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
• 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 understand and to be able to use software and statistical tools required for data science, more particularly, data importation, management and transformation, data visualization and inference. To present results clearly and adequately in a scientific report. To be able to analyze correctly usual biological data in practice.

UE Content: description and pedagogical relevance

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

- Visualisation I - Scatterplot and introduction to software & tools (Software R, RStudio, git & R Markdown/Quarto)
- Visualisation II - Histogram, density plot, violin plot
- Visualisation III - Barplot, piechart, boxplot, plots assemblage
- Data processing I - Importation, conversion, dplyr
- Data processing II - Contingency, sampling, multi-table processing with tidyr
- Probabilities & distributions I and correlation test
- Probabilities & distributions II and Chi-2 test
- Confidence interval/Student test
- One-way analysis of variance
- Two-way analysis of variance

Prior Experience

Basic use of a computer. Bases in calculus, including logarithm and exponential, cartesian coordinate system and elementary geometry in 2D and 3D. Resources to update your prior knowledges: https://www.khanacademy.org/math, math 1, 2 & 3 + geometry, also https://edu.gcfglobal.org/en/computerbasics/ to learn the basics of computers, and possibly https://edu.gcfglobal.org/en/typing/ to learn typing on a keyboard.

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 established via ongoing assessment all along the Q1 and Q2. The different exercises and projects are used to calculate the grade. Penalties are applied if more than 1/5 of the exercices are not done for each module. Given the way grading is done the presence to all sessions is mandatory. Any unjustified absence to a session will result in a 0/20 for the corresponding content (give advance notice if possible and send proof within two weeks maximum).
See the course summary and the course in line for details on the planning and the grade calculation by type of exercise.
 

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-. 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

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

Q2 UE Assessment Comments

Grading is established via ongoing assessment all along the Q1 and Q2. The different exercises and projects are used to calculate the grade. Penalties are applied if more than 1/5 of the exercices are not done for each module. Given the way grading is done the presence to all sessions is mandatory. Any unjustified absence to a session will result in a 0/20 for the corresponding content (give advance notice if possible and send proof within two weeks maximum).
See the course summary and the course in line for details on the planning and the grade calculation by type of exercise.

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-. 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