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Data science interviews test both hard technical skills and soft skills. Being well-prepared with strong answers for commonly asked data science interview questions is key to standing out.

In this blog post, we will learn about 26 data science interview questions that you should expect. The questions cover statistics, Python, SQL, machine learning, data analysis, projects, and more. Whether you’re a student, career changer, or experienced data scientist, reviewing these questions can guide your preparation and help you walk into interviews feeling more confident and ready to impress.

## 1. Explaining 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. Learning from Mistakes

Q: Have you ever made a significant mistake in your analysis? Can you explain how you dealt with the situation, and what insights you gained from it?

## 3. Adapting to Changing Requirements

Q: Can you share an experience of working on a project with unclear or ever-changing requirements? How did you adapt to the situation?

## 4. Anagram Checker

Q: Write a function to check if two strings are anagrams.

## 5. Finding the Missing Number

Q: Given an array containing n distinct numbers taken from 0 to n, find the one that is missing.

## 6. Euclidean Distance Calculation

Q: Write a function to calculate Euclidean distance in Python?

## 7. Comparing JOINs

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 NULLs 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’s not in the SELECT statement?

## 11. Probability of Same Suite

Q: What is the probability of drawing two cards (from the same deck of cards) that have the same suite?

## 12. Elevator Probability Problem

Q: What’s the chance that each of the four people in the elevator gets 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 bring the margin of error down to 0.3?

## 15. Assessing A/B Test Randomness

Q: In an A/B test, how can you check if assignment to the various buckets was truly random?

## 16. Data Analytics Project Approach

Q: What process would you follow while working on a data analytics project?

## 17. Outliers Treatment

Q: How do you treat outliers in a dataset?

## 18. Understanding Data Visualization

Q: Can you provide an explanation of data visualization? Additionally, how many types of visualizations exist?

## 19. Data Validation

Q: What is data validation? And what are the different methods that can be used to validate data?

## 20. Evaluating Clustering Performance

Q: If the labels are known in a clustering project, 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. Neural Networks Basics

Q: Explain the core components that make up a neural network using a simple example.

## 23. Managing Unbalanced Datasets

Q: How do you manage an unbalanced dataset?

## 24. Avoiding Overfitting

Q: How can you avoid overfitting your model?

## 25. Investigating a Drop in User Engagement

For this case study, your responsibility is to identify the reason behind the decrease in user engagement for the Xfinite project. It is important to first get an overview of the project and then analyze data from four specific tables.

## 26. Validating A/B Test Results

Explore the results of an A/B test with significant differences between control and treatment groups to validate or invalidate through detailed analysis.

Data science interviews test a wide range of skills, from the technical to the interpersonal. The 26 questions provide a thorough overview of 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 equip you with a comprehensive understanding of the practical and theoretical aspects of data science.

** Abid Ali Awan** (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.