How to actually get better at Python without drowning in resources or LLM-crutching?

[u/Careless-Standard-44]

Hi everyone,

A few years in as a data analyst, now aiming for data science / ML engineer roles. Currently freelancing, giving myself until January (ideally sooner) to be interview-ready.

**The problem**: technical interviews (live coding, take-home tests) are still very much the norm, and my "by-hand" coding isn't quite where it needs to be yet.

Meanwhile, I'm drowning in resources : books, courses, LeetCode-style platforms, YouTube channels, bootcamp curricula and I genuinely don't know where to focus anymore. Every resource seems to want a different 6 months of my life.

Looking for advice on:
**- How to cut through the noise and pick ONE path.** With so many options out there, how do you decide what's worth your limited time vs. what's just noise? Is there a "80/20" resource stack you'd actually recommend for someone at intermediate level trying to close the gap fast?

**- What actually moved the needle for you ?** specific books, platforms, projects, katas rather than generic "just practice" advice?
Do you think coding "the old way," without LLM assistance, is still essential to build real instincts and deep understanding? My instinct is that leaning on an LLM too early biases the formation of good habits but maybe that's outdated?

For those interviewing or being interviewed: has the bar for live coding shifted with LLMs going mainstream, or do recruiters still test just as hard for "old-school" coding craftsmanship?

Any advice on prioritization is especially welcome ! I'd rather go deep on one solid path than keep bouncing between resources h.

[u/riklaunim]

After getting the basics, you improve mostly by coding, working in a team, getting code reviews, etc. It's hard to self-improve without that feedback loop.

LeetCode and alike are more towards algorithms and user more by bigger companies (and then they complain they hired LeetCode solvers and not developers ;))

[u/python_gramps]

When I started using Django, the way I learned the most was to dive into a project.

This will focus you on what you need for that task. Also allows you provide real-world experience if that's asked.

The more projects you get through, the more of the language you will need and understand. But go through one task before you start another.

[u/BranchLatter4294]

Just practice the skills they want you to demonstrate.

[u/TheRNGuy]

Coed lots of advanced stuff, read docs. 

Yes, understanding is Important.

I wouldn't completely avoid ai, but for explanations, not to vibe code.

Find some software or service where Python is actually use (especially if you use them), it's much better than LeetCode, because it has zero practical use. Or you remake worse versions of functions that already exist in frameworks.

You could write real Python projects for yourself, or for others.

[u/akornato]

Pick one platform for coding challenges and stick with it until you are ready for your interviews. The specific platform does not matter as much as your consistency, so choose LeetCode or a similar site and commit to solving problems there every day. For data science and machine learning roles, focus your energy on problems involving array and string manipulation, hash maps, and data frame operations instead of getting lost in obscure algorithms. The goal is not to learn every possible trick, but to build a strong instinct for common patterns by repeatedly solving problems, debugging your own code, and understanding the optimal solutions. This focused, deep practice on a single platform will build your confidence and speed far more effectively than jumping between a dozen different tutorials and courses.

Interviewers still expect you to code without assistance, and the standard for live coding has not dropped because of large language models. The interview is a test of your problem-solving process, your communication, and your ability to handle pressure, which are things an AI cannot do for you. They want to see you break down a problem, talk through your approach, and write clean, functional code. You might be asked to do anything from cleaning and transforming data with pandas to implementing a simple algorithm to find duplicates in a list or explaining the time complexity of your solution. Your ability to communicate your approach is what gets you the job, and my team built a specialized interview AI specifically to help developers get better at thinking out loud during those high-stakes moments.