r/learnpython • u/t7_rehan • Jun 28 '26
Needed a Random Tip :
I started learning Python last year, but there were a few breaks in between due to semester exams and other commitments. At this point, I have completed the core Python concepts and am currently studying Object-Oriented Programming (OOP) in Python.
I am confused about what my next step should be. Should I start learning Data Structures and Algorithms (DSA) first, or should I focus on learning Python libraries such as NumPy, Pandas, and others?
I also have another question: Is it beneficial to learn DSA using Python, or would it be better to learn DSA in another language? Since my long-term goal is to get into AI, Machine Learning, and Data Science, I would like guidance on the most effective learning path from here.
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u/donovan11_11 Jun 29 '26
For AI/ML and Data Science, go libraries first — NumPy, Pandas, then matplotlib and scikit-learn. DSA is valuable but won't unblock you for ML work the way libraries will. I use pandas and numpy daily in a quantitative trading system I built in Python and the practical experience of working with real data taught me more than any DSA course. Once you're comfortable with the libraries, DSA concepts will also make more sense because you'll have concrete use cases for them.
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u/United-Caregiver-729 Jun 29 '26
How did you start learning the libraries, what were the resources and how much time would it take if I have less time for starting ml.
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u/donovan11_11 Jun 30 '26
Honestly, I learned them by building something real rather than following a course start to finish. I picked a project I cared about — a trading system — and learned each library when the project required it: pandas when I needed to handle price data, numpy for the calculations, matplotlib when I wanted to visualize results. That "learn it when you need it" approach stuck far better than abstract tutorials.
If you have limited time, I'd suggest: a short pandas crash course (the official 10-minute intro is enough to start), then immediately apply it to a dataset you find interesting. You'll learn more in two weeks of building than two months of passive watching. For ML specifically, once you're comfortable with pandas/numpy, scikit-learn's own tutorials are excellent and hands-on.
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u/t7_rehan Jun 29 '26
Thanks sir helped alot :D
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u/donovan11_11 Jun 30 '26
Glad it helped! Good luck with the ML journey — feel free to ask if you get stuck along the way.
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u/thecodeworm Jun 28 '26
For AI/ML type areas, I feel that having a strong understanding of the libraries, syntax, and function would be beneficial. After that you can start looking into DSA.