50 Data Science Interview Questions to Ace Your Next Interview (+Tips)

Data science interviews often involve a technical component for the interviewer to assess your skills. Even if you are an experienced data scientist, you will still need to prove your abilities to your interviewers. Being prepared for data science interview questions will allow you to confidently showcase your data science skills whilst also demonstrating soft skills.

Data science interviews often involve technical, business and behavioural components, so practicing every type of question will benefit you in your interviews. We're going to go over some example questions and how to answer them, so you can be prepared for your next interview for a data science job.

Data Science Interview - Technical Questions

As data science is a technical field, there will undoubtedly be some technical questions involved. As many roles involve programming skills, you will be assessed on your ability to discuss the use of code and programming in data science. You will need to be able to confidently discuss concepts in coding, data analysis and machine learning, but make sure you read the job description to double check what skills will be examined.

Even if you are experienced, it is still a good idea to practice answering technical questions, so you can make sure you are able to communicate clearly. The questions will vary based on the industry and whether it is an entry-level role or advanced role, so plan your practice accordingly. Here are some practice questions for data science interview questions based on technical skills:

Programming Data Science Interview Questions

Many data scientists use programming in their daily work, so being able to accurately answer questions based on code is essential. Job descriptions for data scientist roles will often mention the types of programming languages needed for the role, so you should reference your job description to narrow down your scope of practice. Many roles will require skills in Python, R and SQL, so here are some practice questions you can use:

1. What is a package in R, and how do you install and load packages?
2. How do you load or input data in R?
3. How do you aggregate data in R? Can you walk me through the steps to do that?
4. What is tuple unpacking in R?
5. What is the purpose of regularization techniques, and how are they used in analysing a data set?
6. What data analysis libraries can be used in Python, and what are some of the most common ones?
7. What are the different flavors of SQL?

Data Analysis and Manipulation Interview Questions

After testing your fundamental knowledge of data science programs, you could also be asked some more topic-based questions. Some of the most common topics are data cleaning, preprocessing, and data manipulation and data visualizations. For data scientists, these should be second nature, and are a fundamental part of starting any data analysis. Even if you know these topics well, you should practice your answers, as some of these topics can be difficult to articulate verbally. Some data analysis questions you can practice with are:

1. What is the importance of cleaning data, and can you talk me through your process for that?
2. Can you discuss some ways you can transform data using data visualization?
3. How can you handle missing data or missing values in a dataset?
4. What is the difference between data analytics and data science? How about the similarities?
5. What is multivariate analysis, and when would it be used?
6. What is the difference between long format data and wide format data?

Machine Learning Data Science Interview Questions

Machine learning is becoming a major topic in data science, and many companies are looking for people confident and experienced in it. Another complex data science topic, practice how you can articulate concepts in machine learning to the interviewer to showcase both your communication skills as well as your technical skills. Some topics in machine learning you might be asked about include machine learning models, machine learning algorithms, evaluating model performance and supervised and unsupervised learning.

1. Can you discuss the difference between supervised learning and unsupervised learning?
2. Compare and contrast these machine learning models: a random forest model and natural language processing.
3. How can you be sure you are not overfitting training data?
4. When would you need to merge multiple decision trees?
5. What is unlabeled data, and how does it differ to unseen data in machine learning?
6. What is cross validation in machine learning, and why is it used in machine learning models?
7. What is the use of machine learning algorithms and AI, for example, in a recurrent neural network or deep learning models?
8. Can you define precision and recall in machine learning, in relation to false positive rates and false negative rates?

Statistical and Mathematical Data Science Interview Questions

Many data science roles will require you to be well versed in statistical models, and fundamental concepts in statistical analysis. Make sure you go over all of your basic statistics knowledge, including basic statistical models, dependent and independent variables, and p values. You can check the job description to see if any specific knowledge or concepts are mentioned. Data science questions will be relating to your understanding of these concepts, as you are expected to have a baseline of knowledge to succeed in the role.

Probability and Statistics Interview Questions

1. Can you describe to me the relationship between the p value and the null hypothesis, and how it relates to type I and type II errors?
2. Can you discuss principle component analysis, and how is it used for exploratory data analysis?
3. What is k fold cross validation, and how does it relate to training or test data?
4. What is univariate analysis, and how does it relate to the probability distribution and feature selection?

Mathematical Foundation Questions

As data science is very mathematics focused, you might also be questioned on your fundamental mathematics knowledge. If you are interviewing for an entry-level role, you should practice ways to describe fundamental concepts such as categorical and continuous variables, linear relationships, and other techniques you would have learned in your data science course. If you are applying for a more advanced role, make sure you revise the techniques used for analysing underlying patterns in complex data.

Data Science Mathematical Questions

1. Can you describe the concept of the normal distribution, and it's function as a statistical model?
2. What is the difference between a linear regression and a logistic regression, and how do they differ in calculation of their data points?
3. What is the false positive rate?
4. What is dimensionality reduction, and what type of data would require it?

