Information visualisation

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5.00 credits

30.0 h + 30.0 h

Language

English
> French-friendly

Main themes

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

Learning outcomes

At the end of this learning unit, the student is able to :

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

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.

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

Moodle page of the course: https://moodle.uclouvain.be/course/view.php?id=3502

Bibliography

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

Teaching materials

Slides of the course, available on Moodle

Faculty or entity

> DACS

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

Title of the programme

Sigle

Credits

Prerequisites

Learning outcomes

Master [120] in Data Science : Statistic

DATS2M

Master [120] in Computer Science and Engineering

INFO2M

Master [120] in Computer Science

SINF2M

Master [120] in Mathematical Engineering

MAP2M

Master [120] in Data Science Engineering

DATE2M

Master [120] in Data Science: Information Technology

DATI2M