Information visualisation

ldata2010  2021-2022  Louvain-la-Neuve

Information visualisation
5.00 crédits
30.0 h + 30.0 h
Q1
Enseignants
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


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

Intitulé du programme
Sigle
Crédits
Prérequis
Acquis
d'apprentissage
Master [120] : ingénieur civil en science des données

Master [120] en science des données, orientation technologies de l'information

Master [120] : ingénieur civil en mathématiques appliquées

Master [120] en science des données, orientation statistique