Course objective

The objective of the course is to initiate young life-science scientists to python programming from scratch, fostering self-learning practice.

Organisation:

The course include twelve classes (two per week), each two-hours long, over a period of six weeks. A large part of each class will be devoted to practical coding ecercices.

Assessment

• The participants will be regularly asked to explain their code during the classes.

• Coding exercices and small projects will be proposed over the duration of the course.

Course material

The course will be based on the textbook published by Whitington (2023), as well as on online ressources from the python community.

Suggested readings

Whitington J (2023). Python from the very beginning. Cambridge: Coherent Press (2nd ed).

 

https://docs.python.org/3/

 

Aims:

Climate change prevention and mitigation requires behavioral change or reconfiguration of behaviors and policies.
The goal of this class is to establish a transformational contact between expertise in geoscience and biological sciences and expertise in behavioral and cognitive sciences on environmental issues, in particular as the ocean and marine biodiversity are concerned.
Themes:
The course 1) Draws from the life sciences and geosciences to understand what has an impact on climate change (i.e., the causal chains);
2) Draws from economics to understand how to regulate and incentivize citizens and firms to go towards what has a positive environmental impact; 
3) Draws from cognitive science to understand the psychological and social side of the needed change (how to understand and change individuals' and groups'; behavior).

Course prerequisites
An interest in the main issues of the course, in particular in brakes to behavioral change on a global scale, and in an understanding of the loops between the physical, biological, and human aspects and roles of the ocean.

Aims 

The objective of the course is to initiate young life-science scientists to the bases of machine learning, and how to use it in Python with the scikit-learn package.

Organisation

The course will include twelve classes (two per week, except for the 1st and 8th of May), each two-hours long, over a period of eight weeks, in April-May 2025.
A large part of each class will be devoted to practical coding exercises.
A few hours of homework per week.

Assessment
• The participants will be regularly asked to explain their code during the classes.
• Coding exercises and quizzes will be proposed over the duration of the course.
• The course will be assessed through a final project to complete after the end of the course

Ackowledgement

The course will be based on the INRIA open online course (https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/), adapted towards biology.