmology. So could you suggest something like which language I should go with? What are the different courses which I can do? So astronomy and what does that mean? Cosmology. Cosmology. Yeah. So I mean, these are three vast fields. I don't do astronomy cosmology myself, but I know that astronomers in Python at least use this package called AstroPy. It's used by a lot of the large labs because it has a support for a wide variety of astrophysics related image files and things like that. So it really depends again on what you're looking at. But say for example, if you're doing optical astronomy, you know, your large aspect of it is doing a lot of image processing. So you can enter through image processing. That's one particular way of doing it. If you're looking at cosmology, you know, there's again the physics side of it, which is, you know, you can either do the theory side of it or the experimental side of it. In which case, you know, it really depends on what you're doing. And also like being an artificial intelligence student, like we have different languages, which is the language which is preferred. Well, I would be biased and I would say, you know, Python is a good pick, but there are many. So there's like lots of programming languages that support ML and AI. But yeah, Python is among the strongest. There's also Julia, which has a good AI story for sure. But yeah, the most of the most popular libraries are in Python. You know, there's TensorFlow, there is PyTorch, of course. And then there's, I think, MXNet. There are many libraries. But the dominant two, for example, TensorFlow and PyTorch, they're Python based. So Python will help you that. So the thing here is even with programming languages, whatever one you learn, you need to be able to do a lot with it. And you need to learn it well. That's the first thing. So supposing you're good at C++, you can do a lot, especially now, because C++ is a much better C++ 11 and all this cleaned up a lot of things. But still it's a harder programming language. And so, you know, doing things like interactive development, quickly making plots, you know, analysis of that kind is much easier and something like that. But, you know, if you have your strength is C++ and you like C++, you can still do things there. But usually most people who work at that level work at one level lower than Python. So you're typically building the building blocks and then people will build on top of that. Now, if you're doing cosmology and you're trying to computational cosmology, then you have to deal with high performance boards, which is another, that's another separate universe. And there again, it depends on what level you're planning. If you're going to use existing libraries, you don't need to know all the low level tools. But if you want to use the low level tools and extend them, then you need to learn the low level tools as well. But there's a lot that you can do with just Python at the high level. So Python, NumPy, SciPy, scikit image is for image processing. So you can do that. If you want high performance, you can use something like Numba for performance. But yeah, so there's a whole host of these tools. But they are good starting points. And then Astropy, if you're specifically interested in astronomy and things like that. Thank you. I believe there are some courses. I don't know you and me or somewhere else. A colleague of mine was trying something. He was taking a course on astronomy with Python and stuff. I'm sure there'll be courses online, but I think you can find them out whenever. So if you find something interesting, take it. Sure. So, Ankit, there are several questions on the chat which I have not seen. Should I go through them? Okay, fine. I'll try to answer a few. So maybe you can tell me if you have answered something. So I'm unable to optimize code. Can I give some tips regarding it? So it depends on what code you want to optimize. If you want to write something better in Python and you want to stick to pure Python, then you need to understand, you know, at least data structures and algorithms. Very often, you know, we write code that is not cognizant of our data structures. So for example, if you're trying to search through a list, for example, so we're trying, you have elements, you have say a lot of fruits that you've stored in a list object and you're going to sort of search saying, okay, find if something exists in this and let's say you're doing that at any times. That's going to be horribly slow because search through a list is an order of N operation. It's not a constant time operation. So you need to be aware of some of these. On the other hand, if you wanted to speed that up, you would use a dictionary or a set because there it's, you know, it's a key map based a hash map, so it's much faster. So it's constant time access. So it really depends on, you know, what the task is and then having a reasonable understanding of the basic data structures that the language provides and how well the language performs. And one trick that a lot of people who try to get performant code do is they will write small snippets and you can use the time it macro that I don't know if it was done in the course lectures here, but there's a Python macro called magic function called percentage time