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
• Integrate knowledge from other fields of knowledge with biology (earth science, physics, chemistry, mathematics), in a critical way, to foster an interdisciplinary approach
• 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
• Analyse and interpret, in an appropriate way, biological data collected in natura, through dissection or based on an experimental protocol 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. To learn to describe data properly, to test conditions of use of the statistical techniques and to draw valid conclusions from the analyses. Presentation and reporting are also discussed, as well as, the use of professional software in data science: R, RStudio, R Markdown, git.

UE Content: description and pedagogical relevance

The pedagogical material is available online: https://wp.sciviews.org. The chapters of this UE are: 

- Simple linear regression and residuals analysis (part I)
- Multiple and polynomial linear regressions, residuals analysis (part II)
- Linear models and contrast matrices
- General linear models
- Nonlinear models
- Hierarchical clustering, K-means, distance matrices, biodiversity indices
- PCA and CA
- MFA and big data
- Databases and MDS
- Open data and SOM
 

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. Use of software R, RStudio, R Markdown & git. An update of your knowledge prior to the course is possible thanks to the online material of the first data science course at https://wp.sciviews.org.

Type(s) and mode(s) of Q1 UE assessment

• Written examination - Face-to-face
• Production (written work, report, essay, collection, product, etc.) - To be submitted in class
• Graded assignment(s) - Remote

Q1 UE Assessment Comments

Grading is done by using an ongoing evaluation of the progression all along the sessions. Presence to the sessions is mandatory. Unjustified absences will be sanctionned.

Method of calculating the overall mark for the Q1 UE assessment

The final grade is the average of the grade for Q1 and the grade for Q2 (50/50) -grades for ongoing assessments, see corresponding AA-. The grade for each AA must be at least 8/20, or the weakest grade is used as UE final grade. In case of failure, both AA must be done again next academic year.

Type(s) and mode(s) of Q1 UE resit assessment (BAB1)

• N/A - Néant

Q1 UE Resit Assessment Comments (BAB1)

Not applicable

Method of calculating the overall mark for the Q1 UE resit assessment

Not applicable

Type(s) and mode(s) of Q2 UE assessment

• Production (written work, report, essay, collection, product, etc.) - To be submitted in class
• Graded assignment(s) - Remote

Q2 UE Assessment Comments

Grading is done by using an ongoing evaluation of the progression all along the sessions. Presence to the sessions is mandatory. Unjustified absences will be sanctionned.

Method of calculating the overall mark for the Q2 UE assessment

The final grade is the average of the grade for Q1 and the grade for Q2 (50/50) -grades for ongoing assessments, see corresponding AA-. The grade for each AA must be at least 8/20, or the weakest grade is used as UE final grade. In case of failure, both AA must be done again next academic year.

Type(s) and mode(s) of Q3 UE assessment

• N/A - Néant

Q3 UE Assessment Comments

Given that the grade for this UE is established through ongoing assessment of works that cannot be organized during the summer, there is no second session.

Method of calculating the overall mark for the Q3 UE assessment

Not applicable