35 Data Analyst Interview Questions (With Sample Answers)

By Indeed Editorial Team

Updated 17 October 2022

Published 23 August 2021

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

If you have an interest in structuring, analysing and interpreting data, you might want to consider a career as a data analyst. Most interviews for this job role require you to answer both technical and behavioural questions related to data analysis, Microsoft Excel and databases. Preparing answers for those questions can help you showcase your qualifications and skills to get the desired job. In this article, we list 35 common data analyst interview questions and include 10 sample answers for reference.

Related: 10 Valuable Data Analysis Skills

What Skills Do You Need To Crack A Data Analyst Interview?

To prepare for your upcoming data analyst interview, you should focus on improving the following skills:

  • Proficiency in a programming language like Python and R

  • Expertise in SQL and databases

  • Advanced Microsoft Excel skills

  • Knowledge of statistics

  • Ability to handle large data sets

Related: Excel Interview Questions (With Example Answers)

25 Common Data Analyst Interview Questions

Here are 25 interview questions commonly asked in a data analyst interview:

  1. Can you create an excel Macro to find the average of a data set?

  2. Give details about the data analytics tools you used in your last job. Which was your favourite and why?

  3. What do you enjoy most about data analytics? Is there something you dislike?

  4. Have you ever used a Pareto chart? If so, for what purpose did you use such charts?

  5. Outline the difference between data mining and data analysis.

  6. How do you test whether a developed data model is good or not?

  7. In your previous job, what problems did you encounter while performing data analysis?

  8. Explain the procedure of highlighting cells with negative values in Excel.

  9. Explain the difference between variance and covariance.

  10. What is normalisation? Explain the different types.

  11. Explain the difference between data and big data. Do you have practical experience working with big data?

  12. When analysing data sets, explain the process of using logistic regression.

  13. How and why do you use a correlogram analysis?

  14. How would you filter or subset data in SQL?

  15. What are your data analytics strengths and weaknesses?

  16. What is a hierarchical clustering algorithm? Explain its important properties.

  17. Are causation and correlation related? If so, how?

  18. Explain two types of imputation techniques.

  19. When would you retrain a model?

  20. Have you ever run an analysis on the wrong set of data? How did you discover your mistake?

  21. What scripting languages have you used in your previous data analyst job? Which is your favourite?

  22. Explain your experience in creating dashboards.

  23. How to handle multi-source problems?

  24. Differentiate between univariate and bivariate analysis.

  25. How can you avoid hash table collision?

10 Interview Questions For A Data Analyst With Sample Answers

Take inspiration from the following interview questions and sample answers when preparing for an upcoming interview:

1. What are the top three skills for a data analyst?

This question lets an interviewer assess if the candidate understands the skills required to excel as a data analyst. It also helps them understand the areas where you may require training. So, when answering this question, align your answer with your experience. Use the STAR interview technique for an effective response.

Example: “The three most important skills for a data analyst are database knowledge, critical thinking and data visualisation. In my previous job, I was tasked to retrieve old employee data and figure out how the age of employees relates to their engagement and productivity at the workplace. I used my database knowledge and created pivot tables and graphs to represent the relationship between the age of employees and their productivity levels. The result was effective, as based on the analysis, my company decided to hire older employees for their top roles.”

2. How do you manage messy and unorganised data?

As a data analyst professional, not all data that you get comes organised. You may come across a dataset with inconsistencies and a lack of structure, making it difficult to analyse. Through this question, the interviewer wants to understand whether you can overcome such common problems. For an effective answer, explain your experience with handling and managing unorganised data.

Example: “The steps I take for organising and cleaning the messy data depends on the business problem. Typically, the first step is to understand the issue and find a solution. Solving a common problem in the data set usually provides me with a strong starting point. I then sort the data based on the variables, attributes and remove duplicates to ensure accuracy in my analysis.”

3. How would you estimate how many cakes a famous bakery can sell in Indore during May?

Such questions typically test your analytical thought process without the help of computers and a data set. Interviewers prefer candidates with critical thinking skills as it helps them identify connections and relations between variables that are not always clear. For an effective answer, communicate your thought process and the ability to identify segments and variables.

Example: “First, I would collect data about the population of Indore, find out the number of bakeries in the city and the average footfall of people in bakeries each month. Next, I would try to find out the total number of anniversaries and birthdays during May from the municipality office. I would also figure out if there are special days like Friendship Day, Mother's Day or Father's Day in May. Based on the data collected and conducting analysis on it, I would estimate how many cakes the bakery can potentially sell in May.”

4. You have to find the sales target data from the past two months to understand whether the recent promotions enhanced the productivity of employees of an organisation. Would the data require any data cleaning to conduct analysis?

Data cleaning is an essential concept in data analysis because it helps avoid inaccurate analysis. This question helps an interviewer gauge your abilities to isolate and clean the data.

Example: “When retrieving past information from the dataset, I make sure to keep an eye on the data, as not all the data entered would be in the same format. Some employees may add the sales target as a percentage. Others may write in monetary figures or write a ‘yes' or ‘no'. This means I have to figure out the inconsistency in data and change the sales target figure to a single format. I need to clean the data to perform accurate analysis.”

5. Why do you want to become a data analyst?

This question helps in understanding your motivation and reason for choosing a data analysis career. So, carefully explain your interests and passion in this field.

Example: “Data analytics is a fast-paced career requiring a professional to think and solve new business problems critically. Since my childhood, I am extremely good with numbers. Also, I believe every data has a story to tell and a problem to solve, which I find fascinating. I chose this role because it encompasses skills I am good at. I find collecting, organising and analysing data interesting.”

6. What is time series analysis (TSA) and where can you use it?

With this question, the interviewer wants to gauge your background knowledge of some basic methods used for analysis.

Example: “TSA is a type of statistical analysis that deals with trend analysis and time-series data. It is useful for understanding how an asset or variable changes over time. TSA involves data at particular intervals of time or set period. You can use TSA for astronomy, weather forecasting, signal processing, earthquake prediction and applied science.”

7. Name different hypothesis testing you used in your last job.

Such questions test your statistical knowledge because hypothesis helps carry out in-depth data analysis. A good answer reflects that you know the different types of hypothesis testing.

Example: “In my previous job, when I had to compare three or more samples using a single test, I used analysis of variance (ANOVA). But when population parameters were unknown and I had to compare the mean of two samples, I used the T-test. I also used the Chi-Square test to compare categorical variables.”

8. How is data profiling different from data mining?

Comparing two concepts helps an interviewer gauge your knowledge of both concepts. So, compare and contrast data profiling and data mining.

Example: “Data profiling is the process of analysing individual attributes of data whereas data mining refers to analysing data to find relations that were not discovered earlier. The former occurs at an instance and provides insights into the quality of each instance. The latter looks for uniformity, relations and dependencies.”

9. In Microsoft Excel, how can you treat a numeric value as text?

This question tests your understanding of scripting software like Microsoft Excel. It is an important tool for data analysts, as it helps create clear and polished charts that aid in data visualisation.

Example: "For treating a numeric value as text in Excel, precede the numeric value with an apostrophe (') symbol. For example, '2340, Excel would consider 2340 as a text and would skip it during the analysis and calculation."

10. What is collaborative filtering?

This question tests your basic understanding of data analysis. Begin by defining collaborative filtering and provide an example.

Example: "It is an algorithm used for creating an algorithm system based on user-behavioural data. A good example is the 'recommended for you' statements on online shopping sites."


Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.


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