Study programme 2018-2019Français
Data Sciences IV
Programme component of Master's Degree in Biochemistry and Molecular and Cell Biology Research Focus à la Faculty of Science
CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M2-BBMCFA-039-MOptional UEGROSJEAN PhilippeS807 - Ecologie numérique des milieux aquatiques
  • GROSJEAN Philippe

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français0000033.00Full academic year

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-BIOG-043Data Sciences IV: practice00000A100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • 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.
    • Methodically research valid scientific information, critically analyse, 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.
    • Adapt their communication to a variety of audiences.
    • Master the techniques of written and oral scientific communication in both French and English.
  • Develop and integrate a high degree of autonomy.
    • Aquire new knowledge independently.
    • 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.
  • Skill 2: Have acquired professional skills in relation to the objective defining the degree.
    • Learn about scientific research and the world of research.
  • 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.
  • 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.
    • Contributing to a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
    • 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.
    • Methodically research valid scientific information, critically analyse, 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.
    • Adapt their communication to a variety of audiences.
    • Master the techniques of written and oral scientific communication in both French and English.
  • Develop and integrate a high degree of autonomy.
    • Aquire new knowledge independently.
    • Pursue further training and develop new skills independently.
    • Develop and integrate a high degree of autonomy to evolve in new contexts.
  • Apply scientific methodology.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

To allow students to improve the acquisition, organization and management of biological data coming from their Master Thesis or in a related field. Use of advanced tools R, RStudio, RMarkdown and git to organize, write reports, collaborate and present analyses and results in data science. The main goal is to be able to design its experiments and to analyze its data in a well-organized way to permit these analyzes to be reproducible. This assignment best prepares for the analysis of the Master Thesis's data, but also to new challenges in the context of Open Science (Open Data, reproducible research, Open Publication).

Content of UE

Good practices in experimental design, data and analyses organization in order to allow them to be shared (internally within the staff, or externally: Open Science). Statistical methods related to the Master Thesis's subject. Mastering of the dedicated software (R ecosystem).

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.

Type of Assessment for UE in Q1

  • N/A

Q1 UE Assessment Comments

Not applicable.

Type of Assessment for UE in Q2

  • Presentation and/or works

Q2 UE Assessment Comments

The report of the analyses within a Github Classroom repository will be evaluated.

Type of Assessment for UE in Q3

  • Presentation and/or works

Q3 UE Assessment Comments

The report of the analyses within a Github Classroom repository will be evaluated.

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
S-BIOG-043
  • Préparations, travaux, recherches d'information

Mode of delivery

AAMode of delivery
S-BIOG-043
  • Face to face
  • Mixed

Required Reading

AA
S-BIOG-043

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-BIOG-043Not applicable

Recommended Reading

AA
S-BIOG-043

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-BIOG-043Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-BIOG-043Barnier, 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).

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-BIOG-043Unauthorized
(*) 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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