10 Commonly Asked Data Visualization Interview Questions and How to Answer Them

Data visualization is a way to present information in a visual form for different types of projects. Learn more about data visualization and potential job roles in the field as well as common questions interviewers may ask during a job interview.

Read more: 5 Types of Data Visualization

What is data visualization?

Data visualization is a way to present information graphically using analytics tools to organize different types of data on various topics. The tools allow you to organize, manage, and analyze large amounts of data concisely to help clients and co-workers better understand a particular topic. Data visualization applications empower you to move project-based decisions forward using insights gleaned from data.

Read more: Data Visualization: Definition, Benefits, and Examples

Careers and salaries in data visualization

Consider these potential job roles that require an understanding of data visualization as part of the requirements for the position. Explore how much you can earn with a data visualization salary, too.

Data visualization specialist

Average annual base salary (US): $75,781 [1]

A data visualization specialist is responsible for creating compelling graphics for clients or co-workers to present data in a visually appealing and easy-to-understand way. You’re responsible for making complex data issues digestible and understanding how to create visuals that adhere to the analysis that a client or co-worker requires.

Business analyst

Average annual base salary (US): $86,068 [2]

A business analyst collects and reviews data for specific business projects. You’re responsible for supporting or leading projects as well as coordinating projects with other teams to analyze issues and present information about your analysis. Data visualization may not be your primary focus, but it can be an important tool as part of your role as a business analyst.

Read more: Data Analyst vs. Business Analyst: What’s the Difference?

Data scientist

Average annual base salary (US): $113,464 [3]

A data scientist is responsible for using tools to collect and analyze data. You may have to create and test different models using data that’s available to you and make recommendations based on your data analysis. Your position could require you to use data visualization software to help you analyze data and present your findings to clients or others in your organization.

Read more: What Is a Data Scientist? Salary, Skills, and How to Become One

10 data visualization interview questions

You will likely encounter questions during your job interview process that test your knowledge and expertise in data visualization tools. Here are some standard questions you can encounter and guidance on how to answer them.

1. What are the different filters used in data visualization?

What they’re asking: Do you understand basic data visualization concepts?

Data visualization tools allow you to filter information and create customized graphics that convey specific facts for your projects. You can include or exclude particular data points or sources, use specific headers to label data, filter data by date, or choose certain dimensions. You can also use calculations from your data sources to filter choices based on calculations using the datasets you've included for particular projects.

Other forms this question might take:

• What filters would you apply to specific cases?

• Are there specific filters you think work best for certain projects?

2. How is a calculated field created?

What they’re asking: Do you know how to build a data set?

A table calculation is a specific field in your data visualization software of choice that uses data in your file. The process allows you to make calculations across a row or a column of data as well as create more involved calculations that use multiple columns and rows based on your specific parameters.

Other forms this question might take:

• Explain your process for calculating data fields.

• Can you walk through how you create a calculated field?

3. What are tree maps? How do they differ from heat maps?

What they’re really asking: Can you make decisions about how to process data?

This question tests your knowledge of different types of data visualization you can encounter in a job using these types of tools. Data visualization allows you to display data in a way that best suits your data sets using important tools to visualize it, such as treemaps and heatmaps.

• Treemaps use nested rectangles that vary in size and color to help illustrate the size and ratios of data points compared to one another.

• Heatmaps use colors to help differentiate data points within a data set.

Other forms this question might take:

• Discuss which types of mapping you would use for this project.

• Do you have a preferred type of mapping for projects, and why?

4. Explain what a parameter is in data visualization.

What they’re asking: Do you understand the limits of data visualization?

Data visualization parameters allow you to set specific variables to filter data and get the desired results by cutting out data that may not be part of your analysis. You may create parameters based on a constant value or create more dynamic parameters using a list of variables that can change and adapt each time you modify data.

Other forms this question might take:

• Can you describe different parameter issues you’ve encountered on a project?

• What kind of data parameters have you put in place for projects?

5. What are the differences between data visualization programs?

What they’re asking: Can you determine which tools are best for projects assigned to you?

Different data visualization apps provide users with specific features for different needs. Consider factors such as how a particular tool organizes data into visual representations to see if it works for your projects. You’ll also want to think about which tools are best for business data or if a particular tool integrates well with the current systems used by the organization.

Other forms this question might take:

• Do you have experience with a specific data visualization program, and what features do you like about it?

• Do you have formal training or certificates in specific data visualization programs?

6. What are the different data types supported in data visualization?

What they’re asking: Do you understand the different tools available to you for a project?

Data visualization tools categorize data into different data types that you can use. For example, data types in Tableau include:

• Text or string values

• Geographic values

• Numerical values

• Cluster group values

• Date values

• Boolean values

• Date and time values

Other forms this question might take:

• What data types would you use for projects assigned to this position?

• What data types do you feel most comfortable working with?

7. Explain the differences between blending and joining data.

What they’re asking: Can you talk about technical data issues in an understandable way?

One difference between data blending and data joining is where your data sources are coming from. You'll want to use data blending if you have data from different sources with common data points. On the other hand, data joining uses one source for data and joins specific data points in that one source.

Other forms this question might take:

• Can you describe issues that arise from bringing different types of data together?

• How have you solved issues when trying to blend or join data?

8. Explain the differences between discrete and continuous data roles.

What they’re asking: Do you know how to present information to clients?

Discrete and continuous data determine how data points appear as part of a graphic in data visualization software. Discrete data has finite values and can be visualized with specific graphics like a bar graph. In contrast, continuous data can be measured on an infinite scale and is visualized with a continuous field, such as a line graph.

Other forms this question might take:

• How do you determine the best option to visualize data for a particular project?

• What questions do you ask clients when deciding how to visualize data?

9. What are the different joins?

What they’re asking: Can you handle multiple data sources?

Data joins allow you to connect data with a common variable. You have four options for joining data from different data points within a source: inner, left, right, and full outer. Each of these four data joins has its own parameters, which can be helpful when organizing data depending on how you want to combine data points.

Other forms this question might take:

• Can you explain the differences among data joins in visualization programs?

• How have you handled issues with joining data in visualization programs?

10. What are filters?

What they’re asking: Do you know how to apply visualization tools to data sets?

Filters reduce the amount of data visualized and show users the specific information they need for a particular project. For example, you can create a range filter to only include data in a certain numerical range or a date filter to only include information for a time frame you specify in the data visualization tool.

Other forms this question might take:

• How do you determine the best ways to filter data for a project?

• Which filters do you find are the most useful for certain data sets?

Getting started with Coursera

Data visualization can be an important skill to add to your resume or a necessary tool for a particular job. You have options, including online courses, to learn more about data visualization and improve your skills.

Check out Data Visualization with the University of Illinois on Coursera to learn job skills using data visualization and build an understanding of different tools. You can also try Data Visualization With Tableau Specialization through UC Davis on Coursera. The course allows you to explore various Tableau features and how to combine data to present a story. Upon completing either program, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.