The Challenge of Teaching Data Visualization
As the demand for producing high-quality data visualizations grow, so does the demand for high-quality instruction. How can students combine statistics, design, and programming to understand and visualize data? Courses in data visualization are now being offered in a wide array of fields: journalism, computer science, art, and public policy, to name a few. And in addition to traditional learning settings such as universities, online courses are allowing more people to learn these skills, but also create new questions about the most effective ways to teach these skills. In this panel, we propose to discuss the challenges surrounding teaching data visualization, a topic that is receiving increased attention and scrutiny (see, for example, this recent Data Stories podcast: http://datastori.es/ds37-teaching-visualization-w-scott-murray-and-andy-kirk/).
Additional Supporting Materials
- What skills should students expect to have upon completing a course in data visualization?
- Are there specific resources, websites, tools, or methods that can be used to improve instruction?
- Should data visualization courses focus on a single tool, multiple tools, or no tools at all?
- To what extent should a data visualization course touch upon statistics and working with data?
- What are the best types of exercises, examples, homeworks, and exams to give to students?
- Jonathan Schwabish, Senior Research Associate, Urban Institute
- Cole Nussbaumer, Storyteller, Storytelling with Data
- Ben Shneiderman, Distinguished University Professor, University of Maryland
- Kaiser Fung, Senior Data Advisor, Vimeo
Jonathan Schwabish, Senior Research Associate, Urban Institute
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