Information Visualization


  1. Promote the understanding of the main visualization techniques and the theoretical foundations necessary for their creation, including the fundamentals of graphic perception, visual coding methods and the main techniques of interaction and narrative;
  2. Promote the critical evaluation of data visualizations, understand their strengths and weaknesses and suggest improvements and improvements;
  3. Stimulate the capacity for data analysis and determination of the problem being treated, organizing the results properly, transforming vague problems into solvable issues and understanding what to include in the analysis and what elements to highlight;
  4. Promote the ability to read and discuss the current literature on visualization;
  5. Promote the acquisition of ideas and interaction techniques through sketches;
  6. Promote the use of data to produce stories with impact and authority

General characterization





Responsible teacher

Ana Raquel de Ponte Figueiras


Weekly - 4

Total - 168

Teaching language





- Brewer, Cynthia A. "Color use guidelines for data representation." In Proceedings of the Section on Statistical Graphics, American Statistical Association, pp. 55-60. 1999.
- Liem, J., Perin, C., & Wood, J. (2020, June). Structure and Empathy in Visual Data Storytelling: Evaluating their Influence on Attitude. In Computer Graphics Forum (Vol. 39, No. 3, pp. 277-289)
- Segel, Edward, and Jeffrey Heer. "Narrative visualization: Telling stories with data." IEEE transactions on visualization and computer graphics 16, no. 6 (2010): 1139-1148.
- Toural-Bran, C., Vizoso, Á., Pérez-Seijo, S., Rodríguez-Castro, M., & Negreira-Rey, M. C. (Eds.). (2020). Information Visualization in the Era of Innovative Journalism. Routledge
- Tufte, Edward R. "The visual display of quantitative information." Vol. 2, no. 9. Cheshire, CT: Graphics press, 1983.

Teaching method

In this course, students will be exposed to the topic of visualization, to develop critical thinking about the issues raised by this topic. They will also be exposed to practical development and will have contact with the available tools. Classes will be theoretical/practical. The first half of each class will be predominantly theoretical, with the subject presented and illustrated with examples. The second half of the class is intended to discuss mandatory readings, of the main course-work (individual and group), and to carry out small practical tasks on the subject presented in the first half of the class.
The course unit's evaluation is based on the quality of student participation in class, the average of the various components of group work, and the classification obtained in the individual final work:
- Participation in classes: 20%
- Group project: 40%
- State of the art Report: 40%

Evaluation method

Continuous Assessment - Group project(40%), Participation in classes(20%), State of the art Report(40%)

Subject matter

- Telling visual stories with data
- Visualization history
- The role of data mining and open data
- Wisdom, Data, Information, and Knowledge
- Exploratory versus explanatory
- Directed by the author versus directed by the reader
- The specifics of narrative visualization
- The value of visualization
- The data and why to visualize
- Visualization in other study areas
- The design of the visualization
- The process
- Types (sets and sequences, temporal data, maps, shapes, text and hypertext, hierarchies and graphics)
- Nominal, ordinal, interval and proportion
- Density, focus and small multiples
- Environment | Desktop, Mobile, Tablet and more
- How did narratives evolve on different platforms?
- Conventional and unconventional forms of presentation
- Success stories: who is innovating in this space?
- Perception, aesthetics, interactivity, animation, color, narrative and software