Teacher(s)

Kieffer Suzanne; Lits Grégoire;

Language

French

Content

• Data acquisition
• Encoding, parsing (cleaning) and filtering of data
• Data analysis
• Data representation
• Interaction with data
• Evaluation and improvement of visualization

Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The instructional approach is blended teaching, which alternates remote online teaching via Microsoft Teams and face-to-face classroom teaching. The distribution between distance and face-to-face is adapted according to the evolution of the health situation. For example, it is possible to switch from one in two face-to-face sessions (“yellow scenario”) to one in three face-to-face sessions (“orange scenario”), and vice versa. In addition, some sessions are replaced by independent work activities carried out individually (e.g. following tutorials about statistics and visualization software, creating an interactive visualization).
The teaching methods are flipped classroom and project-based teaching:

• Flipped classroom: students study the material at home and then meet their teacher and peers in a classroom to ask questions and get additional help or to work with their peers;
• Project-based teaching: students develop a project by combining online learning and face-to-face meetings.

Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Formative assessment (there is therefore no certification exam at the end of the term) according to two modes: individual assignments (60% of the final grade), exercises and tests (40% of the final grade). The validation of the credits associated with this course requires the successful completion in each mode.
Second session: personalized individual assignment to be delivered on the first day of the session.

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.

Online resources

Moodle (asynchronous): course slides, bibliographic resources, calendar, models and rubrics, H5P exercises, tests, assignments, workshops with peer assessment, 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.

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. (1999). Traduction et adaptation de la troisième édition américaine par Louis Imbeau, De Boeck.
Spence, R. (2007). Information Visualization: Design for Interaction.
Tufte, E. (2001). The visual display of quantitative information, 2nd edition. Graphics Press.
Ware, C. (2012). Information Visualization, 3rd Edition, Perception for Design. Morgan Kaufmann.

Faculty or entity

COMU