Study programme 2018-2019 | Français | ||
Data Sciences IV: practice | |||
Activité d'apprentissage à la Faculty of Science |
Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) |
---|---|---|---|
S-BIOG-043 |
|
Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
---|---|---|---|---|---|---|---|
Français | Français | 0 | 0 | 0 | 0 | 0 | A |
Content of Learning Activity
Good practices in experimental design, data and analyses organization in order to allow them to be shared (internally within the staff, or externally: Open Science). Statistical methods related to the Master Thesis's subject. Mastering of the dedicated software (R ecosystem).
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).
Mode of delivery
Type of Teaching Activity/Activities
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)