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Data science interviews test both hard and soft technical skills. Being well prepared with solid answers to frequently asked data science interview questions is key to standing out.
In this blog post, we will learn about 26 data science interview questions you should expect. Questions cover statistics, Python, SQL, machine learning, data analysis, projects, and more. Whether you're a student, career changer, or an experienced data scientist, reviewing these questions can guide your preparation and help you walk into interviews feeling more confident and ready to impress.
1. Explain complex data concepts
Q: Describe a time when you explained a complex data concept to a non-technical person. How did you help them understand?
2. Learn from mistakes
Q: Have you ever made a major mistake in your analysis? Can you explain how you dealt with the situation and what insights you gained from it?
3. Adapt to changing requirements
Q: Can you share an experience working on a project with unclear or constantly changing requirements? How did you adapt to the situation?
4. Anagram Checker
Q: Write a function to check if two strings are anagrams.
5. Find the missing number
Q: Given an array containing n distinct numbers taken from 0 to n, find the missing one.
6. Calculation of Euclidean distance
Q: Write a function to calculate Euclidean distance in Python?
7. JOIN comparison
Q: Can LEFT JOIN and FULL OUTER JOIN produce the same results? Why or why not?
8. Time difference query
Q: Please write SQL queries that can help me find the time difference between two events.
9. Handling NULL in SQL
Q: Can you provide some guidance on how to deal with NULL values when querying a data set?
10. GROUP BY Logic
Q: What happens when you GROUP BY a column that is not in the SELECT statement?
11. Probability of the same suite
Q: What is the probability of drawing two cards (from the same deck) that have the same suit?
12. Elevator probability problem
Q: What is the probability that each of the four people in the elevator will get off on a different floor of the four-story building?
13. Explaining p-value
Q: How would you explain to an engineer how to interpret a p-value?
14. Sample size and margin of error
Q: For sample size n, the margin of error is 3. How many more samples do we need to reduce the margin of error to 0.3?
15. Evaluating the randomness of A/B testing
Q: In an A/B test, how can you check if the assignment to different groups was truly random?
16. Data Analysis Project Approach
Q: What process would you follow while working on a data analysis project?
17. Treatment of outliers
Q: How are outliers in a data set treated?
18. Understanding Data Visualization
Q: Can you explain the data visualization? Also, how many types of visualizations are there?
19. Data validation
Q: What is data validation? And what are the different methods that can be used to validate data?
20. Group performance evaluation
Q: If the labels in a clustering project are known, how would you evaluate the performance of the model?
21. Feature selection methods
Q: What feature selection methods do you use to determine the most relevant variables for a model?
22. Basic concepts of neural networks
Q: Explain the main components that make up a neural network using a simple example.
23. Managing imbalanced data sets
Q: How do you manage an imbalanced data set?
24. Avoid overfitting
Q: How can you avoid overfitting your model?
25. Investigate a drop in user engagement
For this case study, your responsibility is to identify the reason behind the decline in user engagement in the Xfinite project. It is important to first get an overview of the project and then analyze the data from four specific tables.
26. Validation of A/B testing results
Explore A/B test results with significant differences between control and treatment groups to validate or invalidate them through detailed analysis.
Data science interviews test a wide range of skills, from technical to interpersonal. The 26 questions provide a detailed overview of the key topics that aspiring data scientists are likely to encounter during interviews. Being well prepared for these questions will not only help you ace the interview but also provide you with a comprehensive understanding of the practical and theoretical aspects of data science.
Abid Ali Awan (@1abidaliawan) is a certified professional data scientist who loves building machine learning models. Currently, he focuses on content creation and writing technical blogs on data science and machine learning technologies. Abid has a Master's degree in technology Management and a Bachelor's degree in Telecommunications Engineering. His vision is to build an artificial intelligence product using a graph neural network for students struggling with mental illness.
