Study programme 2021-2022Français
Exploration et prédiction des données
Programme component of Master's in Computer Science à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
US-M1-SCINFO-022-MOptional UEGROSJEAN PhilippeS807 - Ecologie numérique
  • 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-025Science des données III : exploration et prédiction1515000Q1100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
  • Manage large-scale software development projects.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
    • Demonstrate independence and their ability to work alone or in teams.
  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
    • Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
  • 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.
  • Apply scientific methodology.
    • Critically reflect on the impact of IT 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

  • Presentation and/or works
  • Practical test
  • Graded tests
  • 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 - eTest

For the continuous evaluation of the progression, presence to the sessions is mandatory.

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Practical Test
  • Graded tests
  • 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)

Not applicable.

Type of Teaching Activity/Activities

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

Mode of delivery

AAMode of delivery
  • 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 : 17/09/2021
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