Photo by Cory M. GrenierLast week, PBS MediaShift invited me to answer some questions about teaching visualization for its #EdShift Twitter chat. I was part of a virtual panel that included Meredith Broussard (Temple University), Alberto Cairo (University of Miami), Hannah Fairfield (The New York Times), Susan McGregor (Columbia University) and Molly Steenson (University of Wisconsin-Madison; Go Badgers). Katy Culver, MediaShift’s education curator, moderated.

The compiled Q&A is on MediaShift’s website. Some of my tweeted answers were a bit longer than what was captured, so I’m putting them in paragraph form here, lightly edited for typos and readability. My answers may be a bit stilted due to the constraints of tweeting in 140-character bursts.

We should teach dataviz in J-schools (and other schools as well) because it’s a valuable way to tell people information. Dataviz is most powerful when it elicits emotion and understanding, and makes people remember information.

When first starting out, I think the challenge is getting data that’s relatively clean and easy to understand, so students can focus on the examination/inquiry tasks of data visualization.

Lots of universities collect cleanish datasets, e.g.: University of Edinburgh School of Informatics, Stanford Network Analysis Project (SNAP), and the Open Science Data Cloud public datasets. And one of my favorite data scientists, Hilary Mason, has a collection of research-quality datasets.

For students/dataviz novices, I’d suggest using datasets for things students are already familiar with. Familiar datasets let students dive into analysis and examination without having to climb a high hurdle of subject expertise they don’t yet have. As the analysis and examination techniques become more familiar, start using less familiar and dirtier (messed up, error-laden, not normalized) datasets.


I love museums & I find museum datasets interesting. Do a web search for “museum collection dataset” & you’ll find things like Sydney’s Powerhouse Museum science and design dataset and the Canadian Museum of Nature collection data. There are many more museum APIs and datasets on this wiki.

With regard to data visualization tools… Honestly? OpenRefine and Microsoft Excel. They’re not flashy, but OpenRefine is the most useful tool available for cleaning and exploration. And Excel a workhorse. Can’t afford an Excel license? LibreOffice will serve you well.


Hannah Fairfield recommended this New York Times lesson plan by Shannon Doyne, Holly Epstein Ojalvo and Katherine Schulten, and I do to. It’s a great resource.

I’d also suggest looking at WTF Visualizations and asking students to point out what’s wrong.


The biggest mistake? Assuming everything you need to know is held within the dataset itself.

It’s important to remember that data collection is often skewed in some way. Sometimes it’s maliciously. More often not. Regardless, always ask yourself questions not just about the data, but who gathered it, how it was gathered, why it was gathered, and what other data or information might complement, enhance or refute the dataset you have.

(Question 7 was directed to Alberto, so we move on to question 8…)


You could look at the “should it be interactive” or not question a few ways:

1) “Should it move?”
Things that move tend to get a lot of attention. But are you making things move for movement’s sake? Don’t bother.

2) “Does it serve the story well to be presented as a slow reveal?”
Perhaps. For example, look at “Riding the New Silk Road,” which allows the reader to concentrate on the story, be pulled through the narrative, and understand geographic location all at once.
Riding the New Silk Road

3) “Does the dataviz have something ‘about me’ in it that’s important to discover?”
If it does, then yes, make it interactive. Take for example, this piece on “The Jobless Rate for People Like You.
The Jobless Rate for People Like You


I’d highly recommend that people interested in data vizualization also work through John Foreman’s “Data Smart: Using Data Science to Transform Information into Insight.” It’s readable and practical, and will teach you great techniques for analyzing data. John is Mailchimp’s chief data scientist.

(Photo: Cory M. Grenier/Flickr)