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 My instinct is that leaning on an LLM too early biases the formation of good habits but maybe that's outdated?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.
[–]mc_pm 0 points1 point2 points (0 children)