5.00 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Lee John;
Language
English
Content
· What and why information visualisation?
· Data abstraction: types of data and of datasets
· Which visualisation for which task?
· Validating visualisations
· Display and ocular perception
· Visualisation channels (colour, size, shape, angle, ...)
· Tabular data: lists, matrices, tensors
· Spatial data: scalar, vector and tensor fields
· Networks and trees
· Link between machine learning and visualisation: clustering, dimensionality reduction, graph embedding
· Interactive visualisation
· Multiple views
· Advanced topics in visualisation
· Data abstraction: types of data and of datasets
· Which visualisation for which task?
· Validating visualisations
· Display and ocular perception
· Visualisation channels (colour, size, shape, angle, ...)
· Tabular data: lists, matrices, tensors
· Spatial data: scalar, vector and tensor fields
· Networks and trees
· Link between machine learning and visualisation: clustering, dimensionality reduction, graph embedding
· Interactive visualisation
· Multiple views
· Advanced topics in visualisation
Teaching methods
Lectures, practical sessions on computers, project.
All activities can switch from presential to comodal or distancial depending on sanitary conditions.
All activities can switch from presential to comodal or distancial depending on sanitary conditions.
Evaluation methods
Oral examination with preparation time. Practical modalities depend on sanitary conditions.
Examination is split in 12/20 for the course and 8/20 for the project.
Online resources
Moodle page of the course: https://moodleucl.uclouvain.be/course/view.php?id=12042
Bibliography
Visualization analysis & Design, Tamara Munzner, CRC Press, 2015.
Teaching materials
- Slides of the course, available on Moodle
Faculty or entity
EPL
