Study programme 2018-2019Français
Data Sciences II: analysis and modelling
Programme component of Bachelor's Degree in Biology à la Faculty of Science
CodeTypeHead of UE Department’s
contact details
US-B3-SCBIOL-006-MCompulsory UEGROSJEAN PhilippeS807 - Ecologie numérique des milieux aquatiques
  • GROSJEAN Philippe

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français150150033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-BIOG-015Data Sciences II : analysis and modelling1501500Q1100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Acquire, understand and use knowledge in the fields of biology and other fields
    • Understand and use mathematical tools and basic statistics to describe and understand biological concepts
    • Synthesise and summarise, in a critical way, information from scientific literature in different forms (textual, numerical, verbal and graphic)
  • Solve issues relevant to biology
    • Make accurate observations in the context of activities in the field and in the laboratory
  • Apply a scientific approach and critical thinking
    • Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, rule of three, syllogism, analogy, etc.)
    • Understand the statistical and/or probabilistic methods
    • Work with efficiency / accuracy / precision
    • Present a hypothesis and hypothetical-deductive reasoning
    • Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
    • Manage doubt and uncertainty
  • Communicate effectively and appropriately in French and English
    • Communicate in French, orally and in writing, the results of experiments and observations by constructing and using graphs and tables
  • Develop autonomy, set training objectives and make choices to achieve them
    • Organise time and work, individually and in groups
    • Prioritise
    • Manage stress regardless of events (exams, presentations, etc.)

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

  • Written examination

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

  • Written examination

Q3 UE Assessment Comments

Similar to Q1.

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable.

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
  • Cours magistraux
  • Conférences
  • Préparations, travaux, recherches d'information

Mode of delivery

AAMode of delivery
  • Face to face
  • Mixed

Required Reading


Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-BIOG-015Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-BIOG-015Not applicable.

Other Recommended Reading

AAOther Recommended Reading
S-BIOG-015Barnier, J., 2018. Introduction à R et au tidyverse ( Ismay, Ch. & Kim A.Y, 2018. Moderndive: An introduction to statistical and data science via R ( Wickham, H. & Grolemund, G, 2017. R for data science ( 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

AAGrade Deferrals of AAs from one year to the next
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 02/05/2019
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Tél: +32 (0)65 373111