Data analyst interview questions: Common questions and answers

Congratulations! You’ve landed an interview for a data analyst position at the perfect company. You’re only inches away from your dream career, and all you need to do to seal the deal is answer a few questions.

Of course, it’s best to practice first. To make sure you’re well-prepared for your upcoming interview, we’ll walk you through some common data analyst interview questions. Read on for a mix of technical and behavioral questions that’ll help refresh your understanding of data analytics’ fundamentals and key principles.

What are the key concepts of data analytics? How have you applied these in working with datasets?

Key concepts and elements of data analytics include:

Big dataDatabase designData miningData segmentationStatistical analysisData cleansingData profilingData validationClusteringCollaborative filteringTime series analysis

Learn the definitions of these terms and concepts and describe how you used them to solve a problem.

Have you solved a data problem by changing processes or tools? In what other ways have you solved data problems?

You may hear this question during a technical interview. Describe ways you debugged code or built or added features to a product. You may want to bring in examples of what you applied using programming languages, coding, data projects, or checking code.

Technical interviews sometimes require candidates to solve a data problem or at least lay out their approach to the problem and explain how they’d find a solution. The interviewer would likely be looking for the candidates’ methods rather than the correct solution.

How do you approach problem solving?

Give your interviewer a glimpse into your mind. Describe any problems you’ve encountered with incomplete or poor quality data, and explain how you cleansed it or filled its gaps.

Provide examples of how you handled new tasks, dealt with challenges, managed multiple responsibilities, overcame disagreements and mistakes, and performed under pressure. You don’t have to explain every challenge you’ve ever faced, but have some real examples on-hand that illustrate your problem-solving ability.

What are your certifications and/or course experience?

Point out any qualifications you’ve earned, especially in key computer languages or systems such as Python. If you’re well-versed in database management or statistics, this is an opportunity to highlight your knowledge of those functions.

You may also want to highlight your familiarity with libraries and frameworks geared toward data science, like SciPy or Seaborn.

If you don’t have many certifications, try explaining how your experiences in past positions relate to your capacity as a data analyst.

Why are you looking to make a career change?

You’ll likely hear this question if you’re switching to a new career in data analytics. In an article for The Muse, Caris Thetford recommends answering this question by explaining how the universal skills you gained from your past experiences have influenced your approach to coding.

This type of question is also one you can answer with a practiced elevator pitch that emphasizes your desire to produce great work as a reason for your career switch.

If you’re uncertain about your experience with or knowledge of data analytics, try addressing how your skills transfer into the specific industry you’re aiming to enter.

Prepare for your data analyst interview

Because of its wide range of applications in data science and analytics, you may also be questioned about your knowledge of Python. Use this list of practice Python interview questions to refamiliarize yourself with the language’s key components and concepts.

If you need a more in-depth refresher of the skills and knowledge you’ll need as a data analyst, check out our Data Analyst Career Path. We’ll cover everything you need to know, help you build a portfolio, and even give you some more tips on interviewing.

Or, if you’re starting fresh, check out our data science career guide. We’ll help you find the right path toward your future career in the field.

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