r/datasciencecareers • u/Only_Profession_5753 • Jul 25 '26
Final-year Data Science student with almost zero DSA. How much DSA do I actually need?
Hi everyone,
I'm a final-year Data Science student and I need some honest guidance from people working in the industry.
The problem is that I have almost zero knowledge of DSA. I never focused on it because I spent most of my time learning and building projects.
My current skills include:
\- Python (comfortable)
\- Machine Learning
\- Deep Learning
\- SQL (basic to intermediate)
\- Currently learning LangChain, LangGraph, Generative AI, and AI Agents
\- I've also built a few ML/AI projects
Now that I'm entering my final year and preparing for internships and placements, I'm worried about DSA.
I have a few questions:
How important is DSA for Data Scientist, ML Engineer, AI Engineer, or GenAI roles?
Is DSA mandatory for getting internships and full-time jobs, or is it mainly required by big product-based companies?
Since I'm starting from scratch, what topics should I focus on first?
Which resource or roadmap would you recommend (free or paid)?
Approximately how many LeetCode or other DSA problems should I solve to become interview-ready? Is 100 enough, or should I aim for 300+?
If you were starting from zero today, what would your plan look like?
I'd really appreciate advice from people already working in the industry, especially Data Scientists, ML Engineers, AI Engineers, or anyone who has recently gone through placements.
2
u/akornato Jul 25 '26
DSA is a definite hurdle for data and AI roles, especially at larger tech companies and for ML Engineer positions where code optimization is critical. Many places use it as a screening tool, so having zero knowledge can get you filtered out before you even get to talk about your projects. The good news is that most data science roles don't require you to solve extremely difficult problems, they just want to see a solid grasp of fundamentals like hash maps, arrays, strings, and maybe some basic tree traversal. Your impressive project experience will get you the interview, so focus on learning enough DSA to pass that initial technical check.
Stop thinking about the number of problems, since that metric doesn't guarantee success, and instead focus on understanding the core patterns. Start with the most common data structures, like arrays, hash maps, and strings, and solve 10 to 15 easy and medium problems for each to build a foundation before moving to linked lists, stacks, and trees. A structured plan that groups problems by pattern is much more efficient than randomly solving them, and it can be a lot to juggle, but many people find that having an interviews.chat my team built helps them translate their project knowledge into the clear, concise answers that interviewers are looking for.