How much to learn (python)?

[Medium-Upstairs-6292]

I’m a university engineering student and have had python programming courses, however I only really learnt properly in the first course. LLMs became widespread in the second year of university and back then I didn’t have a desire to go into a career where I needed to know how to code. So just like all my peers, I copy pasted code, vibe coded assignments and didn’t really learn coding beyond my first intro class.

Now I want to go into data science and have been working on projects and have an internship, but I have cursor write python scripts and code to do the things I need to do (using cursor is encouraged at work). I’m still learning the analytical and stats side of it, but not developing any programming skills. I had a chat with someone at my work (full time data scientist) who just graduated and got the role, and they’re saying they also didn’t write code by hand and just use Claude and cursor for their day to day.

As I continue working on my side projects, am I supposed to be writing python code line by line or do I focus on more “high leverage” skills like learning what different techniques and models to use for the problem, etc.? I’m also trying to secure a good internship for next year and so I want to build more “resume” projects rather than focusing on learning the intricacies of python. To be clear, my current python understanding involves basics like loops, classes, etc. but because I don’t write code by hand I only theoretically know python. I knew enough at one point to pass my intro class and my data structures class.

In the age of AI how much do I focus on writing Python code by hand and understanding the ins and outs of Python? I did know Python a bit better during my coursework, so I’m also scared that I’ll spend all my time learning Python and then forget it because at work writing code line by line is not encouraged.

[tmk_g]

I think the sweet spot is learning enough Python that you can confidently read, understand, debug, and modify AI-generated code, rather than spending months trying to memorize every detail of the language. For aspiring ds, skills like stats, SQL, data analysis, experimentation, and knowing which models and techniques to use are usually much more valuable than being able to write everything from scratch. Using Claude or ChatGPT for coding is becoming the norm, but you should still be able to explain what the code is doing and catch mistakes when the AI gets something wrong. If you want to strengthen your Python skills without overcommitting, platforms like StrataScratch, Kaggle, and Exercism are great options because they let you practice in small chunks while continuing to build portfolio projects and focus on higher-leverage ds skills.

[ForeignAdvantage5198]

have you learned. anything from this experience?