Software development

This course focuses on discovering good coding practices for a professional level.
The language used is Python, but some elements of bash and git will also be useful.
A particular emphasis will be placed on data processing and visualization at the heart of the course.
We will focus mainly on basic programming concepts, as well as the discovery of Python's scientific libraries, including "numpy, scipy, pandas, matplotlib, seaborn".
Beyond knowledge of these fundamental packages, we will introduce modern practices for code: (unit) testing, version control (git), automatic documentation generation, etc.

Be able to create a professional-level package in Python, including code versioning, unit testing and documentation.

Students should have a basic knowledge of probability, optimization, linear algebra and statistics. A minimum knowledge of basic programming structures (if, then... else, while/for loop) is also required.
 

 
Recommended prerequisites: A minimum knowledge of Python would be a plus, as well as numerical analysis (floating point, rounding errors, etc.).

- CC
- Project (70% of final grade)
- Modality: small group, with a balanced contribution from each member.
- Expected work: a "github" repository where the work is available, a summary report and a defense.
- Graded practical work (15% of final grade)
- Quiz (15% of final grade)

1. coding: algorithms, modules, basic types, functions, loops
   2. coding: list, dictionary, tuples, if and loops, exceptions
   3. classes (`__init__`, `__call__`, etc....), operator overloading, file management, git: a first introduction
   4. `numpy`: basic notions of arrays, slicing, simple linear algebra, masking; `matplotlib`: first plots
   5. `github`, creating an ssh key, various git commands, conflict, pull request
   6. `numpy`: casting, concatenation, `imshow`, `meshgrid`, casting, copy, scipy`: EDO, Interpolation, Optimization
   7. hands on git, intro to linux basics and command-line tools
   8. `scipy`: Images/channel, FFT, Pandas: missing data
   9. bash, regexp, grep, find, rename, Python virtual environment
   10. Pandas: first steps
   11. Python virtual env: Anaconda, IDE : VScode, Create a Python Module
   12. Learn more about Pandas
   13. Creating a Python module, unit testing
   14. unit testing
   15. Sparse matrices and graphics and memory
   16. Numba
   17. Documentation with Sphinx
   18. Statsmodels

Timetable:
CM : 12h
TD : 18h
TP :
Field :