Teacher(s)
Language
French
Main themes
· Visual perception
· Representation (encoding of values, of relations)
· Presentation (visualization techniques) and interaction
· Design principles (Gestalt, Bertin, color theory)
· Dashboards and visual analytics
· Representation (encoding of values, of relations)
· Presentation (visualization techniques) and interaction
· Design principles (Gestalt, Bertin, color theory)
· Dashboards and visual analytics
Learning outcomes
At the end of this learning unit, the student is able to : | |
| 1. | Describe data visualizations in terms of data type, data representation, presentation and interaction technique, and user task ; |
| 2. | Explain the different stages involved in the development of interactive visualizations by illustrating each step through its typical results (e.g. deliverables) ; |
| 3. | Apply Information Visualization principles and techniques to design and develop an interactive visualization of a large data set ; |
| 4. | Evaluate a visualization using criteria and propose improvements. |
Content
Visual perception
Processing, representation and presentation of data
Interaction with data
Design principles
Trends: dashboards and visual analytics
Processing, representation and presentation of data
Interaction with data
Design principles
Trends: dashboards and visual analytics
Teaching methods
The pedagogical approach is blended teaching, which alternates face-to-face classroom teaching with online distance learning via Microsoft Teams. Teaching methods include flipped classroom and project-based learning:
- Flipped classroom: students study or complete an assignment at home and then meet with teachers and peers in a classroom to ask questions, get extra help or work in groups;
- Project-based learning: students develop a project by combining online learning (e.g. watching tutorials or completing assignments) and face-to-face meetings.
Evaluation methods
Continuous assessment without examination in June following two modes: knowledge tests (40%), and group and/or individual assignment (60%). In September, a custom-made individual assignment (i.e., based on failed modes) must be submitted on the first day of the session.
The use of artificial intelligence (AI) tools must comply with the guidelines established by the ESPO faculty. It is permitted as a writing aid (e.g., text improvement, translation) and for information retrieval. For the submission of certain assignments, the instructor defines the other authorized uses (e.g., idea exploration, brainstorming, image or text generation).
The use of artificial intelligence (AI) tools must comply with the guidelines established by the ESPO faculty. It is permitted as a writing aid (e.g., text improvement, translation) and for information retrieval. For the submission of certain assignments, the instructor defines the other authorized uses (e.g., idea exploration, brainstorming, image or text generation).
Other information
All relevant information regarding these modalities and the progress of the activities (calendar, detailed instructions, evaluation criteria, etc.) are presented during the first course and are available on Moodle.
Some resources (e.g. bibliographic resources, slides, explanatory videos) are in English.
Some resources (e.g. bibliographic resources, slides, explanatory videos) are in English.
Online resources
Moodle (asynchronous): course slides, bibliographic resources, calendar, models and rubrics, H5P exercises, tests, assignments, workshops with peer assessment, group choice, Q&A forum
Microsoft Teams (live): calendar, meetings, documents, discussion, lecture notes
Web links: how-to videos, websites, online software
Tableau software (https://www.tableau.com/) : online tutorials, academic license with UCLouvain email address.
Microsoft Teams (live): calendar, meetings, documents, discussion, lecture notes
Web links: how-to videos, websites, online software
Tableau software (https://www.tableau.com/) : online tutorials, academic license with UCLouvain email address.
Bibliography
Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C. (2010, April). Useful junk?: the effects of visual embellishment on comprehension and memorability of charts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2573-2582). ACM.
Bertin, J. (1983). Semiology of graphics; diagrams networks maps (No. 04; QA90, B7.).
Cairo, A. (2015). Graphics lies, misleading visuals. In New Challenges for Data Design (pp. 103-116). Springer, London.
Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. Acm, 53(6), 59-67.
Fox, W. Statistiques sociales. Traduction et adaptation de la troisième édition américaine par Louis Imbeau, De Boeck, 1999.
Spence, R. Information Visualization: Design for Interaction. 2007.
Tufte, E. The visual display of quantitative information, 2nd edition. Graphics Press. 2001.
Ware, C. Information Visualization, 3rd Edition, Perception for Design. Morgan Kaufmann. 2012.
Bertin, J. (1983). Semiology of graphics; diagrams networks maps (No. 04; QA90, B7.).
Cairo, A. (2015). Graphics lies, misleading visuals. In New Challenges for Data Design (pp. 103-116). Springer, London.
Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. Acm, 53(6), 59-67.
Fox, W. Statistiques sociales. Traduction et adaptation de la troisième édition américaine par Louis Imbeau, De Boeck, 1999.
Spence, R. Information Visualization: Design for Interaction. 2007.
Tufte, E. The visual display of quantitative information, 2nd edition. Graphics Press. 2001.
Ware, C. Information Visualization, 3rd Edition, Perception for Design. Morgan Kaufmann. 2012.
Faculty or entity
