The aim of the course is to train students in the design and analysis of data visualization, with a strong focus on abstract data. Topics discussed are:
- Visualization in general: pipeline model, purposes and categories of visualization;
- Design: nested process model, design thinking, graphics design;
- Perception and cognition: understanding strengths and limitations of the human brain, and how to exploit these;
- Interaction: enabling exploration of data;
- Visualization methods: visualization of multivariate data, hierarchical data, network data, geographic data, text, story telling;
- Visualization technology: frameworks and tools for producing visualizations.
The focus of thee course is not on technology, this is not a D3-course. Technology changes rapidly, an understanding of principles how to communicate and explore data and how to reflect on this is more important.
The lecture hours are used for formal lectures, workshops, discussion of homework exercises, and presentations of students. Students do 4-5 individual assignments, and one team project.
- Tamara Munzner, Visualization Analysis and Desicn, AK Peters, 2014, ISBN 9781466508910. Thorough and extensive treatment of data visualization
- Stephen Few, Now you see it: Simple Visualization Techniques for Quantitative Analysis, Analytics Press, 2009. Practical introduction to data visualization
- Colin Ware, Visual thinking for design, Morgan Kaufmann, 2008, ISBN 9780123708960. Excellent description of human visual perception for design purposes
Basic computer skills