Study programme 2022-2023Français
Data Sciences IV : reproducible research
Programme component of Master's in Biology of Organisms and Ecology : Research Focus (MONS) (day schedule) à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M2-BIOEFA-015-MOptional UEGROSJEAN PhilippeS807 - Ecologie numérique
  • GROSJEAN Philippe

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français03000033.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-BIOG-077Data Sciences IV : reproducible research030000Q1100.00%

Programme component

Objectives of Programme's Learning Outcomes

  • In the field of biological sciences and particularly in the field of the biology of organisms and ecology, 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 the biology of organisms and ecology.
    • 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 the biology of organisms, ecology and its applications.
  • Develop and integrate a high degree of autonomy.
    • Pursue further training and develop new skills independently.
  • In the field of biological sciences and particularly in the field of the biology of organisms and ecology, 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 the biology of organisms and ecology.
    • 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 the biology of organisms, ecology 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.
  • 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 their discipline in general, and on the contribution to projects.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

To specialize students in biology in biological data science through their initiation to various complementary concepts to previous courses. This course supplements data science concepts taught until now with various more advanced topics: floating-point calculation precision, best coding of data in relation with the current problem, how to realize perfectly reproducible analyses, reproducible pseudo-random generators, writing functions and objects. This course is partly modular in function of specific needs of students.

UE Content: description and pedagogical relevance


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

- 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

Prior Experience

General knowledge in data science, including project management, data importation and transformation, visualization of data through graphs and bases of writing reproducible reports. Advanced biostatistics in main areas used in biological data analyses.
An update of knowledge prior to the course can be done via material related to courses 1 to 3 in data science available online at https://wp.sciviews.org.

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-BIOG-077
  • Travaux pratiques
  • Exercices de création et recherche en atelier
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
S-BIOG-077
  • Hybrid

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-BIOG-077The content for this course is available online https://wp.sciviews.org

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-BIOG-077Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-BIOG-077Barnier, 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.

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-BIOG-077Authorized

Term 1 Assessment - type

AAType(s) and mode(s) of Q1 assessment
S-BIOG-077
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Graded assignment(s) - Remote

Term 1 Assessment - comments

AATerm 1 Assessment - comments
S-BIOG-077Evaluation based on a report that applies the techniques stidued in this course, as well as the different exercices done in the modules..

Resit Assessment - Term 1 (B1BA1) - type

AAType(s) and mode(s) of Q1 resit assessment (BAB1)
S-BIOG-077
  • N/A - Néant

Term 3 Assessment - type

AAType(s) and mode(s) of Q3 assessment
S-BIOG-077
  • Production (written work, report, essay, collection, product, etc.) - To be submitted in class
  • Practical exam - Remote

Term 3 Assessment - comments

AATerm 3 Assessment - comments
S-BIOG-077Similar to Q1.
(*) 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 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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