Study programme 2021-2022 | Français | ||
Data Sciences II: analysis and modelling | |||
Programme component of Bachelor's in Biology à la Faculty of Science |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
US-B3-SCBIOL-006-M | Compulsory UE | GROSJEAN Philippe | S807 - Ecologie numérique |
|
Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 0 | 40 | 0 | 0 | 0 | 3 | 3.00 | Année |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
S-BIOG-015 | Data Sciences II : modelling | 0 | 20 | 0 | 0 | 0 | Q1 | |
S-BIOG-061 | Data Sciences II: analysis | 0 | 20 | 0 | 0 | 0 | Q2 |
Programme component | ||
---|---|---|
US-B2-SCBIOL-006-M Data Sciences I : visualisation and inference |
Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
To be able to analyze varied biological data. Linear model (linear model, generalized linear model, nonlinear model), ordination (PCA, FA) and classification (dendrogram) techniques are studied. To learn to describe data properly, to test conditions of use of the statistical techniques and to draw valid conclusions from the analyses. Presentation and reporting are also discussed, as well as, the use of professional software in data science: R, RStudio, R Markdown, git.
Content of UE
The chapters of this UE are :
- Linear model
- Linear model, diagnostic tools
- Generalized linear model
- Nonlinear model
- Distance matrices & hierachical clustering
- K-means, MDS & SOM
- PCA & factor analysis
- MFA & biodiverstiy indices
Prior Experience
Bases in data science, including project management, data importation and transformation, visualization of data through graphs and writing of reproducible reports. Uni- and bivariate statistics, including ANOVA, variance, covariance and correlation. Use of software R, RStudio, R Markdown & git.
Type of Assessment for UE in Q1
Q1 UE Assessment Comments
Final grade made of different parts:
- Continuous evaluation of the progression
- Participation in flipped classes
- Output during practical sessions
- Evaluation of a report of data analysis
- eTest
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Assessment for UE in Q2
Q2 UE Assessment Comments
Final grade made of different parts:
- Continuous evaluation of the progression
- Participation in flipped classes
- Output during practical sessions
- Evaluation of a report of data analysis
- eTest
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
Similar to Q1 & Q2.
Type of Resit Assessment for UE in Q1 (BAB1)
Q1 UE Resit Assessment Comments (BAB1)
Not applicable.
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
---|---|
S-BIOG-015 |
|
S-BIOG-061 |
|
Mode of delivery
AA | Mode of delivery |
---|---|
S-BIOG-015 |
|
S-BIOG-061 |
|
Required Reading
AA | |
---|---|
S-BIOG-015 | |
S-BIOG-061 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|---|
S-BIOG-015 | Not applicable |
S-BIOG-061 | Not applicable |
Recommended Reading
AA | |
---|---|
S-BIOG-015 | |
S-BIOG-061 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|---|
S-BIOG-015 | Not applicable. |
S-BIOG-061 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|---|
S-BIOG-015 | 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. Husson, F., S. Lê & J. Pagès, 2009. Analyse de données avec R. Presses universitaires de Rennes, Rennes. 224pp. 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). |
S-BIOG-061 | 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. Husson, F., S. Lê & J. Pagès, 2009. Analyse de données avec R. Presses universitaires de Rennes, Rennes. 224pp. 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). |