Business and Scenario-Based Interview Questions and Answers

As data science roles often require you to work with teams of engineers, business analysts and external clients, you will also be asked business based interview questions. There are a number of business themes you could be interviewed about, so doing some practice outside of data science knowledge is also necessary. We've made some examples of business related interview questions and answers for you to practice with:

Business Acumen

Business acumen relates to your understanding and comprehension of business practices, and being able to act appropriately within a corporate context. Learning more about business operations and strategies will allow you to not only ace your interview, but also gives you an opportunity to become a well-rounded professional as well.

Behavioral and Soft Skill Questions

Although you might feel like you need to brush up on technical skills only and don't need to practice any behavioral questions, every interview will have behavioral and soft skill questions. These questions are used to assess your ability to work with others and communicate. Although a role in data science might not be client-facing, you still need to demonstrate you can effectively work in teams. One good thing about soft skill questions is that they give you a chance to also mention your achievements and experiences that you want to share.

Communication Skills

Being a strong communicator is key in many roles, including in data science. You could be working with people who are not familiar with data analysis or data science, so knowing how to explain concepts to them is key. Likewise, you will also be collaborating with many different people, so being able to communicate across disciplines is also essential. You could be asked about conflict resolution, providing feedback and handling complaints for example.

You should practice how you would describe data concepts to people who are not as familiar with data science as you are, as a way to showcase your communication skills. This is also a likely interview question, for example:

1. How would you describe the random forest model to someone who is not experienced in data analysis?
2. How can you justify using feature selection methods to a board of stakeholders who are not familiar with data analysis?
3. If your team does not comprehend the results of a model, how would you describe the way a data model performs to them?
4. Suppose an employee doesn't know anything relating to data science, how would you describe a classification model to them?
5. Describe the purpose of training data to someone who is not a part of the data analytics team.

Teamwork and Collaboration

As mentioned before, a data scientist works with many different people. Being able to show you can effectively collaborate with others is key in many roles, including in data science. If you can draw from real examples, you can use the STAR technique to mention your experience working in teams. Some practice questions for teamwork are below.

1. How do you delegate tasks when working with teams?
2. What tools do you use to communicate with your team?
3. What role do you usually play in a team?
4. Describe a time where you effectively worked with a diverse team.
5. How do you set goals or deadlines with your team?

Problem-solving and Adaptability

Data science is all about problem solving, so showing the interviewer your problem solving skills is key. You could be given situational questions, where you can also use the STAR method. Some problem solving questions are below.

1. What do you do when you receive conflicting information from teammates?
2. What is your approach to solving problems?
3. Describe a time you effectively solved an issue on your own.
4. Describe a difficult problem you solved with a team in your previous role.
5. When do you know when to escalate an issue?

Leadership and Initiative

As roles in data science involve cross collaboration, you might be the only person working on data analysis on a team, meaning you will need to use initiative and leadership when it comes to working with data. If you are interviewing for a leadership role, you will need to outright discuss your leadership skills. Some questions about leadership and initiative include:

1. How do you motivate your team?
2. How would you get up to speed in your first month of working with us?
3. How do you advocate for your team to management?
4. How do you deliver and receive feedback?
5. When and why would you escalate a problem to a manager?

Tips for Acing a Data Science Interview

Now you have some practice questions to try, here's our advice for acing your interview:

1) Pre-Interview

• Brush Up On Theory and Skills As data science interviews will involve technical questions, you should make sure you review your skills and theory in data science. Make sure you go back over the basics and try to practice how to articulate difficult concepts in mathematics and statistics. You can also try some coding problems and play around with different languages to refresh your mind. You can try using courses, YouTube videos, textbooks or practice problems to brush up on your knowledge.

2) During the Interview

• Communicate Clearly: If you reviewed the theory and brushed up on skills, you should also focus on being able to communicate these concepts effectively. Practice out loud how you would answer problem-solving questions - it can be quite difficult to describe data concepts! When you are asked a problem-solving question, you need to think out loud and talk through your processes and thinking. The interviewer can't read your mind, so make sure you tell them what you're thinking!

If you are asked a difficult question and can't answer it perfectly, don't worry. The point of some problem-solving questions isn't to be correct - you are being tested on how clearly you can communicate your thinking process and justify your reasoning.

3) Post-Interview

If you are interested in the role after the interview, you can thank the interviewer via email for their time. You can reflect on the questions, your strengths, and areas for improvement so you can prepare better for any other interviews you have coming up. Then, relax!

Key Takeaways

Data science interviews are a blend of technical skills and soft skills and will test your fundamental and advanced knowledge in data science. By brushing up on the basics, doing practice questions, and knowing how to answer soft-skill questions, you can impress the interviewer as a well-rounded candidate.

Practicing for data science interviews will involve persistence, but stay confident and consistent in your preparation. Use our bank of interview questions in this article to practice and reflect on your strengths, as well as what needs to be worked on. If you want more advice on interview preparation, check out Cake! Cake has free articles for interview preparation so you can do your best in interviews!

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— Originally Written by Bronte McNamara —