[D] Could really use some guidance . I'm a 2nd year Data Science UG Student

Reddit r/MachineLearning / 3/28/2026

💬 OpinionIdeas & Deep AnalysisTools & Practical Usage

共有:

Key Points

A second-year Data Science undergraduate asks for guidance on what to study next after completing fundamentals like linear/logistic regression and decision trees, and building comfort with Python, pandas, sklearn, and SQL.
The student feels overwhelmed by the variety of learning resources, including fast.ai, Andrew Ng’s courses, Kaggle competitions, and the idea of building projects.
They specifically mention wanting to begin deeper learning into PyTorch or Keras and note they are currently less knowledgeable about neural networks and NLP.
The core request is for a recommended learning order and advice on which resources are most worth the student’s time.

I'm currently finishing up my second year of a three year Bachelor of Data Science degree. I've got the basics down quite well, linear regression, logistic regression, decision trees, (not knowledgable about neural networks/nlp though) I'm comfortable with Python, pandas, sklearn, and I plan to start learning PyTorch/Keras(whichever might be better). I also know SQL at a decent level.

But I feel a bit lost on what to do next. There's so much material out there and deciding a source to learn from gets confusing. I've seen people mention fast.ai, Andrew Ng's courses, Kaggle competitions, building projects, and I genuinely don't know what order makes sense or what's actually worth my time. Any help is GREATLY appreciated

submitted by /u/Crystalagent47
[link] [comments]