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 Python software to complete data visualization exercises aiming to explore visual interaction with data for analysis and communication.

Caracterização geral





Professor responsável

Pedro da Costa Brito Cabral


Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês


  • The lectures, group discussions, reading materials, quizzes, and exams will be all in English language. Although not mandatory, the students should have a proficient English language skills.
  • Basic knowledge of Python is advisable.


Wilke, C. O. (2019). Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media.

Kirk, A. (2016). Data visualisation: A handbook for data driven design.Sage. Second edition.

Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization. New Riders.

Cairo, A. (2016). The truthful art: Data, charts, and maps for communication. New Riders.

Cairo, A. (2019). How Charts Lie: Getting Smarter about Visual Information. WW Norton & Company.

Munzner, T. (2014). Visualization analysis and design. CRC press.

Método de ensino

Theoretical-practical classes related with Data Visualization concepts and specific software (Python). The students will also put into practice the theoretical concepts with two group projects that will be evaluated using the peer-review approach.

Método de avaliação

1st epoch

  • Group project 1 (40%). Poster to be placed in the building hall until the end of the last class of the semester. Peer review assessment.
  • Group project 2 (40%). Interactive dashboard in Python. Peer review assessment.
  • Individual peer review using the visualization wheel for project 1 (10%).
  • Individual peer review using the visualization wheel for project 2 (10%).

2nd epoch

  • Individual homework (50%). Assessed by teacher.
  • Individual project (50%). Poster to be placed in the building hall. Assessed by teacher.


  • The teacher can review and adjust the peer-review grades if needed.
  • The students that go for second epoch, cannot submit the same projects that they submitted during first epoch.


LU1. Introduction to Data Visualization. Main concepts and inspiration.

LU2. Data representation (Data and image models). Principles of Gestalt.

LU3. The visualization wheel. Embellishments.

LU4. Exploring data. Visualizing Distributions. Revealing change.

LU5. Seeing Relationships. Mapping data. Uncertainty and significance

LU6. Plotly fundamentals

LU7. Basic visualizations

LU8. Domain specific visualizations

LU9. Visualizing spatial data

LU10. Dash framework

LU11. Interactive implementation

LU12. Dashboard layout / Project assistance

LU13. Color. Interactivity and Annotation.

LU14. Final remarks and Wrap-up.