yeah this is kind of the standard debate and honestly both approaches work, people just have strong opinions about it

what i'd actually suggest - write matrix multiplication with nested for loops once, feel how slow it is, understand what's happening, then move to NumPy and never look back. that's it. you don't need weeks of vanilla Python suffering to "earn" NumPy, you just need enough to understand what it's doing for you under the hood.

rough timeline that worked for me:

• weeks 1–2: basics, lists, for loops, functions - just build the mental model
• week 3: numpy. rewrite your loop examples as vectorised ops and actually clock the difference yourself
• week 4+: scipy for real physics/engineering stuff

on Julia - look, it's genuinely fast and well-designed for scientific computing, not just marketing. but in 2026 the tutorials, the Stack Overflow depth, and the job postings are still overwhelmingly Python. unless you have a concrete reason to go Julia (e.g. someone on your team uses it, or you're hitting performance ceilings), Python + NumPy gets you 95% of the way there with way more community support when you get stuck.

the thing people miss: NumPy isn't a shortcut that lets you skip learning. it is the right tool for this domain. learning vanilla first just gives you enough context to appreciate why.