 | Study programme 2018-2019 | Français | |
 | Science des données III : exploration et prédiction | ||
Programme component of Master's Degree in Biochemistry and Molecular and Cell Biology à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCBBMC-004-M | Compulsory UE | GROSJEAN Philippe | S807 - Ecologie numérique des milieux aquatiques |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 15 | 15 | 0 | 0 | 0 | 3 | 3.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-BIOG-025 | Data Sciences III: exploration and prediction | 15 | 15 | 0 | 0 | 0 | Q1 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- In the field of biological sciences and particularly in the field of the biochemistry, molecular and cell biology, possess highly specialised and integrated knowledge and a wide range of skills adding to those covered in the Bachelor's programme in biological sciences.
- Conduct extensive research and development projects related to biological sciences, in biochemistry and in molecular and cell biology.
- Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
- Show initiative and be able to work independently and in teams.
- Manage and lead research, development and innovation projects.
- Understand unprecedented problems in biological sciences, and more specifically in biochemistry and molecular and cell biology and its applications.
- Master communication techniques.
- Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
- Master the techniques of written and oral scientific communication in both French and English.
- Develop and integrate a high degree of autonomy.
- Pursue further training and develop new skills independently.
- Apply scientific methodology.
- Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.
Learning Outcomes of UE
To be able to analyze correctly biological data with time-dependencies, to fit a nonlinear model (kinetic curve, growth model, dose-response curve, etc.) and to find useful information in a large dataset using data mining and machine learning tools. To be able to present results in a reproducible way (reports) and to use professional software in data science: R, RStudio, R Markdown, git.
Content of UE
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.
Prior Experience
Bases in data science, including project management, data importation and transformation, visualization of data through graphs and writing of reproducible reports. General uni- and multivariate statistics.
Type of Assessment for UE in Q1
- Oral examination
Q1 UE Assessment Comments
Preparation of a theoretical subject, or based on a partly solved dataset during 1/2h. Discussion around this question (explanation of the method, what to do next, others methods appliable on such data, etc.)
Type of Assessment for UE in Q3
- Oral examination
Q3 UE Assessment Comments
Preparation of a theoretical subject, or based on a partly solved dataset during 1/2h. Discussion around this question (explanation of the method, what to do next, others methods appliable on such data, etc.)
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
Q1 UE Resit Assessment Comments (BAB1)
Not applicable.
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-BIOG-025 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-BIOG-025 |
|
Required Reading
| AA | |
|---|---|
| S-BIOG-025 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-BIOG-025 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-BIOG-025 |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-BIOG-025 | Not applicable. |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-BIOG-025 | 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. |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-BIOG-025 | Authorized |