HAB796B9: Software development for data science
(Almost) everything you need to know as an applied mathematician/statistician concerning coding and system administration.
Teachers
- Benjamin Charlier: benjamin.charlier@umontpellier.fr
Prerequisite
Some rudiments in coding are expected (if, for, while, functions) but not mandatory. Students are expected to know basic notions of statistics.
Course description
This course focuses on discovering good coding practices (the language used is Python, but some elements of bash and git will also be useful) for professional coding. A special focus on data processing and visualization will be at the heart of the course. We will mostly focus on basic programming concepts, as well as on discovering the Python scientific libraries, including numpy, scipy, pandas, matplotlib, seaborn. Beyond pandas ninja skills, we will also introduce modern practices for coders: (unitary) tests, version control, documentation generation, etc.
| Date | Teacher | Details |
|---|---|---|
| 25/09/2023 | BC | Command-line tools, Version control with Git |
| 02/10/2023 | BC | Version control with Git |
| 16/10/2023 | BC | Version control with Git, IDE / Python virtual environment |
| 23/10/2023 | BC | Python basics |
| 06/11/2023 | BC | SciPy, Pandas |
| 14/11/2023 | BC | Project snapshots |
| 12/12/2023 | BC | The end: Project presentations |
Grading
For this course, the grading consists of one group project.
Books and other resources
The resources for the course are available on the present GitHub repository. Additional elementary elements (in French) on Python are available in the course python for biologists.
Moodle webpage
The Moodle web page is available to registered students only.
Additional resources
- (General): The Missing Semester of Your CS Education
- (Algorithmic basis): Algorithms, by Jeff Erickson
- (Data Science): Python Data Science Handbook, With Application to Understanding Data by J. Van DerPlas, 2016;
videos: Reproducible Data Analysis in Jupyter - (General) Skiena, The algorithm design manual, 1998
- (General) Courant et al., Informatique pour tous en classes préparatoires aux grandes écoles: Manuel d’algorithmique et programmation structurée avec Python, 2013, (french)
- (General/data science) Guttag, Introduction to Computation and Programming, 2016
- (Code and style) Boswell et Foucher, The Art of Readable Code, 2011
- (Scientific computing tools for Python) Scipy lectures notes
- (Datasets) Open Climate Data