1. How would you handle missing values in a dataset? Provide examples of different techniques you would use for numerical and categorical variables.

2. Can you explain the difference between overfitting and underfitting in machine learning? How would you address these issues?

3. Describe the process of feature selection and explain why it is important in data analysis. Provide examples of different feature selection techniques.

4. How would you assess the performance of a classification model? What metrics would you use and why?

5. Can you explain the concept of regularization in linear regression? How does it help in controlling overfitting?

6. How would you handle imbalanced datasets in classification problems? Describe different techniques you would use to address this issue.

- Describe a time when you had to work with incomplete or messy data. How did you handle it and what steps did you take to clean or analyze the data?

- Can you give an example of a time when you identified a pattern or trend in data that was not initially apparent? How did you go about uncovering this insight and what impact did it have?

- Tell me about a time when you had to present complex data or analysis to a non-technical audience. How did you ensure that your message was clear and understood?

- Describe a situation where you had to make a decision based on data that conflicted with your initial assumptions or beliefs. How did you handle this situation and what was the outcome?

- Can you share an experience where you had to use data to solve a problem or make a decision, but the data was limited or not readily available? How did you approach the situation and what steps did you take to gather the necessary information?

- Tell me about a time when you faced a tight deadline for a data analysis project. How did you manage your time and prioritize tasks to ensure that the project was completed on time?

1. Can you describe a time when you had to work with incomplete or messy data? How did you handle it and what were the results?

2. Have you ever had to present complex data analysis to a non-technical audience? How did you ensure they understood the findings and recommendations?

3. How do you stay up-to-date with the latest trends and advancements in data analysis? Can you provide an example of how this knowledge has influenced your work?

4. Tell me about a project where you had to work collaboratively with cross-functional teams. What challenges did you face and how did you overcome them?

5. Can you give an example of a time when you had to make a data-driven decision that contradicted your initial assumptions or intuition? How did you handle the situation?

6. Describe a situation where you had to deal with conflicting priorities or deadlines in your data analysis work. How did you prioritize and manage your time effectively to meet the requirements?