Study programme 2019-2020Français
Science des données III : exploration et prédiction
Programme component of Master's in Biology à la Faculty of Science

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-BIOL60-004-MCompulsory UEGROSJEAN PhilippeS807 - Ecologie numérique des milieux aquatiques
  • GROSJEAN Philippe

Language
of instruction
Language
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-025Science des données III : exploration et prédiction1515000Q1100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Skill 1: Have acquired highly specialised and integrated knowledge and broad skills inbiological sciences, adding to those covered in the Bachelor's programme in biological sciences.
  • Skill 2: Have acquired professional skills relating to the degree.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to contribute to the achievement of research project in biology.
  • Skill 3: Conduct extensive research and development projects related to biological sciences.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
  • Skill 4: Master communication techniques.
    • Communicate, in English both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
  • Skill 5: Develop and integrate a high degree of autonomy.
    • Pursue further training and develop new skills independently.
    • Develop and integrate a high degree of autonomy to evolve in new contexts.
  • Skill 6: Apply scientific methodology.
    • Critically reflect on the impact of their discipline in general, and on the contribution to projects.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

To be able to find useful information in a large dataset using data mining and machine learning tools , to analyze correctly biological data with time-dependencies and to analyse the spatial data. 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

The chapters of this UE are : 

- Classification I - bases & LDA
- Classification II - metrics & trees methods
- Classification III = SVM, deep learning
- Time series I - description, ACF, spectral analysis
- Time series II - decomposition & regularisation
- Spatial statistics, maps & krigging

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)

  • Oral examination
  • N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable.

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-BIOG-025
  • 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
S-BIOG-025
  • Face to face
  • Mixed

Required Reading

AA
S-BIOG-025

Required Learning Resources/Tools

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

Recommended Reading

AA
S-BIOG-025

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 (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

AAGrade Deferrals of AAs from one year to the next
S-BIOG-025Authorized
(*) 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 : 13/07/2020
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