Study programme 2018-2019Français
Data Sciences I : visualisation and inference
Programme component of Bachelor's Degree in Biology à la Faculty of Science
CodeTypeHead of UE Department’s
contact details
US-B2-SCBIOL-006-MCompulsory UEGROSJEAN PhilippeS807 - Ecologie numérique des milieux aquatiques
  • GROSJEAN Philippe

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français255000066.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-BIOG-006Data Sciences I : visualisation and inference2550000Q1100.00%
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
    • Integrate knowledge from other fields of knowledge with biology (earth science, physics, chemistry, mathematics), in a critical way, to foster an interdisciplinary approach
  • Solve issues relevant to biology
    • Analyse and interpret, in an appropriate way, biological data collected in natura, through dissection or based on an experimental protocol 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 master software and statistical tools required for data science, more particularly, data importation, management and transformation, data visualization and inference.
To present results clearly and adequately in a scientific report. To be able to analyze correctly usual biological data in practice.

Content of UE

Software R, RStudio, git & Markdown. Importation and transformation of datasets. Visualisation of uni-, bi-, and multivariate data. Descriptive statistics; Mean; Median; Standard deviation; Variance; Q-Q plot; Boxplot; Histogram; Statistical population; Sampling; Inference; Probabilities; Statistic distribution; Central limit theorem; Confidence interval; Hypothesis test; Parametric and non parametric tests; Binomial, Poisson, Chi-2, Normal, Student t and F distributions; Student t-test; One and two factors ANOVA; Wilkoxon-Mann-Withney test, Kruskal-Wallis test; Correlation; Pearson; Spearman.

Prior Experience

Basic use of a computer. Bases in calculus, including logarithm and exponential, cartesian coordinate system and elementary geometry in 2D and 3D.

Type of Assessment for UE in Q1

  • Presentation and/or works
  • Written examination
  • Quoted exercices
  • eTest

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 - Written exam

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Written examination
  • Quoted exercices
  • eTest

Q3 UE Assessment Comments

Similar to Q1.

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)


Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
  • Cours magistraux
  • Conférences
  • Exercices dirigés
  • Utilisation de logiciels
  • Démonstrations

Mode of delivery

AAMode of delivery
  • Face to face
  • Mixed

Required Reading


Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-BIOG-006Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-BIOG-006Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-BIOG-006Barnier, 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 ( 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