Visualize This: Statistician Nathan Yau Helps Non-Experts Understand Data
August 21, 2017
Nathan Yau is a statistician best known for being the author of the highly successful FlowingData.com, a blog on visualization, statistics, and information design. Nathan graduated from University of California, Los Angeles, with a doctorate in statistics and created FlowingData.com to catalog his work. His focus is on data visualization and he has turned the blog into a full-time job, using it to make data easy for non-experts to understand.
Speaking to his street creds, Yau gave the commencement speech at the UCLA Statistics graduation this spring, which included the line, “Statistician is the sexy job of the decade, landing itself in lists of top jobs year after year.” It’s a great read with many lessons for aspiring statisticians. We also recommend you read his 2010 post, Think Like a Statistician – Without the Math.
This is Statistics had the opportunity to talk with Nathan about being a statistician and what that means.
What made you decide to become a statistician?
I enjoyed sifting through data from my first year of college. “Introduction to Statistics” was a required course, and I remember a lot of my classmates were having a tough time, but it was fun to me — the ability to extract information from what otherwise might be a meaningless spreadsheet, data file, or database to most.
I also like the applied nature of it. Statistics is a neat vehicle to learn about any topic you want.
What inspired you to start Flowing Data and why did you think it would interest people?
FlowingData was for me at first. I had to study remotely after my second year of graduate school, and FlowingData was a way for me to catalog work that I thought was interesting and to think through visualization concepts. People started visiting and I went with it.
What types of people take your courses? Are they all aspiring statisticians?
It’s a big mix. I think most people veer on the side of wanting to communicate with data, which includes designers, journalists, statisticians, engineers, business analysts, etc. Like I said, I like the applied nature of statistics and visualization, and data is everywhere these days, which means there’s a variety of fields that want to make sense of the numbers.
What fascinates you the most about data visualization and why do
you think it has become so popular?
My Ph.D. work was on personal data collection, specifically how non-professionals could use and understand data in an everyday setting. Visualization is a good way to convey the information. People can see patterns and relationships without having to know statistical theory. Of course, strong statistical foundations will only improve understanding, but there’s a lot that can be gleaned without the background.
Tell us more about using data visualization in your research. Where do you get the ideas for your content?
A lot of it comes from my everyday life. Throughout the day, little curiosities and questions pop into your head. A lot of the time, instead of just stopping with a Google search, I try to answer the questions with data.
How can you see the teaching of statistics and data visualization changing in years to come?
The audience for statistics is much more broad now. It’s becoming more necessary for people who don’t work with data every day to understand how data works to some extent. So you have a lot of casual learners or those in non-statistical fields interested in learning. Luckily, with the web, we can reach those people more easily and those people can access information much more readily. It’s up to statisticians to make sure these people who are eager to learn to get the right information.
What advice would you give to young statisticians or students wondering if they can integrate statistics into their studies in other disciplines?
Stay interested in things outside of statistics and think about how your work applies to the outside world. We’re living in a time when statistics plays a huge role in how people live and the choices they make. It’s important we don’t pass on the responsibility.
In this year’s Fall Data Challenge, Fight Food Insecurity, 76 teams and 139 students submitted their data visualizations and analyses of the current state of food security across the United States. Students recommended a range of creative potential solutions to help provide relief for food scarcity and insecurity. Their statistical analyses of the dataset allowed…
Students, are you ready for the Fall Data Challenge? Gather your team, the challenge opens this Monday, October 11. The deadline for submissions is Sunday, November 7, at 11:59 PM EST. We’ve just released the dataset, so you can take your first look at it now! Here are the resources to start with: Read the ‘Getting…