As a budding data scientist or ML/DL engineer, you’ve probably heard the advice: ‘Practice LeetCode to ace your job interviews.’ But is LeetCode-style Dynamic Systems Algorithm (DSA) prep really crucial for roles in data science, machine learning engineering, or deep learning engineering?
I’ve been wondering the same thing, so I decided to dive deeper. From what I’ve gathered, the answer is not a simple yes or no. It depends on the company, the role, and the type of problems you’ll be tackling.
For data science roles, you might not need to be a LeetCode master. Instead, focus on building a strong foundation in statistics, machine learning theory, and domain expertise. You should be comfortable working with datasets, creating models, and communicating insights effectively.
However, for ML/DL engineering roles, LeetCode-style problems can be more relevant. You’ll be working on large-scale systems, optimizing models, and ensuring they’re scalable. In this case, practicing LeetCode can help you develop problem-solving skills, such as breaking down complex problems into manageable parts and writing efficient code.
So, how deep should you go with LeetCode? Focus on the basics, like arrays, hashmaps, and graphs. You don’t need to be an expert in dynamic programming or trees, but having a solid grasp of the fundamentals will help.
Remember, LeetCode is just one part of the equation. Don’t neglect other essential skills, such as system design, model building, and theory. And most importantly, practice solving real-world problems that align with your desired role.
What do you think? Have you had any experiences with LeetCode in your data science or ML/DL engineering journey? Share your thoughts in the comments!