5.00 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Bogaert Patrick; Hanert Emmanuel (coordinator); Vanclooster Marnik;
Language
French
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Content
The course has 3 parts: An introduction to the Python programming language, a course on numerical methods and a group project. The introduction to Python is mainly carried out through practical sessions in the computer room supplemented with self-learning. The numerical methods module covers the following topics:
- General introduction to programming in Python
- Numerical solution of systems of linear equations
- Interpolation and curve fitting
- Roots of equations
- Numerical differentiation and integration
- Numerical solution of initial value problems
- Applications of these different tools to concrete cases
Students will then apply these programming and numerical methods tools in a group project that will also include mathematical and statistical concepts seen in previous courses. This project mostly takes place in the computer room and has a significant personal work component. The study of a real case is proposed to groups of four students. It involves the following steps:
- Analysis of raw data and development of a program in Python to process the data files and make them usable for the following steps of the project.
- Formulation of mathematical and statistical models describing the problem to be solved.
- Description of a method for the numerical solution of the problem.
- Programming in Python of the numerical algorithm.
- Writing up by each group of a report and oral presentation of this report.
Teaching methods
This module involves lectures for the numerical methods part with practical sessions in the computer room to learn Python. These sessions are supplemented with online self-learning. The project part is presented during a lecture. The students then work in groups of four or five. Practical sessions specifically dedicated to the project are also planned.
Evaluation methods
The evaluation is based on a written exam for numerical methods part, a test during the semester on the Python programming part and on the group report and the oral presentation of the project. The final mark is the weighted average of these different elements. The project is a group activity that will only be assessed during the June exam session.
Other information
The course does not use any particular support which would have to be paid and deemed obligatory. Any paid books that may be recommended are optional.
Online resources
Bibliography
Pour la partie informatique : notes de cours rédigées en anglais ainsi que de nombreux exemples de programmes Matlab disponibles sur le site Moodle du cours ainsi que des livres de référence sur le langage de programmation Matlab en nombreux exemplaires à la Bibliothèque des Sciences exactes. Pour la partie projet : fascicules, livre de référence, vade-mecum et instructions détaillées disponibles sur Moodle.
Faculty or entity
AGRO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Environmental Science and Management
Interdisciplinary Advanced Master in Science and Management of the Environment and Sustainable Development
Additionnal module in Geography