Objectives of Programme's Learning Outcomes

• Acquire, understand and use knowledge in the fields of biology and other fields
• Understand and use mathematical tools and basic statistics to describe and understand biological concepts
• Synthesise and summarise, in a critical way, information from scientific literature in different forms (textual, numerical, verbal and graphic)
• Solve issues relevant to biology
• Make accurate observations in the context of activities in the field and in the laboratory
• Apply a scientific approach and critical thinking
• Understand and apply the basic principles of reasoning (obtaining data, analysis, synthesis, comparison, rule of three, syllogism, analogy, etc.)
• Understand the statistical and/or probabilistic methods
• Work with efficiency / accuracy / precision
• Present a hypothesis and hypothetical-deductive reasoning
• Develop critical thinking, test and monitor conclusions understanding the domain of validity, and explore alternative hypotheses
• Manage doubt and uncertainty
• Communicate effectively and appropriately in French and English
• Communicate in French, orally and in writing, the results of experiments and observations by constructing and using graphs and tables
• Develop autonomy, set training objectives and make choices to achieve them
• Organise time and work, individually and in groups
• Prioritise
• Manage stress regardless of events (exams, presentations, etc.)

Learning Outcomes of UE

To be able to analyze varied biological data. Linear model (linear model, generalized linear model, nonlinear model), ordination (PCA, FA) and classification (dendrogram) techniques are studied. They learn to describe their data properly, to test conditions of use of the statistical techniques and to draw valid conclusions from their analyses. Presentation and reporting are also discussed, as well as, the use of professional software in data science: R, RStudio, R Markdown, git.

Content of UE

The chapters of this UE are : 

- Linear model
- Generalized linera model
- Nonlinear model
- Robust & quantile regression/survival analysis
- Distance matrices & hierachical clustering
- K-means & SOM
- PCA & facotr analysis
- MFA & Multidimensional scaling

Prior Experience

Bases in data science, including project management, data importation and transformation, visualization of data through graphs and writing of reproducible reports. Uni- and bivariate statistics, including ANOVA, variance, covariance and correlation.

Type of Assessment for UE in Q1

• Presentation and/or works
• Practical test
• 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
- E-test

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

Type of Assessment for UE in Q2

• Presentation and/or works

Q2 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

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

Type of Assessment for UE in Q3

• Written examination

Q3 UE Assessment Comments

Similar to Q1 & Q2.

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable.