| Course label : | Numerical modeling |
|---|---|
| Teaching departement : | MIN / Applied Mathematics and General Computing |
| Teaching manager : | Mister CHRISTOPHE DUJARDIN |
| Education language : | |
| Potential ects : | 0 |
| Results grid : | |
| Code and label (hp) : | ENSCL_CI_M7_5_2 - Modélisation numérique |
Education team
Teachers : Mister CHRISTOPHE DUJARDIN / Mister JEREMIE BOUQUEREL / Mister LUDOVIC THUINET
External contributors (business, research, secondary education): various temporary teachers
Summary
The aim of this course is to expand and improve the use of VBA and Python programming languages through application exercises borrowed from numerical analysis.
Educational goals
At the end of this course, students should: 1/ Know the main methods of algorithmic resolution associated with the mathematical problems commonly encountered in programming (fixed point, Newton-Raphson, secant, Monte Carlo, Euler, Runge-Kutta, and finite difference methods) 2/ Know how to put these methods into practice using VBA and Python programming languages
Sustainable development goals
Knowledge control procedures
Continuous Assessment
Comments: Final exam on a computer (duration: one hour and 45 minutes)
Online resources
One "Introduction to Python" course handout One "Introduction to Excel VBA" course handout One worksheet on VBA One worksheet on Python
Pedagogy
The main algorithmic resolution methods are covered in the lecture. In the tutorials, these methods are applied using VBA and Python programming languages.
Sequencing / learning methods
| Number of hours - Lectures : | 3 |
|---|---|
| Number of hours - Tutorial : | 9 |
| Number of hours - Practical work : | 0 |
| Number of hours - Seminar : | 0 |
| Number of hours - Half-group seminar : | 0 |
| Number of student hours in TEA (Autonomous learning) : | 0 |
| Number of student hours in TNE (Non-supervised activities) : | 0 |
| Number of hours in CB (Fixed exams) : | 0 |
| Number of student hours in PER (Personal work) : | 0 |
| Number of hours - Projects : | 0 |
Prerequisites
Students must have completed the "Statistics and data processing" course in semester 5. Students must have completed the "IT tools for engineers" course in semester 6.