Information visualisation

ldata2010  2022-2023  Louvain-la-Neuve

Information visualisation
5.00 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Lee John;
Language
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
Teaching methods
Lectures in classroom, practical sessions on computers, project as homework plus Q&A sessions.
Evaluation methods


Oral examination with preparation time. Interrogation on the course material and about the project realization.
The examination grade is split into 10/20 for the course and 10/20 for the project.
A project report must be handed in as a condition to take the exam.
Online resources
Bibliography
Visualization analysis & Design, Tamara Munzner, CRC Press, 2015.
Teaching materials
  • Slides of the course, available on Moodle
Faculty or entity
EPL


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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology