 | Study programme 2018-2019 | Français | |
 | Data Sciences V : reproducible research | ||
Activité d'apprentissage à la Faculty of Science |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) |
|---|---|---|---|
| S-BIOG-077 |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Français | Français | 10 | 10 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
Data management; databases; SQL queries; S language (software R) with RStudio; floating-point calculation; pseudo-random numbers generation; reproducible analysis; unit tests; data formats; optimization of calculation speed; optimization of RAM used; vectorized algorithms, ...
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). Chambers, J.M., 2008. Software for data analysis. Programming with R. Springer, New York, 498pp. Dagnelie, P., 2007. Chambers, J.M., 1998. Programming with data. A guide to the S language. Springer, New York, 469pp. Fortner, B., 1995. The data handbook. A guide to understanding the organization and visualization of technical data. Springer, New York, 350pp.
Mode of delivery
- Face to face
- Mixed
Type of Teaching Activity/Activities
- Cours magistraux
- Conférences
- Ateliers et projets encadrés au sein de l'établissement
- Préparations, travaux, recherches d'information
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)