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Thank you @pratiksen7809! Happy New Year to you too!

So I _think_ I was using Jupyter Notebooks (as opposed to the newer Jupyter Labs I use nowadays). For that I was using a theme and settings I document here: tinyurl.com/bddfupbz But for the newer JupyterLab I just use their default dark theme.

Glad it sparked some new learning for you! 😃

Thanks!

I typically don't use Celery unless I actually need its more extensive (but more complicated) features. Also, I believe this is still the case, Celery is not supported on Windows, so YMMV.

Thanks!

Sure, you could just choose to write in C, Rust, etc. But then you'd lose out on packages such as PyTorch, TensorFlow, Scikit-learn, Keras, Theano, FastAPI, Pydantic, etc - basically the Python package ecosystem. (and many of those packages *are* written in C/Rust/etc so from a pure performance perspective using Python does not give up much).

Thanks!

Thanks!

Thank you!!

@@mathbyteacademy any news on your new course?

Merci Totor! 😃

@@mathbyteacademy Mais rien. Amplement mérité.

Thanks, glad you liked the courses! For APIs, I would probably learn FastAPI. You'll need a foundation in Python type hinting and Pydantic as well (I have videos on type hinting and Pydantic in this channel, or even a full course on Pydantic on Udemy). I would look for YT videos and blog posts on REST API concepts in general (does not have to be specific to Python or FastAPI) to learn how REST APIs are structured and some of the best practices. Then look for FastAPI specific content. I would also probably learn about various databases, starting with relational databases such as Postgres, and maybe a nosql database such as Mongo. As far as distributed systems go, once you have FastAPI covered, you'll need to learn about how to use message queues, and then how to architecture distributed systems. I don't know of any specific resources that will cover all of that - at this more advanced stage, you'll probnably need to piece your knowledge together from a variety of sources - courses, YT videos, books, blog posts, product docs, etc.

​​Thanks Fred for your advice. I really appreciate your advice and trust it because of your background as a mathematician and an experienced software engineer and also your DD in Python is such high quality teaching and I can't wait for what next you have to offer! Also I have enrolled for the Pydantic course so I can start learning APIs next after finishing it :) ​@@mathbyteacademy

@@mathbyteacademy I got to know from your udemy profile that you are into data engineering. Can we expect something on that?

Thanks! Glad you liked it.

@@mathbyteacademy is YoYo production ready? Also I do not understand why in Python there are just a few migration tools and nothing that is even close to e.g. Go migrate.

@@szymonpiechutowski2340 Yes, YoYo is production readyu - been using it for several years now. SQL migration tools don't have to be language specific, so plenty of choices around if you don't want a Python base migration tool.

@@mathbyteacademy Thank you so much for sharing your experience! btw Course on SQL would be awesome. If you create a waiting list, I can join it!

Thanks! I will add it to my list of future topics to cover.

You are welcome!

Very true 😆 Only project I could think of that has a lot of modules would be pypy - maybe there are larger ones out there. I counted the number of modules in that project (basically just counted number of files with .py extension)... 4586 !!

Thank you!

Thanks!

I __have_ been thinking about a course on FastAPI, especially since I now have the course on Pydantic, just haven't had the time to dedicate to it. GraphQL I probably would not do - I have not used it enough to have what I consider sufficiently deep knowledge - at least not to create something beyond the run of the mill intro type "courses".

@@mathbyteacademy understandable, well in that case I really hope you make an in-depth Fast API Course on Udemy! Also, do you have any videos on how to properly read programming language documentation? I know that sounds simple but I highly doubt I could get even half the knowledge you have regarding more advanced python topics just from reading documentation, I feel like there’s probably something you do differently than I do. Thankfully we have LLM’s now so when I come across vague or poor documentation that doesn’t have many examples I can most of the time have ChatGPT explain further with examples but I would really love to know how to pick up in-depth advanced knowledge like you have regarding a language. I’m assuming you have a methodology by now that lets you approach programming documentation and lets you extract a deeper understanding than myself?

Thanks! I don't do web development (I assume you mean client-side things like React, etc), so no course on that from me. There are a few good ones on Udemy already anyway. I you mean backend stuff to support web clients, then I do plan on trying to get a course done on FastAPI at some point.

​​@@mathbyteacademy Yes, I mean backend. That's really good to know!

Thanks! Probably not, for a variety of reasons, the main one being that it takes a lot of time to produce in-depth and detailed courses, and some of these libraries have a tendency to change quite a bit, breaking backward compatibility. Which means I would have to update such a course quite frequently, certainly more often than would be viable. I cover the basics of NumPy, Pandas and Matplotlib in my fundamentals course, and Pandas has been a pain in the you-know-what for that reason. Also, if I were starting out, I think I may look at Polars instead of Pandas.

@@mathbyteacademy Thanks for replying back! Yes, your reservations are completely understandable. Sometimes as learners we often fail to appreciate the time and effort it takes for the instructor to produce quality content, and update them whenever necessary. Nonetheless, what advice would you give to someone who's just getting started with NumPy, Pandas, and Matplotlib, apart from viewing the basics from your course?

@@tirthasg In terms of my fundamentals course, the sections are still very relevant - numpy is pretty stable, and changes in Pandas have not been too great (but still, I hate when libraries just break backward compatibility). Matplotlib has been also good, I think, like NumPy. So you could certainly start there, or you could look for books, RUclips videos, blog posts, maybe other courses. But with any of those resources, be ready to also consult the official library documentation for any breaking changes between the time the contetn was created and whatever latest version of the library you decide to use. Then, once you have the basics a bit, time to start coding with them - that's really the only way to learn - and as you try things out, you'll most likely need to go to the library docs (this is something I highly recommend getting used to as early on as possible). Books, courses, YT videos can only take you so far - then you need to deep dive on your own - various blogs and stackoverflow will help you along quite a bit when yuu have *specific* things you want to learn more.

Thanks!

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Glad it was helpful!

Thanks!

Thanks, will look into doing a video on that. Although I do cover the tenacity library in this channel - if you have not seen it before I recommend looking at it, it's really useful.

Hi, not at the moment.

@@mathbyteacademy 😔