![]() | Study programme 2018-2019 | Français | |
![]() | Data Sciences II: analysis and modelling | ||
Programme component of Bachelor's Degree in Biology à la Faculty of Science |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
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US-B3-SCBIOL-006-M | Compulsory UE | GROSJEAN Philippe | S807 - Ecologie numérique des milieux aquatiques |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Français | 15 | 0 | 15 | 0 | 0 | 3 | 3.00 | 1st term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
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S-BIOG-015 | Data Sciences II : analysis and modelling | 15 | 0 | 15 | 0 | 0 | Q1 | 100.00% |
Programme component |
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Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
To be able to analyze multivariate biological data in practice. Ordination (PCA, FA) and classification (dendrogram) techniques must be perfectly mastered at the end of the course. Students learn to solve actual cases in asking precise questions from a statistical point of view. Data modeling will be also studied via simple, polynomial and multiple regression. They learn to describe their data properly, to test conditions of use of the statistical techniques and to draw valid conclusions from their 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
Multivariate statistics: PCA; factor analysis; hierarchical clustering; simple, polynomial and multiple regression.
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.
Type of Assessment for UE in Q1
Q1 UE Assessment Comments
Written exam. Calculation by hand of a distance matrix and/or a dendrogram, plus a couple of exercises in multivariate statistics and data modeling, and to draw conclusions about these analyzes.
Type of Assessment for UE in Q3
Q3 UE Assessment Comments
Similar to Q1.
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 |
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S-BIOG-015 |
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Mode of delivery
AA | Mode of delivery |
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S-BIOG-015 |
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Required Reading
AA | |
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S-BIOG-015 |
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
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S-BIOG-015 | Not applicable |
Recommended Reading
AA | |
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S-BIOG-015 |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
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S-BIOG-015 | Not applicable. |
Other Recommended Reading
AA | Other Recommended Reading |
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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). |
Grade Deferrals of AAs from one year to the next
AA | Grade Deferrals of AAs from one year to the next |
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S-BIOG-015 | Authorized |