Study programme 2022-2023Français
Data Sciences IV : reproducible research
Learning Activity
CodeLecturer(s)Associate Lecturer(s)Subsitute Lecturer(s) et other(s)Establishment
S-BIOG-077
  • GROSJEAN Philippe
      • UMONS
      Language
      of instruction      		Language
      of assessment      		 HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term
      FrançaisFrançais030000Q1


      Content of Learning Activity

      The pedagogical material is available online: https://wp.sciviews.org. The chapters of this AA are (can possibly change according to the needs of the students that take this AA): 

      - Particular data: dates, text, circular variables
      - Projects: structure, different types of reproducible documents
      - Code modularization: functions, documentation
      - Code optimisation: tests, objects, optimisation techniques
      - Initiation to packages and continue integration
      - Parallelization and cloud computing

      Required Learning Resources/Tools

      The content for this course is available online https://wp.sciviews.org

      Recommended Learning Resources/Tools

      Not applicable

      Other Recommended Reading

      Barnier, 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). Chambers, J.M., 2008. Software for data analysis. Programming with R. Springer, New York, 498pp. Dagnelie, P., 2007. Chambers, J.M., 1998. Programming with data. A guide to the S language. Springer, New York, 469pp. Fortner, B., 1995. The data handbook. A guide to understanding the organization and visualization of technical data. Springer, New York, 350pp.

      Mode of delivery

      • Hybrid

      Type of Teaching Activity/Activities

      • Travaux pratiques
      • Exercices de création et recherche en atelier
      • Projet sur ordinateur
      • Etudes de cas

      Evaluations

      The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)

      (*) 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
      More information on Higher Education Structure (’Paysage’ Decree)
      Date de dernière mise à jour de la fiche ECTS par l'enseignant : 15/05/2022
      Date de dernière génération automatique de la page : 20/06/2023
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