 | Study programme 2018-2019 | Français | |
 | Data Sciences III: exploration and prediction | ||
Activité d'apprentissage à la Faculty of Science |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) |
|---|---|---|---|
| S-BIOG-025 |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Français | Français | 15 | 15 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
Space-time series; machine learning; random forest; discriminant analysis; nonlinear regression; growth model; dose-response curve; Von Bertalanffy; Richards; Weibull; Gompertz; R and RStudio software including R Markdown and Notebook, git.
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). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II). Venables W.N. & B.D. Ripley, 2002. Modern applied statistics with S-PLUS (4th ed.). Springer, New York, 495 pp. Legendre, P. & L. Legendre, 1998. Numerical ecology (2nd ed.). Springer Verlag, New York. 587 pp.
Mode of delivery
- Face to face
- Mixed
Type of Teaching Activity/Activities
- Cours magistraux
- Conférences
- Travaux pratiques
- Travaux de laboratoire
- 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)