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
• 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 techniques (PCA, FA)
 - Classification techniques (dendrogram) 

Correctly describe the data and test the conditions of use of the statistical techniques.

Draw appropriate conclusions from their analysis.

Introduction to bioinformatics and genomic data analysis with Bioconductor

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 chapters of this UE are : 

- Linear model
- Generalized linera model
- Nonlinear model
- Distance matrices & hierachical clustering
- K-means & SOM
- PCA & CA
- MFA & Multidimensional scaling
- Introduction to Bioconductor 
- RNA-Seq analysis
 

Prior Experience

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

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 online
• Graded assignment(s) - Face-to-face
• Practical exam - Face-to-face

Q1 UE Assessment Comments

Final grade made of different parts:
- Continuous evaluation of the progression
- 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.

Method of calculating the overall mark for the Q1 UE assessment

Data Science - Modeling : 40% of the grade

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

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

Q2 UE Assessment Comments

Final grade made of different parts:
- Continuous evaluation of the progression
- 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.

Method of calculating the overall mark for the Q2 UE assessment

Data sciences : Multivariate analyses : 40% of the grade
Bioinformatics: 20% of the grade

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

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

Q3 UE Assessment Comments

Final grade made of different parts:
- Continuous evaluation of the progression
- 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.

Method of calculating the overall mark for the Q3 UE assessment

Data sciences - Modeling : 40% of the grade
Data sciences - Multivariate analyses : 40% of the grade
Bioinformatics: 20% of the grade