Top 10 Data Analyst Interview Questions and Answers

If you’ve got an upcoming interview for a data analyst role, congratulations! Working in the exciting world of data can mean tremendous growth and opportunities. Whether you’re new to the field of data analysis or you have some experience, it’s important to know that the data industry can be a competitive one, and qualified candidates who nail their interviews can find themselves in a lucrative position. In fact, Glassdoor reports that the average base salary for a data analyst in the United States is $67,377 annually. With the average annual median income in the United States being $31,099, it’s easy to see why more and more professionals are recognizing the value of training for a career in data analysis

To better equip yourself for any interview, it’s always a good idea to be ready for different types of questions by having the right answers prepared. This doesn’t mean sounding rehearsed or scripted, but instead, just having a good idea of how to respond so that you don’t find yourself at a loss for words. Sometimes, these questions are meant to catch you off-guard, so preparing ahead of time can help you to avoid these potential setbacks. 

More importantly, you'll want to be prepared for the type of questions that are geared specifically for the role and industry you’re interviewing for. Although it can be impossible to predict all of the questions you’ll be asked, we’re here to help by presenting some of the most commonly asked data analyst interview questions, along with suggested responses.

Question 1: What are some issues that data analysts typically come across?

All jobs have their challenges, and your interviewer not only wants to test your knowledge on these common issues, but also know that you can easily find the right solutions when available. In your answer, you can address some common issues, such as having a data file that’s poorly formatted, or having incomplete data. 

Question 2: What are the main responsibilities of a data analyst?

It is important to be able to clearly define the role you’re interviewing for. Some of the different responsibilities of a data analyst you can use in your response include: analyzing all information related to data, creating business reports with data, and identifying areas that need improvement.

Question 3: When analyzing data, what are some of the statistical methods used?

There are quite a few answers you can give to this question, so be prepared to answer without much hesitation. Some of the examples you should give to your interviewer include simplex algorithm, Markov process, and bayesian method. 

Question 4: What does the standard data analysis process look like?

A: If you’re interviewing for a data analyst job, it’s likely you’ll be asked this question and it’s one that your interviewer will expect that you can easily answer, so be prepared. Be sure to go into detail, and list and describe the different steps of a typical data analyst process. These steps include data exploration, data preparation, data modeling, validation, and implementation of the model and tracking.  

Question 5: How does data analysis differ from data mining?

As a professional data analyst, you should be able to easily identify what sets data mining apart from data analysis. Use a few key examples in your answer: for instance, you can explain that data analysts must create their own equations based on a hypothesis, but when it comes to data mining, algorithms automatically develop these equations. You may also want to mention that the data analysis process begins with a hypothesis, but data mining does not. 

Question 6: What two steps are performed during the data validation process?

You should easily be able to demonstrate to your interviewer that you know and understand these steps, so be prepared for this question if you are asked. Be sure to not only answer with the two different steps—data validation and data verification—but also how they are performed. 

Question 7: What is interquartile range?

Shown in a box plot, the interquartile range is the difference between the lower and upper quartile, and is a measure of dispersion of data. If you’re interviewing for a data analyst job, it’s important to be prepared with a similar answer and to answer confidently. 

Question 8: What is outlier?

Another must-know term for any data analyst, the outlier (whether multivariate or univariate) refers to a distant value that deviates from a sample’s pattern. 

Question 9: Why do you want to be a data analyst?

If you already have experience as a data analyst, this can be easier to answer: simply explain why you love working as a data analyst and why you want to continue. As a new data analyst, this question can catch you off-guard, but be prepared with an honest answer as to why you want to work in this industry. For example, you can say that you enjoy working with data and it has always fascinated you. 

Question 10: In your opinion, what skills and qualities should a successful data analyst have?

There is no right or wrong answer to this question necessarily, but it’s good to be prepared for the possibility of this question coming up. Being an analytical thinker and good problem solver are two examples of answers you could use for this type of question. 

As mentioned earlier, these data analyst interview questions are just sample questions which may or may not be asked in a data analyst interview, and it would largely vary based on the skillsets and the experience level the interviewer would be looking for. So, you need to be prepared for all kind of questions on the related topics including probability and statistics, regression and correlation, Python, R and SAS programming and more.

Whether you’re new at data analysis or you’re looking to further your training, Simplilearn has a variety of courses and programs available to suit your needs and goals. Two popular choices include our Business Analytics Expert Master’s Program and our Business Analytics Certification Training with Excel. We also offer specialized training for those looking to learn more about a specific aspect of data analysis, such as our Python for Data Science Certification Training Course, Data Science Certification Training - R Programming Course, and Data Science with SAS Certification Training. Enroll in one of our highly accredited programs today and get a jumpstart on your career!

About the Author

Shivam AroraShivam Arora

Shivam Arora is a Senior Product Manager at Simplilearn. Passionate about driving product growth, Shivam has managed key AI and IOT based products across different business functions. He has 6+ years of product experience with a Masters in Marketing and Business Analytics.

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