it. So you can just give it a single statement and it'll execute that statement in times and then tell you how fast it is. So there's a good way to get a sense of, oh, you know, this thing is a little fast. This is slow. This is how it works. And then you start digging deeper into Python's implementation, read online. You'll learn a little more about optimization at the Python level. If you want to now go to a level lower, so let's say you have numeric code, like code that uses numpy or anything like that. A good starting point would be something like number where you just do a decorator, make sure you're only, you know, you're only manipulating numpy arrays, you're not creating new objects or things like that in a function. And you can have number do the optimization and basically convert that code into something that can basically directly converts it into LLVM IR. So basically into a low level code or machine code automatically for you. And that is executed on the CPU. So you can use tools like that to speed it up. And if that's not enough, you can now go one level lower. You can implement it in C or C++ or Fortran or, yeah, or say CUDA or OpenCL or whatever. And then you can wrap that to Python and there are also lots of tools to do that relatively easily from the Python, from the Python there. So it really depends on where you're trying to optimize what the only thing that you should be very careful about is don't unnecessarily optimize things that don't need to be optimized. So first thing you need to do is profile your code. Make sure you're paying attention to the actual code that is taking the time, most of the time. So use a profiler. There is a good profiler, Scalene, S-C-A-L-E-N-E. It's a nice profiler. So you can use Scalene to do your profiling. And then once you find out what are the functions that are slow or fast, you can optimize all the slow functions, both at the Python level or at the C level. So that is the suggestion. One second. I noticed a question that sort of could resonate with you. Manish Narnavari asks, what is a good book to keep Python to a novice? I will try and answer that. So what's a good book to keep Python to a novice? Maybe what is a good resource is more general. Yeah, so there are several books. None of them are... Some of them I don't quite... They're all at different levels. So it depends on your audience. If you have students who are primarily from an engineering, non-CS background, what they require is slightly different. If you have folks from a CS background, they have really no choice. So you can do the standard approach of teaching, which most textbooks take. So Alan Downey's Think Python is a good book. There is a nice book. If you have more science-y bent, I want to do interesting things with scientific computing, sort of, at a high level. There is Dr. Ajit Kumar's book. It's available freely online on his xpies.in website. I think Ankit, you can share a link. He has a PDF of a book that he has written, and that's freely available. If you have a background on already no basic programming, the way I started learning Python is through the Python Tutorial. It's actually very well written. So that's a nice... It's a tutorial, so you can just go through it and sort of walk you through the various features of the language. So if you're already a programmer, it's easy. But if they are a novice as in complete, novice to programming in general, then some of the other books like Think Python, those are all better books to teach Python. So usually when I've been teaching Python, I don't quite refer to a book. I kind of teach it. I have not used a book. I've been working on a book myself, but it's not finished yet. So I'll make that available when that happens. But yeah, those are books. There used to be a book called Dive Into Python, which I read many, many years ago, which is nice. But again, that is not necessarily for beginners. I think Swaroop had written a nice book long back, but I don't know. I really don't know what is the best book for someone to learn. My suggestion is you look at a bunch of books. Most of them you'll find at least table of contents or at least the blurb or a chapter or sample chapter. Find the book that talks to you the most and go with it. At least this is what I'm finding with a lot of even other subjects. Different books appeal to different people based on their personality or what they like. So there's no harm picking more books than one. Okay, next question. How much time will it take to master Python? I don't know, infinite time. You never master it. It's a language. It's like everything else. You're always learning. And the language is also evolving. It's not a fixed thing that you master and you attain something and you're done. And the same way, once you attain it, it's not like it always stays with you. So for example, if I don't program for two years in Python, I will forget all my questions. Remember how to think about writing a good program, but I may not remember the actual syntax. And I may not remember all the languages. Like even now, I can't remember which book. Somebody asked me a book and like, I have to recall names, scaling. I don't forget. So this happens. Yeah, and then you have also suggested a nice book, Automate Boring Stuff with