Nathan Yau
Nathan Yau is a graduate student in statistics at UCLA and the author of the extremely popular data visualization blog flowingdata.com. He recently published a book Visualize This-a really nice guide to modern data visualization using R, Illustrator and Javascript - which should be on the bookshelf of any statistician working on data visualization.
Do you consider yourself a statistician/data scientist/or something else?
Statistician. I feel like statisticians can call them data scientists, but not the other way around. Although with data scientists there’s an implied knowledge of programming, which statisticians need to get better at.
Who have been good mentors to you and what qualities have been most helpful for you?
I’m visualization-focused, and I really got into the area during a summer internship at The New York Times. Before that, I mostly made graphs in R for reports. I learned a lot about telling stories with data and presenting data to a general audience, and that has stuck with me ever since.
Similarly, my adviser Mark Hansen has showed me how data is more free-flowing and intertwined with everything. It’s hard to describe. I mean coming into graduate school, I thought in terms of datasets and databases, but now I see it as something more organic. I think that helps me see what the data is about more clearly.
How did you get into statistics/data visualization?
In undergrad, an introduction to statistics (for engineering) actually pulled me in. The professor taught with so much energy, and the material sort of clicked with me. My friends who were also taking the course complained and had trouble with it, but I wanted more for some reason. I eventually switched from electrical engineering to statistics.I got into visualization during my first year in grad school. My adviser gave a presentation on visualization, but from a media arts perspective rather than a charts-and-graphs-in-R-Tufte point of view. I went home after that class, googled visualization and that was that.
Why do you think there has been an explosion of interest in data visualization?
The Web is a really visual place, so it’s easy for good visualization to spread. It’s also easier for a general audience to read a graph than it is to understand statistical concepts. And from a more analytical point of view, there’s just a growing amount of data and visualization is a good way to poke around.
Other than R, what tools should students learn to improve their data visualizations?
For static graphics, I use Illustrator all the time to bring storytelling into the mix or to just provide some polish. For interactive graphics on the Web, it’s all about JavaScript nowadays. D3, Raphael.js, and Processing.js are all good libraries to get started.
Do you think the rise of infographics has led to a “watering down” of data visualization?
So I actually just wrote <a href="http://flowingdata.com/2011/12/08/on-low-quality-infographics" target="_blank">a post</a> along these lines. It’s true that there a lot of low-quality infographics, but I don’t think that takes away from visualization at all. It makes good work more obvious. I think the flood of infographics is a good indicator of people’s eagerness to read data.</div>
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<strong>How did you decide to write your book “Visualize This”?</strong>
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Pretty simple. I get emails and comments all the time when I post graphics on FlowingData that ask how something was done. There aren’t many resources that show people how to do that. There are books that describe what makes good graphics but don’t say anything about how to actually go about doing it, and there are programming books for say, R, but are too technical for most and aren’t visualization-centric. I wanted to write a book that I wish I had in the early days.
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<strong>Any final thoughts on statistics, data and visualization? </strong>
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<strong><br /></strong>Keep an open mind. Oftentimes, statisticians seem to box themselves into positions of analysis and reports. Statistics is an applied field though, and now more than ever, there are opportunities to work anywhere there is data, which is practically everywhere.
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