Here are the answers from Chad Skelton, data journalist at the Vancouver Sun and one of the winners of the GEN Data Journalism Awards 2014. He teaches at Kwantlen Polytechnic University, which four campuses located in the Metro Vancouver region of British Columbia, Canada.
Chad Skelton is an award-winning data journalist at The Vancouver Sun and a journalism instructor at Kwantlen Polytechnic University. In 2014, Chad won an international Data Journalism Award for his portfolio of work in the previous year. He has also received the Jack Webster Award, British Columbia’s top journalism prize, six times, most recently for a data project on political donations and lobbyists in B.C.
What are you teaching in terms of data visualization at the university?
Chad Skelton: “Students typically come into my class with little or no experience in analyzing data. So we start with the basics: how to open a spreadsheet in Excel and answer some simple questions using sorting and filtering. Then we move onto Pivot Tables and, after that, how to make data visualizations and interactive maps. I try to find a balance between concepts and tools. So I’m teaching them about data visualization basics — like why pie charts are problematic — but then also how to actually make interactive charts with tools like Datawrapper.”
Why do you think data visualizations are relevant?
“Human beings evolved to make sense of color and patterns, not look at rows of data on a spreadsheet. Data visualization allows us to see the patterns and trends in our data at a glance. Indeed, one of the key messages I give my students is that, rather than just thinking of data viz as something they do at the very end of their research, it should be an integral part of their research: helping them to figure out what’s most important about their data by letting them see what’s going on.”
How and why do you use Datawrapper?
“Datawrapper’s simplicity makes it one of the best tools I’ve come across for teaching data visualization to beginners. Students are able to create useful interactive charts in a matter of minutes, which helps build confidence. Also, what some might see as one of Datawrapper’s limitations — it works best with small datasets, not massive ones — is actually a feature when it comes to teaching. It forces students to think about their data, and get it into shape — through sorting, filtering or Pivot Tables — before they start to make a chart.”
What would be features we should add to the tool for better use?
“I honestly can’t think of any at the moment as most features would undermine the simplicity that makes Datawrapper so great. For example, I really like Tableau’s “tooltip” feature as it can allow me to “show my work” on a chart: so, for example, if a chart showed the % decline in murder in 10 countries over the past decade, the tooltip could reveal the actual number of murders in 2004 and 2014. In Tableau, you can put that tooltip data in adjoining columns. But in Datawrapper, adjoining columns means it defaults to a multi-dimensional chart — and I think that’s good, as it’s a more intuitive interface.”