Python. Yeah, okay, fine. Yeah, ML and AI, can you suggest some resources and modules? Yeah, so there's lots of tons of resources and modules are ready. So for example, PyTorch has some really nice tutorials. So if you're already familiar, those tutorials are great. We're learning PyTorch pages, reading their tutorials. So those are nice resources. The scikit-learn as well. Scikit-learn documentation is excellent. So you can learn, scikit-learn has really good talks. You can start there. And in fact, you may find very good tutorials online on scikit-learn. And in fact, every year at scipy US, there is a tutorial or two, usually one or two tutorials, like four or tutorials on machine learning. Some particular like, could be scikit-learn, could be PyTorch, whatever. Okay, more questions. Aha, somebody's asked a difficult question, a direct message. What should I learn if I am interested in Android development or mobile development? Yeah, so that's a hard one. That's a hard one because there are not many, many good tools in Python for this. There are, there is something called QV and there's an Android for Python, but none of them is like writing it natively with Java and the, you know, Android development kit and stuff, or even Kotlin or other programming languages, which are kind of designed for the mobile space. Things are changing now though. So if you want to build something that's heavily front-end driven, so something based on a browser, you can do that because it is starting to change because you can actually write front-end related stuff with Python. It's a little heavy because you have to have a Python runtime, but it has been done and it's not very hard to do. But yes, that is a space which is kind of not very strong Python. So Android development, I would say that, you know, Python is not a very strong story. There's something called, I think, something called DeWare or something like that. Yes, DeWare. Okay, I keep forgetting these names. Okay, so DeWare is a thing which allows you to write it in Python, but I don't know, I've not used it. I checked it out several years ago, but there's some status update as of August and they do have something. All right, so you can check it out. There are some projects, but not too many. Kivi is another one, K-I-V-Y, Kivi Python. And then you can see there may be cross-platform Python for NUI development, so native UI development. So yeah, so there are some of these and Kivi Python framework for mobile development also you can check out. So some of these, and there's also Android for Python, but some of them are not, as I said before, not the same scale or not the same ease or anywhere near the level of maturity that standard Android SDK is at. How does R compare to Python on data science works? Okay, so this is a, I don't know if it's a bait question. So I'll be a little careful. So firstly, R is really fantastic for statistics. The kind of libraries and statistical packages you will have access to in R is something that you will not get direct access to in Python. However, there is an R Python bridge, which means if you have an R runtime, you can load that into Python and vice versa. You can do stuff from R in Python to some extent. So that is one way to sort of bridge the two. But if you ask me personally for my kind of work, I think as a language, Python is a nicer language. It's easier to understand, easier to reason about Python like as a computer scientist would look at it. R code is kind of more magic key that is, you know, you do something and it's like, how does this work? You know, it's like kind of magic sometimes. At least for me, I don't know R very well though. So I should be careful when saying that. But I think as a language, Python is a stronger language. It feels more natural to me. And Python is a lot more general purpose than R in the sense that I don't know if there are any web frameworks written in R and I don't think there are too many of them. But R is really good at a very specific niche. So supposing you want to build an app which does a bunch of data analysis and statistics and you want to share that online, R has an entire ecosystem for this. It has shiny apps. And the nice thing with R is there's only kind of, there'll only be one or two tools at best. You know, there's Knitter or something like that. But shiny is like one and only. Whereas in Python, for the same thing, you want to put, you know, a little data processing app online and you want to share it with a UI and stuff. There'll be four or five different solutions. So this is both an advantage and a disadvantage. So there's one thing called panel. There is, Plotly has its own thing. Then, you know, there's a bunch of these players, you know, in Python, the star, streamlit. There are a lot of these little packages which allow you to sort of put up an app with a dashboard or something like that where you can pull a slider and it'll do something, you can upload a data set. So this is again the same kind of thing. Python is so incredibly popular that you'll find lots and lots of things. But in certain niche areas, especially so if you're looking at statistics, R is like very hard to pick because R has an extensive set of packages and a huge community of people who build projects under R. So that