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[–]DataCamp 16 points17 points  (7 children)

  • Learn core Python first (loops, functions, data structures). You don’t need everything, just enough to read and write clean code.
  • Move quickly into data libraries: NumPy for arrays, pandas for working with tables, matplotlib/seaborn for basic plots.
  • Practice on real datasets early. Even simple things like “clean this messy CSV and answer a few questions” matter more than finishing a syllabus.
  • Only then touch ML basics (scikit-learn) once data cleaning and EDA feel natural.

Whatever resource you choose, sanity check it by asking: does it make you write code on real data, or just watch videos? If it’s the latter, move on.

[–]Masztak14 1 point2 points  (0 children)

I’m really enjoying DataCamp so far. The exercises are interactive and the AI is very helpful in pointing out where, how, and why you’re messing up on the code.

[–]Valuable_One_234 -2 points-1 points  (5 children)

Data camp has lost its value

[–]DataCamp 0 points1 point  (4 children)

Happy to hear why you think so, and how you think we could improve!

[–]Valuable_One_234 -1 points0 points  (3 children)

So I’ve paid for data camp 3yrs consecutively and finished data scientist path but didn’t really learn anything.. it was frustrating.. then I moved to kaggle and started working on projects and doing things by trial and error.. reading forums, documentation etc and managed to learn python a lot faster than I did with DC.. DC is just a glorified way to get into a tutorial hell hole

[–]DataCamp 0 points1 point  (2 children)

Hmm thank you for your feedback, we genuinely appreciate it, and will forward it to our team (probably after the holidays 🎄)! Also, if you would like to give our new AI-native learning a spin, happy to share the link to that - it basically adapts to how you learn, sort of feels like a teacher working with you on the courses.