Visualisation des données et de l'information

• Understand the different types of data that can be viewed.
• Master the elements constituting a graph, their strengths and weaknesses.
• Know the types of graphics available, and how best to use them.
• Be able, based on a set of data and an objective, to choose the visualization that will best convey a message.
• Make the best use of visualization tools to explore a set of data, to understand its essence, the (co-)relationships
• Be able to critically analyze a graph you are confronted with.
• Master the graphing tool studied during the course (ggplot)

•  8 sessions of 3 hours of ex-cathedra courses (to be given via video capsules in 2020-2021 due to the health crisis)
• 3 practical sessions (to be given via Teams in 2020-2021 due to the health crisis)
• a group project to be carried out during the year

Contribution to the teaching profile

For M-JOURR:

CARRYING OUT SCIENTIFIC WORK

• Designing answers
• Collecting, structuring, analyzing and interpreting data and documents
• Format and communicate research results (written and oral expression)
• Develop a clear, precise, structured and well-argued discourse

• Implementing scientific expertise
• Demonstrating critical thinking and autonomy
• Implementing the capacities of analysis, synthesis, contextualization, rigor and coherence

• To critically apply what one has learned and to innovate in order to conduct research independently.
• Collects data and documents using appropriate work instruments and submits them to the appropriate department to the criticism of the data and documents collected
• Formulate hypotheses, analyze, structure and interpret data
• Format and communicate research results (written and oral expression)
• Elaborate a clear and constructed discourse, argue and use the scientific language of the discipline

• Implementing scientific expertise :
• Demonstrating critical thinking and autonomy
• Implementing the capacities of analysis, synthesis, contextualization, rigor and coherence.

References, bibliography, and recommended reading

• Cleveland, W. S., Visualizing data, Murray Hill, Hobart Press, 1993 ;
• Tufte, E., The visual display of quantitative information, Cheshire, Graphics Press, 2001 ;
• Ware, C., Information visualization : Perception for design, San Francisco, Morgan Kaufmann (Elsevier), 2004 (The Morgan Kaufmann Series in Interactive Technologies, 22) ;
• Few, S., Show me the numbers : Designing tables and graphs to enlighten, Oakland, Analytics Press, 2004.

Course notes

• Université virtuelle

Contacts

Sébastien de Valeriola

sebastien.de.valeriola@ulb.be

Campus

Solbosch

Method(s) of evaluation

• written examination
• Written report

written examination

Written report

Written exam in session, group project during semester.

With regard to the use of generative artificial intelligence tools, the terms and conditions of this course follow those set out for dissertations and TPMs in the Master "dissertation guide".

Mark calculation method (including weighting of intermediary marks)

First session: in-session exam: 12/20; group draft: 8/20

Second session: in-session exam: 20/20

Language(s) of evaluation

• french