kind of thing. So if it's very specialized, then you'll probably have to get into R. But if you're writing a general purpose thing, then I would suggest Python. So it really depends again who you are and what you're trying to do. But if you're just doing statistics for the sake of statistics and you're not a programmer, you're looking at just stats. You want to just say, okay, I want a library's access to the latest statistical packages. I would suggest try R, use R. But if you are like uncomfortable with the language, you're more comfortable with the Python's way of doing things than think about an R to Python bridge. So you can use some of the R tools in Python. In the past, this was very bad. There was a big difference between R and Python. But gap is going closer and closer as Python is getting better and better, closer and closer. Like Pandas is like R's data frame. Pandas data frame is like very simple. The thing is now it's not just Pandas. There's also XRA, there's DASC. Now there are a bunch of tools in Python which go beyond Pandas. So, yeah, that's the nice thing about Python. Lots and lots of things. Can we take one last question and then I can, I'm trying to sort of do a hostile takeover because we need to complete a bit of... Yeah, I'm going to skip the goal and question because I don't, Python is not... Yeah. You can skip the opinionated questions if you want to. We see AI and cloud computing in coming years. I have no idea. Okay. Cloud computing is here to stay. AI is there's a lot of hype around it so be careful. It's our best book resource hosted on DSA. What is DSA? I don't know what DSA is. Data structures and algorithms. Oh, okay. Yeah, then you have to pick a classic, read a nice, you know, the classic textbooks on, you know, data structures and algorithms. There's a book by Goldwasser and Goodrich and all that data structures and algorithms in Python but you know, I would suggest you pick a good data structures and algorithms book in and of itself and then, you know, go from there. There are a whole bunch, right? There's one by, you know, I think for drivers now. What's the most popular data structures book? I forget now the title of that also. No, there are too many books that show up for me right now. Okay. Yeah, you can pick a standard, see a reference text. There are lots of them. Look at the algorithms. So that's what I would suggest. Okay. ML and AI, can you conduct such workshops and training sessions? Okay, that's good feedback. Any other questions, Ankit that I missed? No, most are in the same vein. So like data structure and algorithm, how to use Python. Data structures and algorithms, so it really depends, you know, so if you're looking at it from the low level, you don't even need a programming language, right? If you're trying to understand data structures and algorithms in abstract, you don't need a programming language. But if you really want to apply it to a specific programming language, yeah, you can always use any language, you know. Python is easy to write, so use it in Python. But the problem is some things are like, why would you write this in Python? Somebody's already written, you know, a dictionary or a hash map. You don't need to write it again. So they really want to say, no, I want to like, waitlifting, they say, you want to torture yourself writing algorithms. Yeah, you should do it. You can write it in any language. You can write it in Python. You can write it in C, C++. Somebody's asked Python for image analysis. Yeah, it has pretty good tools. OpenCV is accessible through Python. And scikit image is also very good. So I would suggest scikit image, scikit-image. Security, I'm sorry, I don't know. How do you use it in security side? There are some people who talk about it. But again, I think they are cross cutting. You know, it's not like, there's nothing specific. Security, why is that Python or C++ or something else? It's better suited for. If you want to learn it. Okay. Python for AR, VR. Yeah, good question. There are tools for doing visualization and graphics in Python. AR, VR really depends on the hardware as well. So, yeah. I don't know if SDK is per se, but so Apple has its own set of stuff. Android will have its own set of stuff. Something cross-platform for that domain I'm not familiar with. So I need to look up that. I don't know. Python for use in EarthScience is the same as any other engineering system. So yes, a lot of people use Python for EarthScience. It is geophysics, geomechanics. Yeah, you can use it there. Okay. I think I've answered as much as I can in the short time. I have not answered everything, some of which I don't have the answers for. No problem. Thank you for joining in, sir. I understand that there will always be questions. So in Professor's absence, I guess we can try and answer them later if you want to. But generally Python for X or Python for Y is something that you can always get hold of on the Internet. Usually a lot of resources need this. So Professor Prabhu, thank you for joining in. I hope you could answer most of your questions. Thank you very much. Nice to see you all. Wish you all the best and hope you enjoy the rest of the workshop and hope to see you again in future workshops. Thank you. Thank you, sir. Bye. Thank you, everyone.