Data Visualization


This course will teach how to create visualizations that effectively communicate the meaning behind data through visual perception. Concepts about human perception of graphic information as well as different ways of mapping different forms of quantitative and qualitative data will be addressed. We will use R software to complete data visualization exercises aiming to explore visual interaction with data for analysis and communication. Demonstrations with Excel/Tableau Public will also be used to teach data visualization concepts.

Learning outcomes:

  • Describe how computer graphics are used to visualize data.
  • Understand how users process information through visual displays.
  • Understand the impact of colors on perception.
  • Know different techniques for visualizing different forms of data.
  • To be aware of current research in Data Visualization.
  • Use R toolkit with packages such as ggplot2 and ggmap to compute and generate statistics and visualizations.
  • Share interactive visualizations using Shiny Apps.
  • Being able to tell stories with data.

General characterization





Responsible teacher

Pedro da Costa Brito Cabral


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English




Tamara Munzner (2014). Visualization Analysis and Design. CRC Press.

Nathan Yau (2011). Visualize this ¿ The FlowingData Guide to Design, Visualization, and Statistics. Wiley Publishing, Inc.


Teaching method

Theoretical-practical classes related with Data Visualization concepts and specific software (Excel, R, Tableau Public).

Evaluation method

1st phase

Papers from other journals can also be selected. In any case, the paper selection must be approved by the professor by email before 19/March. The list with the 5-minute presentations will be made available until 22/March

  • Project report and presentation (35%). The dates of the presentations are 11 and 12 of June after 18:30h. Dates will be arranged individually.
  • Test during the class (50%) (40 multiple choice and T or F questions, no minimum grade). There is not 1st phase exam.


 2nd phase

  • Project report and presentation (50%). It is not possible to improve the grade of the project.
  • 2nd phase Exam (50%) (minimum grade is 9.5 points, 3 open questions and 40 multiple choice and T or F questions).

Subject matter

1.  Introduction to visualization

2.  Data Abstraction and data types

3.  Marks and channels

4.  Evaluation. Rules of Thumb.

5.  Visualizing Tables.

6.  Spatial Data.

7.  Graphs. Networks and Trees.

8.  Color.

9.  Reduce items and attributes

10.  Storytelling