Teaching Data Visualization


Questions for Discussion

Why is data visualization important in your field?

What tools do you use to generate images?

What do your data visualization assignments look like right now?

What tools are available to your students?

How can your department or small groups of faculty encourage core learning in this area? What are you doing already?


Best Practices

  • Be sure to give students learning materials related to data visualization and information design. Show examples of good and bad design.

  • Scaffold assignments so that students create graphics for more and more complex data as the semester goes along. 

  • Allow students one exercise or assignment to explore the myriad concepts of data visualization before they apply it to data from your course.

  • Establish Data Vizualiation workshops so that students understand the importance of audience and interpretation.

  • Invite a guest lecturer to cover what you can’t!



Piktochart – Infographic editor with lots of templates and features

Canva – Infographic editor.

Venngage (free for students) Infographic editor

Airtable: a relational database with visualization features.

Google Sheets “Explore” feature.

Handout from Georgetown U – Types of charts

A Periodic Table of Visualization Methods

What is Chartjunk? Tufte Explains (good reading assignment)

Google Fusion Tables, spreadsheet charts, pivot tables, all available in Google Drive—https://support.google.com/drive/#topic=2799627

Silk— http://silk.co —create interactive databases for people to explore

Datavisual— http://datavisu.al —create charts and graphs with multiple datasets

Polychart— http://www.polychart.com —easy drag/drop interface for charts, requires download

Plotly— http://plot.ly —has it’s own API and accepts a wide variety of data formats including MATLAB

Datawrapper­— https://datawrapper.de —open source tool for creating simple, attractive charts

Tufte’s Fundamental Principles of Analytical Design

1: Show comparisons, contrasts, differences.

2: Show causality, mechanism, explanation, systematic structure.

3. Show multivariate data; that is, show more than 1 or 2 variables.

4. Completely integrate words, numbers, images, diagrams.

5. Thoroughly describe the evidence. Provide a detailed title, indicate the authors and sponsors, document the data sources, show complete measurement scales, point out relevant issues

Download a PDF of this chapter from Beautiful Evidence

Tufte’s Advice for Graphical Displays

  • show the data
  • induce the viewer to think about the substance rather than about methodology, graphic design, the technology of graphic production, or something else
  • avoid distorting what the data have to say
  • present many numbers in a small space
  • make large data sets coherent
  • encourage the eye to compare different pieces of data
  • reveal the data at several levels of detail, from a broad overview to the fine structure
  • serve a reasonably clear purpose: description, exploration, tabulation, or decoration
  • be closely integrated with the statistical and verbal descriptions of a data set.

Download a PDF of this chapter from The Visual Display of Quantitative Information

Video Collection for Students

Gestalt Principles

Edward Tufte Lecture

David McCandless TED Talk

Royal Statistical Society