Information visualisation

Enseignants

Lee John;

Langue
d'enseignement

Anglais

Thèmes abordés

Visualisation of information, data, tasks, tools, perception, visualizing tabular and spatial data, graphs and trees, links with machine learning, interaction, multiple views.

Acquis
d'apprentissage

A la fin de cette unité d’enseignement, l’étudiant est capable de :
1	With respect to the AA referring system defined for the Master in Data Science Engineering the course contributes to the development, mastery and assessment of the following skills : ·         DATA 1.2 ·         DATA 2.1, 2.2, 2.3, 2.4, 2.5 ·         DATA 5.1, 5.2, 5.3, 5.4, 5.5 ·         DATA 6.1, 6.2, 6.3 At the end of the course, students will be able to : ·         Understand perceptive and cognitive processes behind visualisation ·         Relate tasks and visualisation tools ·         Categorize data types ·         Analyze an existing visualisation ·         Design an appropriate visualization ·         Validate visualisations ·         Implement visualisation tools

Contenu

·         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

Méthodes d'enseignement

Lectures, practical sessions on computers, project.
All activities can switch from presential to comodal or distancial depending on sanitary conditions.

Modes d'évaluation
des acquis des étudiants

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.

Ressources
en ligne

Site Moodle du cours: https://moodleucl.uclouvain.be/course/view.php?id=12042

Bibliographie

Visualization analysis & Design, Tamara Munzner, CRC Press, 2015.

Support de cours

• Slides of the course, available on Moodle

Faculté ou entité
en charge

EPL