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.
Pedro da Costa Brito Cabral
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
- 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.
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.
- 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%).
- 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.
Programs where the course is taught:
- Specialization in Information Analysis and Management
- Specialization in Risk Analysis and Management
- Specialization in Knowledge Management and Business Intelligence
- Specialization in Information Systems and Technologies Management
- Specialization in Marketing Intelligence
- Specialization in Marketing Research and CRM
- Specialization in Knowledge Management and Business Intelligence – Working Hours Format
- Specialization in Information Systems and Technologies Management - Working Hours Format
- Specialization in Marketing Intelligence - Working Hours Format
- Post-Graduation in Information Analysis and Management
- Post-Graduation Risk Analysis and Management
- PostGraduate in Smart Cities
- PostGraduate in Digital Enterprise Management
- PostGraduate in Information Management and Business Intelligence in Healthcare
- Post-Graduation in Knowledge Management and Business Intelligence
- Post-Graduation Information Systems and Technologies Management
- PostGraduate in Enterprise Information Systems