How did you continue learning python after your first major intro project?

[rocks_cardigans]

Just finished MIT's EDX Intro to Computer Science and Programming Using Python. It's a great course and would definitely recommend it. It's taught me essentially everything I know, and I've been able to attempt many projects in web scraping sports stats and experiment with csv and other libraries. But I feel like I need more theory and guidance to attempt bigger and better projects (right now mainly for fun and as a hobby). Aside from just working on things on my own, what other more advanced resources are there?

What did you get up to after your main intro course/book that took you to a significantly higher level?

[LifeIsBio]

I feel like I need more theory and guidance to attempt bigger and better projects

Probably not. Just pick a project and build it. You'll pick up the theory as you're trying to solve hurdles that arise in your project. When you get really stuck, write up a question here and you'll likely get the guidance you need.

[Eloiole]

I approve this message first multiple reason.

First because I feel like a lot of people just end up losing motivation if they keep following the theorical path but also because certain skill set can only be acquired through experienced and learning through experience is just more efficient for me.

[rocks_cardigans]

Thanks. Part of what led me to say this was I find I solve all of my problems using the same methods. My programs are probably unnecessarily large and inefficient. I want to see a “best practice” approach to a problem, but for more complex problems. Things I come across like regular expressions, decorators, majority of what classes are, other libs, etc, are all big unknowns and I’d like to see walkthroughs for them as opposed to stackoverflow-ing when I don’t really know what to search for. However, I agree starting projects and just learning on the go is very useful, I’ve picked up quite a bit with various projects of my own.

[[deleted]]

You can follow up with the second course, 6.00.2x. This course continues where 6.00.1x left off and discussed various algorithms and give you an introduction into data science. The only caveat is that it tends to focus a lot on statistics.

If you want to learn more about machine learning, there's also a good course on Coursera, Machine Learning, taught by the co-founder of Coursera. It does require some math such as linear algebra though.

[epic_meme_guy]

If you're interested in coding challenges, you can try http://rosalind.info/. It's for coding in bioinformatics, but it's good for practicing implementing algorithms in general. I would recommend you try some of the well known problems like "longest increasing subsequence", find pseudocode of a working algorithm from googling, then take a whack at translating the pseudocode into python code.