Information visualisation

ldata2010  2020-2021  Louvain-la-Neuve

Information visualisation
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
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
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

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

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

Oral Exam.Practical modalities depend on sanitary conditions.
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
Force majeure
Teaching methods
Ex-cathedra course broadcasted or recorded.  Practical sessions on computers, and project to be carried out individually or by groups of 2 students.
Evaluation methods
Oral exam with Teams and open book, if a face-to-face oral exam in LLN is not permitted; questions might concern the project.


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

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

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology