Top Data Analyst Interview Questions and Answers

Last updated by Nitin Grover on Sep 25, 2024 at 06:05 PM | Reading time: 8 minutes

Owing to ever-increasing competition and high hiring standards companies adopt, a data analyst interview can be really difficult to clear. To succeed in the interview, candidates need to prepare for various technical and managerial rounds. Presented below are top data analyst interview questions and answers one can look into as part of interview preparation.

For more in-depth interview preparation training, one can opt for the data analyst and business analyst interview masterclass offered by Interview Kickstart.

Along with covering all basic data analytics-related concepts, the course equips candidates to clear technical rounds, including programming languages and database design techniques.

Thoroughly reviewing these top data analyst interview questions and answers can also aid in interview prep training for FAANG companies.‍

Top Data Analyst Interview Questions and Answers

Below are 20 top data analyst interview questions and answers candidates may be asked when they apply for a relevant position in an organization.

1. What do data analysts do?‍

This is the very first question in the series of top data analyst interview questions and answers.
Since you’ve come for a data analyst role, as a reply don’t start with the definition of what data analysts do. You should instead focus on outlining the main tasks of data analysis that can help businesses take informed decisions.
Provide an outline of the main tasks of identifying, collecting, cleaning, analyzing and interpreting data. Elaborate further if the interviewer asks.‍

Also read: What Does a Data Analyst Do? A Complete Guide

2. What was your most successful/most challenging data analysis project?
Here, the interviewer is not asking for any project-specific experience of yours but your strengths and weaknesses. Align your project experience with how you improved your technical and soft skills while working on that project.
Be honest about your weaknesses and the lessons you learnt while working on the project.
Be specific about the mistakes you made, such as not analyzing the data properly in any past assignment you undertook or not picking up the right information for available sources. Also discuss what corrective measures you took to work on your weaknesses.

4. What is the difference between data mining and data profiling?
To answer this question, you can start by giving the definition of data mining and profiling and then share some unique differences between the two.
Data mining is the process of sorting large data sets to identify patterns and relationships that can help solve business problems.
Data profiling is the process of identifying the potential of source data for a specific use case by reviewing it, understanding its structure, content and interrelationships.
In data mining, raw data is converted into valuable information. One can do so by scraping data from the Internet or filtering the existing data set. However, in data profiling one can not identify the raw data.

5: Define the term data wrangling?
As an answer to this question, reply by defining what data wrangling is, which is:
The process of transforming and organizing data from its raw form into something that is more structured and useful.
Data wrangling involves cleaning, structuring, and enriching the data to enhance its value for analytical purposes. By processing large volumes of data sourced from various origins, data wrangling makes the information more suitable for analysis. ‍

Also read: Mastering Data Wrangling Techniques: Cleaning and Preparing Messy Datasets

6: What are the various steps involved in a data analytics project?
This is one of the most basic interview questions asked for the data analyst role. The various steps involved in any common analytics projects are as follows:
First understand the business problem at hand, define the organizational goals and plan for an appropriate solution.
In the second step, collect the right data from various sources and other information based on your priorities.
The third step requires cleaning the data to remove unwanted, redundant and missing values and make it ready for analytics.
The fourth step involves exploring and analyzing the data. You can do this by using data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze the data. The last step is interpreting the results. You will interpret the results to find out hidden patterns, future trends, and gain insights.

7: Provide information about the technical tools you have used for analysis and presentation purposes?
As a data analyst, you need to have a sound hands-on experience of the tools mentioned below:

SQL: To query, insert, delete and modify records in relational databases
Python: A versatile programming language used to manipulate data
SAS: A software suite for advanced analytics‍
Tableau: For creating reports and dashboards ‍
MS-Powerpoint: For creating presentations for explaining the data analysis results

Also read: Top SQL Interview Questions for Data Analysts

8: What is the largest data set you’ve worked with?
Here the hiring managers want to know about your capability of working on large datasets. Here is the chance for you to provide all the details related to the working experience you have with datasets including the type of data or the variables you’ve worked with.
The experience you show can be based on your present job role or from the boot camps and freelance projects you undertook at any stage of your career.

9: What are the common problems data analysts often encounter?
In reply to this answer, clearly state all the problems you encounter during your work as a data analyst. This will give your prospective employers a clear understanding of the level of experience you have.
A few common problems that a data analyst encounters include:

Issues with the quality of raw data given for mining or vetting
You don’t have a clear understanding of the questions to ask
Poor communication issues between stakeholders and its effect on the final outcome of analysis
Data handling issues and keeping the data secure
When presenting the problems, the best approach is to present your opinion of how the problems could be resolved as well

10: What are descriptive, predictive and prescriptive analytics?
In reply to this question, you may start by giving a basic definition about the three analytics types, followed by your understanding related to the types:

Descriptive analytics provides insights into the past to understand what has happened.
It’s used to understand data and draw conclusions from it. To gauge whether certain assumptions are true, you use various techniques such as hypothesis testing, frequency distribution, regression analysis, and confidence interval estimation.
Predictive analytics is used to understand the future to answer the questions on what could happen. It uses available data to predict the future based on available patterns.
Prescriptive analytics uses advanced processes and tools to predict optimal course of action, going forward. It seeks to answer what to provide a potential solution to the problem.

11. What is your understanding of cleaning data?
For data analysts, this activity occupies a major chunk of their daily work agenda. The potential employer will seek your understanding of data cleaning or data cleansing and what makes it crucial.
Explain what data cleaning is and why it's important for the overall process. Subsequently, explain the steps you would perform to clean the data. Also explain how you would deal with messy data.

12. What are the different types of sampling techniques used by data analysts?
To answer this question, first explain what data sampling is, which is to analyze a subset of data to understand the relevance of a larger dataset.
Now explain the two types of sampling techniques: probability sampling and non-probability sampling.
Now you can provide information about the various sampling methods under these two sampling types:

Probability sampling: Simple random sampling, Systematic sampling, Stratified sampling, and Cluster sampling
Non-probability sampling: Convenience sampling, Quota sampling, Purposive sampling, and Snowball sampling

Also read: Time Series Analysis: Uncovering Trends and Patterns in Temporal Data

13. What are the best techniques for data cleaning?

Explain the various data cleaning techniques you’ve worked with the most prominent ones are listed below:

Remove duplicates: Remove all duplicate entries from all the data you’ve accumulated from various sources
Remove irrelevant data: Remove irrelevant data like html tags, URLs, excessive blank spaces, or anything irrelevant
Standardize capitalization: Ensure that words are capitalized properly for the algorithms to differentiate between them
Convert data type: Assigned correct data types for values. For instance, numbers need to be processed as numerals
Clear formatting: Remove any kind of formatting to ensure the ML algorithms can understand them
Fix errors: Extra punctuation, error codes, inconsistencies in formatting need to be fixed as part of data cleaning
Language translation: Translate everything into the language in which your model is able to interpret
Handle missing values: There should not be any incomplete values in the data you are analyzing.

Also read: Data Preprocessing Techniques: The Foundation of Clean ML Data

14. How can you explain technical results to a non-technical audience?
The interviewer wants to understand how effective you are in communicating your findings to non-technical audiences.
While it’s important to analyze the results properly, it’s equally important to present them to effectively communicate the insights to stakeholders, management, and non-technical co-workers.
You can start by sharing the experiences you’ve had in the past while presenting the data to various types of audiences, including non-technical ones. Even if you don’t have any such experience, you can explain how you would have handled such situations to the interviewer.

15. What data analytics software are you familiar with?
The interviewer wants to know about how proficient you are with various data analysis software as well as a few certifications you have. This will also give a fair idea to the interviewer about your experience level and the training you might require to take your role forward in the organization, if you are selected.
You can share the experience you have with any specific software listed in the job description and how you have used them in different stages of your career thus far.

16. How good are your statistical skills?
The interviewer wants to have an understanding about your knowledge in statistics. Make sure to research a few most commonly used concepts including measure of central tendency, measures of dispersion, mean, median and standard deviation.

17. What is exploratory data analysis?
With this question, the interviewer wants to know about your understanding about exploratory data analysis. You can start by giving the definition of exploratory data analysis. Subsequently, you can explain how you’ve used this data analysis type in any of your previous job roles or how you plan to implement it in the future.

18. What are the important steps in the data validation process?
With this question, the hiring manager wants to know about your understanding about data validation. The steps involved are:

Define Validation Rules and Criteria
Data Profiling
Data Cleaning
Automated Validation
Manual Review
Cross Validation
Error Reporting and Logging
Correction and Revalidation
Documentation and Metadata Management
Final Review and Approval

19. Write a SQL query?
With this question, the hiring manager wants to understand how much knowledge you have about structured query language. As an answer to this question, you can give an example of all the basic operations of SQL including insert, delete, and update.

20. What are the criteria to decide the efficacy of the developed model?
This is the last question in the series of top data analyst interview questions and answers.
With the question, the user wants to know about your decision-making capabilities related to the efficacy of the developed model. Here are the key criteria to consider including Performance Metrics, Generalization Ability, Statistical Significance, Robustness and Stability, Model Complexity and Interpretability, Business Relevance, Consistency and Reproducibility.

Also read: How to Write a Winning Data Analyst Resume?