Why are Data Science freshers struggling with technical rounds lately?

All answers to this question.

The gap is usually "Problem Solving" rather than "Coding." Most freshers can write a script, but they struggle to explain why they chose a specific algorithm for a business case. Companies in 2024 are looking for candidates who can perform Exploratory Data Analysis (EDA) and then translate those findings into business value. You need to practice talking through your logic out loud. Also, make sure your GitHub has end-to-end projects, not just Kaggle notebooks. Showing you can deploy a model using Flask or FastAPI will put you ahead of 90% of other applicants.

   Answered 2025-09-12 by Kimberly Vance

Are you finding that the questions are more focused on LeetCode-style algorithms or actual data manipulation and cleaning?

   Answered 2025-09-14 by Laura Bennett

• Honestly, Laura, it’s a mix of both. However, most rejections happen because freshers can't handle "dirty data." In the real world, data isn't organized like a textbook. Practicing with messy, real-world datasets is the best way to prepare for the technical hurdles recruiters are throwing at us.

     Commented 2025-09-15 by Jason Myers

It's often the lack of fundamental statistics. Everyone wants to do AI, but they forget how to explain a p-value or a confidence interval during the interview.

   Answered 2025-09-16 by Gregory Lane

• Spot on, Gregory. Solidifying those core math concepts is just as important as knowing the latest Python library.

     Commented 2025-09-17 by Justin Walsh