 | Study programme 2021-2022 | Français | |
| Data Sciences I : visualisation | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| S-BIOG-006 |
|
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Français | Français | 0 | 35 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
The chapters of this AA are:
- Introduction - Software & tools (Software R, RStudio, git & Markdown)
- Visualisation I - Scatterplot
- Visualisation II - Distributions
- Visualisation III - Barplot/boxplot
- Data processing I - Importation/conversion
- Data processing II - Contingency/sampling
- Data processing III - Multi-tables/databases
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Barnier, J., 2018. Introduction à R et au tidyverse (https://juba.github.io/tidyverse/index.html). Ismay, Ch. & Kim A.Y, 2018. Moderndive: An introduction to statistical and data science via R (http://moderndive.com). Wickham, H. & Grolemund, G, 2017. R for data science (http://r4ds.had.co.nz). Cornillon, P.A. Et al, 2008. Statistiques avec R. Presses Universitaires de Rennes. 257pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II).
Mode of delivery
- Mixed
Type of Teaching Activity/Activities
- Travaux pratiques
- Exercices de création et recherche en atelier
- Projet sur ordinateur
- Etudes de cas
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)