Study programme 2021-2022 | Français | ||
Bioinformatics and data sciences II | |||
Programme component of Bachelor's in Biology (Charleroi (Hor. jour)) à la Faculty of Science |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
US-B3-SCBIOC-940-C | Compulsory UE | CONOTTE Raphael | S819 - FS - Service Décanat-Site CHRL (Charleroi) |
|
Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 0 | 60 | 0 | 0 | 0 | 5 | 5.00 | Année |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
S-BIOG-937 | Data sciences - Modeling | 0 | 25 | 0 | 0 | 0 | Q1 | |
S-BIOG-958 | Data sciences - Mulitfaceted analysis | 0 | 20 | 0 | 0 | 0 | Q2 | |
S-BIOG-959 | Bioinformatics | 0 | 15 | 0 | 0 | 0 | Q2 |
Programme component | ||
---|---|---|
US-B2-SCBIOC-926-C Bioinformatics and data sciences |
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 techniques (PCA, FA)
- Classification techniques (dendrogram)
Correctly describe the data and test the conditions of use of the statistical techniques.
Draw appropriate conclusions from their analysis.
Introduction to bioinformatics and genomic data analysis with Bioconductor
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
- Generalized linera model
- Nonlinear model
- Robust & quantile regression/survival analysis
- Distance matrices & hierachical clustering
- K-means & SOM
- PCA & facotr analysis
- MFA & Multidimensional scaling
- Introduction to Bioconductor
- RNA-Seq analysis
- Chip-Seq analysis
Prior Experience
Bases in data science, including :
- project management,
- data importation and transformation,
- visualization of data through graphs,
- writing of reproducible reports,
- Uni- and bivariate statistics, including ANOVA, variance, covariance and correlation.
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
- E-test
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
- E-test
For the continuous evaluation of the progression, presence to the sessions is mandatory.
Type of Assessment for UE in Q3
Q3 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
- E-test
For the continuous evaluation of the progression, presence to the sessions is mandatory.
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-937 | |
S-BIOG-958 | |
S-BIOG-959 |
Mode of delivery
AA | Mode of delivery |
---|---|
S-BIOG-937 |
|
S-BIOG-958 |
|
S-BIOG-959 |
|
Required Reading
AA | |
---|---|
S-BIOG-937 | |
S-BIOG-958 | |
S-BIOG-959 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|---|
S-BIOG-937 | Not applicable |
S-BIOG-958 | Not applicable |
S-BIOG-959 | Not applicable |
Recommended Reading
AA | |
---|---|
S-BIOG-937 | |
S-BIOG-958 | |
S-BIOG-959 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|---|
S-BIOG-937 | Not applicable |
S-BIOG-958 | Not applicable |
S-BIOG-959 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|---|
S-BIOG-937 | 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). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. 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. |
S-BIOG-958 | 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). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. 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. |
S-BIOG-959 | Not applicable |