Study programme 2018-2019Français
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
CodeTypeHead of UE Department’s
contact details
US-M1-SCBBMC-004-MCompulsory UEGROSJEAN PhilippeS807 - Ecologie numérique des milieux aquatiques
  • GROSJEAN Philippe

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français151500033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-BIOG-025Data Sciences III: exploration and prediction1515000Q1100.00%
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

AAType of Teaching Activity/Activities
  • Cours magistraux
  • Conférences
  • Travaux pratiques
  • Travaux de laboratoire
  • Exercices de création et recherche en atelier
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
  • Face to face
  • Mixed

Required Reading


Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-BIOG-025Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-BIOG-025 Not applicable.

Other Recommended Reading

AAOther Recommended Reading
S-BIOG-025Barnier, 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. 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

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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