Introduction to scientific computing with python
Ingénieur 2000, Arts et Métiers, 2020
This short course is part of the program FIP (no idea what it stands for) proposed by Arts et Métiers Institute of Technology. Its aim is to introduce students to scientific computing with python. Because the audience consists mostly of engineering students spending half of their time in industry, the idea is to have some “fun” time rather than a lengthy course about scientific computing. To do so, the course consists in a series of small projects oriented toward pratical applications. In the process, they will learn the basics of data manipulation with numpy, visualization with matplotlib and optimization with scipy. Another proper course on scientific computing using matlab is also proposed in this program wherein students will learn the basics of numerical differentiation and data analysis.
Prerequisite
- Basic mathematics (e.g. matrix and vector calculus, derivative)
Lesson plan
The course consists in five computer sessions of 1.5 hours.
- Tutorial 1: Introduction to Python.
- Tutorial 2: Introduction to NumPy.
- Tutorial 3: Introduction to Matplotlib.
- Tutorial 4: Good practices in NumPy.
- Tutorial 5: Introduction to SymPy.
Recommended reading
- J. Nathan Kutz. Data-driven modeling & scientific computing: methods for complex systems and big data.
- S. L. Brunton & J.N. Kutz. Data-driven science and engineering. Machine learning, dynamical systems and control.