19 Interview Questions that Every Data Scientist Should Know

Like all other domains, the data scientist job demands special skills. And based on those skills, the interviewer can ask you questions.

Many think that interviewer will ask questions straight to the topic. No.

As I thoroughly believe, aspirant data scientists should know the practical data analytics applications and different scenarios. Scenarios revolve around defining data schema, forming the proper structure and then visualizing the data. Visualizing the data is important to make business decisions.

Apart from the technical questions, you can also expect some scenario, application, and behavioral-based questions related to data engineering, visualization, and decision-making.

Here are some of the Data Scientist interview questions you should be aware of. Whether you are a fresher or experienced candidate, you are expected to answer them when an interviewer asks you in the interview.

Most Important Data Science Interview Questions

1. What are the assumptions of the linear model?
2. What is overfitting and how do you fix it?
3. How to handle outliers?
4. What is the effect of having too many features or having too less features?  
5. What is a cross-validation strategy and what are some different cross-validation strategies?
6. Why do you train-test-split and what is the process of train-test split?
7. What is the difference between R square and Adjusted R square?
8. How do you select a model?
9. What is Hyperparameter Tuning?
10. What is Bagging and Boosting?
11. What is the difference between Bias and Variance?
12. What is the difference between error and residual error?

The one primary language used in data science is Python. When it comes to data science, Python is way ahead of any other programming language. In the technical interview round, you can also expect questions related to the Python libraries for Data Science.

An interviewer can also ask you questions on data ethics, critical thinking, and data governance. Every company competing here in the data-driven technology looking to get benefited from the ROI.

13. How do you define value for the business in your work with data?
14. How do you make sure the business gets actionable insights from your work with data?

We all know, mathematics is more important than any other subject in data science. Without math, you can not get into data science. In an interview, the interviewer definitely will ask you questions about mathematics. Knowing and preparing all the mathematics topics from engineering is a really cumbersome task. Instead, here are some of the important math topics you should prepare for a data science interview.

You can also questions from Machine Learning.

15. What are the different phases of an ML life cycle?
16. What are the different machine learning algorithms?
17. How do you frame a business problem into an ML problem?

Here are some of the questions asked in the interview to check the knowledge of the candidate with real-world scenarios.

18. What are the assumptions of linear regression?
19. How do you check that data is fulfilling those and let’s say if it is not satisfying one or more assumptions then what do you understand by that and how do you correct that?

To accomplish the data science task, we use different data science tools like Splunk, Talend, QlikView, Apache Spark, Power BI, KNIME, Rapidminer, Tableau, Microsoft Excel, etc. Based on your experience or project demands, the interviewer can ask you questions related to the different data science tools.

Let me know your experience attending the Data Science interviews in the comment section. If you are preparing for a data scientist role, you can ask your question below. I will try my best to help you.

Best wishes!

Aniruddha Chaudhari

I am a Python enthusiast who loves Linux and Vim. I hold a Master of Computer Science degree from NIT Trichy and have 10 years of experience in the IT industry, focusing on the Software Development Lifecycle from Requirements Gathering, Design, Development to Deployment. I have worked at IBM, Ericsson, and NetApp, and I share my knowledge on CSEstack.org.