lcomu2811  2019-2020  Louvain-la-Neuve

Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h
Q2
Teacher(s)
Kieffer Suzanne;
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
Aims

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.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
  • Visual perception
  • Processing, representation and presentation of data
  • Interaction with data
  • Design principles
  • Trends: dashboards and visual analytics
Teaching methods
Hybrid teaching combining lectures, flipped classroom and teaching by project
Evaluation methods
Formative assessment including individual assignments, group assignments and knowledge tests. The validation of the credits associated with this course requires the success of each of these activities. All relevant information related to these terms and conditions is available on Moodle.
Other information
Some teaching resources are in English.
Online resources
  • Moodle: slides, bibliography, workshops, assignments, models and evaluation criteria grids
  • Web: videos, blogs, websites, online software
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.
Faculty or entity
COMU


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Information and Communication Science and Technology

Master [120] in Communication

Master [60] in Information and Communication