Technical Skills Machine Learning: Explain a machine learning project you’ve worked on. What challenges did you face and how did you overcome them? How do you handle overfitting in a machine learning model? Describe the differences between supervised, unsupervised, and reinforcement learning. Provide examples of each. Data Analysis: How do you approach exploratory data analysis (EDA)? Explain a situation where you had to clean and preprocess a large dataset. What techniques did you use? How do you decide which features to include in your model? Programming: Which programming languages do you use most often, and why? Can you walk me through your code for a recent data science project? What were the key challenges? How would you optimize code for handling large datasets? Statistics: How do you apply statistical tests to validate your models? Explain the concept of p-value and its significance in hypothesis testing. What is the Central Limit Theorem, and why is it important in data science? Tools and Libraries: Which data science tools and libraries are you most proficient in? Why do you prefer them? How do you manage and deploy machine learning models in production? Have you worked with big data tools like Hadoop or Spark? Describe your experience. Problem-Solving and Case Studies Describe a complex problem you solved with data science. How did you approach it?