Should I Focus on Data Structures and Algorithms or Start Learning Machine Learning?

Absolutely prioritize DSA at this stage in your studies. It will form the backbone of your technique and is what most internships will test you on. That said, you don’t have to completely ignore machine learning. You can take it at a slower pace and learn concepts like NumPy and Pandas or even start looking at simple machine learning cases and projects to bridge the gap. Your third and fourth years will be great for diving deeper into ML if you find you enjoy it!

If you're aiming for a career in data science or machine learning, be prepared for a long journey—it can even require a PhD! Focus on building a solid foundation now, and keep your options open for later specialization as you advance through your studies. Balance is key, so find a study pattern that works for you now and adapt as you learn more.

You should definitely focus on DSA first. Solid problem-solving skills really pay off in internships and placement exams, and using platforms like LeetCode or Codeforces is a great way to build those skills. Once you have a good grasp on the basics, diving into machine learning will feel much easier. You can also start exploring fundamentals like NumPy and Pandas gradually, so you won’t feel overwhelmed.

I agree—DSA should be your main focus for the first couple of years. It’s the key to developing core thinking skills necessary in computer science. Keep doing practice on Striver with LeetCode or Codeforces. In the meantime, consider dabbling with the basics of ML on the side; Andrew Ng's course is a good start, and learning small concepts daily can keep you engaged without rushing. Once you're more seasoned, you can delve deeper into ML.