20 Data Storytelling Interview Questions and Answers

A data story is a narrative that uses data to communicate a message or an idea. Data storytelling is an important skill for data analysts and data scientists, as it allows them to take complex data sets and turn them into easily understandable stories that can be used to inform and persuade others.

If you’re interviewing for a position that involves data storytelling, you’re likely to be asked questions about your experience and approach to data storytelling. In this article, we’ll review some common data storytelling interview questions and how you can answer them.

Data Storytelling Interview Questions and Answers

Here are 20 commonly asked Data Storytelling interview questions and answers to prepare you for your interview:

1. What is data storytelling?

Data storytelling is the process of using data to tell a story. This can be done in a number of ways, but the goal is always to use data to communicate a message in an interesting and engaging way. This can be done through visualizations, infographics, or simply by writing a story that uses data to illustrate a point.

2. How do you define a story in the context of data science?

A story in the context of data science is a way of conveying data-driven insights in a way that is both informative and engaging. A good data story will make use of both data visualizations and narrative storytelling to communicate its message.

3. Can you explain what narrative visualization means?

Narrative visualization is a way of representing data through visuals in order to tell a story. This can be done in a number of ways, but often includes using charts, graphs, and other visual aids to help explain the data in a way that is easy for the audience to understand.

4. Can you give me some examples of how stories are used to convey information?

Stories are often used to communicate information in a more engaging and memorable way than simply presenting data alone. For example, a data story might be used to explain how a new product is selling in different markets, or to communicate the results of a customer satisfaction survey. Stories can also be used to illustrate trends over time, or to highlight unusual data points that might be otherwise overlooked.

5. Why is it important for data scientists to focus on storytelling and presentation skills?

Data scientists need to focus on storytelling and presentation skills in order to be able to effectively communicate their findings to those who need to make decisions based on that data. Data by itself is not particularly useful unless it can be effectively communicated and interpreted. Data scientists who are able to tell a story with their data and present it in a way that is easy to understand are much more likely to have their work used in a meaningful way.

6. What are some ways that data scientists can improve their data storytelling skills?

Data scientists can improve their data storytelling skills by focusing on creating narratives that are clear, concise, and easy to follow. Additionally, data scientists should focus on using data visualizations to help tell their stories, as this can help make complex data more understandable. Finally, data scientists should practice telling their stories to others, as this can help them to better understand how to communicate their findings effectively.

7. What types of visualizations are best suited for different kinds of problems or datasets?

When it comes to data visualization, there is no one-size-fits-all solution. The best visualization for a given problem or dataset will depend on a number of factors, including the nature of the data, the purpose of the visualization, and the audience. Some of the most common visualization types include bar charts, line graphs, scatter plots, and heat maps.

8. What are some techniques that can be used to make infographics more effective?

Some techniques that can be used to make infographics more effective include using clear and concise text, using strong visuals to convey data points, and using an easily understandable layout.

9. Can you explain what the term “Data Journalism” means?

Data Journalism is the process of using data to create stories that inform the public. This can include everything from using data to investigate a story, to using data to create visualizations that help explain complex issues.

10. What’s the difference between data journalism and data storytelling?

Data journalism is the process of using data to report on a news story, while data storytelling is the process of using data to tell a story. Data journalism often relies on data visualization to help readers understand the data, while data storytelling may use any number of storytelling techniques to help the data come alive for the reader.

11. How does data journalism differ from traditional journalism?

Data journalism is a relatively new field that is still evolving. In general, data journalism relies heavily on data visualization and analysis to tell stories. Traditional journalism, on the other hand, relies more on narrative storytelling. Data journalism often requires a higher level of technical skills, as journalists need to be able to understand and work with data.

12. Is there any relationship between data journalism and traditional journalism? If yes, then what are they?

Data journalism and traditional journalism share some commonalities, but they also have some key differences. Both types of journalism rely on accurate and reliable information, but data journalism relies heavily on data interpretation and analysis, while traditional journalism relies more on narrative storytelling. Additionally, data journalism often uses data visualizations to help readers understand the data, while traditional journalism typically relies on written descriptions.

13. What role does data journalism play in modern society?

Data journalism is a relatively new field that is growing in popularity due to the increasing availability of data and the need for journalists to be able to understand and interpret it. Data journalism can be used to investigate and report on a wide variety of topics, from government spending to crime rates to the spread of disease. In a world where data is becoming increasingly important, data journalists play a vital role in helping the public to understand and make sense of it.

14. What is your opinion about the future of data journalism?

I believe that data journalism will continue to grow in popularity, as it is a very effective way to communicate complex information in an easily digestible format. I also think that data journalism will become more interactive, as readers will want to be able to explore the data for themselves and see how it affects them personally.

15. Do you think that data journalists should have experience with machine learning and AI technologies?

I think that data journalists should have at least a basic understanding of machine learning and AI technologies, as these are becoming increasingly important tools for analyzing and understanding data. However, I don’t think that data journalists need to be experts in these fields – there are other people who can provide that expertise. I think the most important thing for data journalists is to be able to effectively communicate the findings of their data analysis to a non-technical audience.

16. What is the most important skill needed by a data journalist?

The most important skill needed by a data journalist is the ability to effectively communicate data. This means being able to take complex data sets and distill them down into a form that is easily understandable by the average reader. Data journalists also need to be able to tell a story with their data, and to do so in a way that is engaging and interesting.

17. What are some common challenges faced by data scientists when telling stories using data?

Some common challenges faced by data scientists when telling stories using data include making sure that the data is accurate and relevant, finding the right way to present the data so that it is understandable, and making sure that the story is interesting and engaging.

18. What are some good sources where I can learn more about data storytelling?

There are a few great sources where you can learn more about data storytelling. I would recommend starting with the Harvard Business Review article, “The Art of Data Storytelling” by Randy Olson. You can also check out the book, “Storytelling with Data” by Cole Nussbaumer Knaflic, or the blog, “Data Stories” by Enrico Bertini and Moritz Stefaner.

19. Where do you find inspiration when creating new data visualizations or stories?

I find inspiration for data visualizations and stories from a variety of sources. I might see something interesting in another visualization that I want to try to recreate or improve upon. I might read an article or hear a news story that piques my interest and makes me want to learn more about a particular topic. I might also simply be exploring data and come across something that I find fascinating and want to share with others.

20. As a data scientist, why would you want to use an infographic instead of a simple chart or table?

An infographic can be a more visually appealing and engaging way to present data, especially if the data is complex or has a lot of different elements. An infographic can also help to tell a story or convey a message in a way that is more memorable than a simple chart or table.