Information visualisation

LDATA2010  2018-2019  Louvain-la-Neuve

Information visualisation
5.0 credits
30.0 h + 30.0 h
1q

Teacher(s)
Lee John ;
Language
Anglais
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

·         Reducing items and attributes: feature selection and dimensionality reduction

·         Interactive visualisation

·         Multiple views

·         Advanced topics in visualisation

Teaching methods

Lectures, practical sessions on computers, project

Evaluation methods

Oral Exam

Online resources

Moodle page of the course

Bibliography
  • Slides of the course, available on Moodle
Faculty or entity


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

Program title
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
5
-

Master [120] in data Science: Information technology
5
-