Episode 127 - Data Visualization Made Simple with Kristen Sosulski
Every day, we see images, charts, graphs, and various pictorial representations explaining data and trends. While strategic use of these tools is immensely useful, it can be misleading and confusing. Now that we're experiencing a pandemic, effective data visualization is important to relay accurate and reliable information to the public. Now more than ever, we need to learn how to tell a story using data visualization.
In today’s episode, NYU professor and data visualization expert Kristen Sosulski tells us about the basic principles of data visualization and her new book, Data Visualization Made Simple: Insights Into Becoming Visual. She discusses some of the common approaches and effective tools to visualizing data, as well as common mistakes and pitfalls.
Tune in to the episode to learn more about the importance of dynamic data visualization!
About Kristen Sosulski
Kristen Sosulski is an Associate Professor of Information, Operations and Management Sciences at New York University's Stern School of Business. She is also the Director for the NYU Stern Learning Science Lab, leading the team to design immersive learning environments for professional business school education.
Kristen is the author of the book Data Visualization Made Simple: Insights Into Becoming Visual.
You may connect with Kristen on Twitter. To know more about her, head over to her website to read her bio.
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Here are three reasons why you should listen to the full episode:
Understand the difference between effective and ineffective data visualization.
Who needs to learn about data visualization?
What's the difference between data visualization and infographics?
Resources
Data Visualization Made Simple: Insights Into Becoming Visual by Kristen Sosulski
Check out Becoming Visual for free resources on data visualization to go along with the book.
Visualizing Data Course at NYU Stern
Coding in R for Data Course at NYU Stern
Debunking Myths about the Third World, a TED Talk by Hans Rosling (2006)
Listening Archive Podcast episode with Aimee Coelho and Charlie Kilner
Related Episodes
Episode 115 on coronavirus models
Episode 15 with Sarah Spagnolo on turning data insights into news stories
Episode 68 on 6 university courses that influenced Max in surprising ways
Episode Highlights
Critiquing Pandemic Visualizations
Max notes that HealthData.org’s projections keep changing. But there are no dates and indications whether the past projections were right or wrong.
They also did iterations of uncertainty bounds, but it’s not intuitive. The visualization for projections on state comparisons are also not effective.
Johns Hopkins University does a good job of aggregating data about the pandemic. However, their map of confirmed COVID-19 cases is not as effective as it was when there were only a few hotspots.
Visualizations are a significant part of the puzzle when you’re drawing inferences and making decisions. Therefore, they have to be effective and accurate in telling a story.
Intersection Between Data Science and Design
The ability to communicate your analysis and insights is an important skill.
Data visualization allows you to present your data as pictorial evidence that people can read and interpret.
Your audience doesn’t have to know much about your data to understand the key insights you want to communicate.
What Drew Kristen to Data Visualization
For Kristen, working with data and producing papers and reports as an undergraduate always involved visuals.
Doing quantitative analysis on film during grad school was the turning point for her. She discovered the power of visualizing data, and it inspired her to be more serious about her practice.
To visualize a film, you look at the elements that lead to creating suspense.
They analyzed a film shot by shot and looked at patterns in camera movements and angles. Then, they assigned values to categorical data, which they were able to interpret on the visualizations.
The Benefits of Data Visualization
Everyone and anyone who works with data would benefit from learning data visualization.
More people are now looking at and sharing data in order to see what changed over time.
The questions you ask drive the data you want to see. This typically concerns current events and the changes that led to it.
Data visualization is an effective tool for revealing patterns and trends.
Common Mistakes in Creating Visualizations
One common mistake is not testing your data graphic. You have to get initial feedback to see if the person reading it will interpret it as you intended.
Just because you create a data graphic doesn’t mean it’s saying anything. It defeats the purpose when it’s only pretty but doesn’t add any real value to your message.
You have to make sure that your data graphic answers a single question.
Many people use their own shapes. Don’t create a visualization with shapes that don't equate to any numbers.
People sometimes mix up infographics with real data graphics. The former may look pretty, but they are not driven by data.
Creating an Effective Data Visualization
You want to create visualizations that work with dynamic data.
Present a picture of what’s happening now and show it in a predictive way.
Showing what-if scenarios allows audiences to understand the concept of uncertainty.
Pointers for Visualizing Geospatial Data
Make sure there is a reason when you’re representing data points on a map. This is to avoid falling into population map problems. Likewise, use accurate maps.
Use the right encoding. Make sure you’re representing values in terms of their tiers. Remember to provide a legend.
When creating maps with bubbles, set larger bubbles in the back and smaller bubbles in front.
Animating for a macro view or by categorical variables is useful in location maps.
Listen to the full episode to discover the most effective visualization tools recommended by Kristen!
Debunking Myths About the Third World
This TED talk by Hans Rosling is an excellent example of effective data visualization. The late Hans Rosling is famous for his moving bubbles chart.
He effectively used bubble charts to tell the economic development in developing countries.
Listen to the full episode to learn how he visualized the distribution of life expectancy and family size in developing countries!
5 Powerful Quotes from This Episode
“Data visualization allows you to show your data as evidence in pictures that people can interpret. And they don't have to know that much about your data in order to understand some of the key insights you're trying to communicate.”
“The data you want to look at isn't going to be like what happened last year. It's going to be what's happening now and what led up to that change.”
“Just because you create the data graphic doesn't mean that it's actually saying anything. A common pitfall is that we create it, it's like this really cool donut chart. But it doesn't say anything, and there's no reason for it.”
“You need to make sure that every visualization or data graphic that you create answers a single question. That's all I ask—a single question.”
“See if the person reading your graphic will have the same takeaway as you intended.”
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To expanding perspectives,
Max
