Data Science Interviews typically assess a mix of analytical thinking, technical foundations, and real-world problem-solving. Interview formats can vary widely across companies and roles, and questions may span exploratory data analysis, statistics, machine learning concepts, SQL queries, or scenario-based case studies. Some interviews are entirely resume-based, focusing on your fundamentals and deep follow-up questions about your past projects and experience. Others may ask you to open a Colab or notebook environment and build a traditional machine learning model from scratch.
A strong understanding of evaluation metrics, validation strategies, data preprocessing, and deployment considerations is just as important as model selection itself. Clearly state your assumptions, ask clarifying questions, and explain why you choose certain methods over others.
Preparing for Data Science interviews can feel daunting; but this guide is our attempt to help you navigate the process with more clarity and confidence.
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