Introduction to Python for Data Science
Master 1, Université Grenoble Alpes, UFR IM2AG, September 2025 - November 2025
Overview
This is the webpage of the course Introduction to Python for Data Science (or L'Introduction au Python pour la Science de Données in French).
The course aims to equipe students with basic Python knowledge. Then we focus on several libraries/packages specialized in statistical learning and data science such as Pandas, Sklearn and Pytorch.
The course aims to equipe students with basic Python knowledge. Then we focus on several libraries/packages specialized in statistical learning and data science such as Pandas, Sklearn and Pytorch.
Course organisation
This course has 8 classes. There is no lecture, and only practical sessions and one project presentation at the end of the course for the evaluation.
For practical sessions, students are invited to clone/download the following Github repository: https://github.com/tung-qle/python-for-data-sciences
In this Github repository, there is a series of Python notebooks (a web-based interactive computing platform that allows people to combine text, code and visualisation) providing basic information about Python and its libraries. There are five main chapters, each of which is dedicated to an important component of Python programming language and its usage in learning context:
- Chap. 1 - Introduction to Python
- Chap. 2 - Python for Scientific Computing
- Chap. 3 - Data Handling with Pandas
- Chap. 4 - Machine Learning with ScikitLearn
- Chap. 5 - Deep Learning with Pytorch
You can progress as your own pace during practical sessions: feel free to jump to the next chapters if you already finished the current one.
These chapters will help you get familiar to Python and eventually do your final project - your only mode of evaluation in this course.
These chapters will help you get familiar to Python and eventually do your final project - your only mode of evaluation in this course.
Evaluation
Your evaluation is entirely based on your final project. You will form a group (of maximum 2 people, solo is also possible) and choose a challenge from https://www.kaggle.com/. At the end of the course, you will submit/present:
- A notebook (or Python code) that implement your methods
- A report (maximum $3$ pages) describes briefly your methods. Sample for report is given here: report sample
- A presentation ($10-15$ minutes, depending on the number of presentations)
Important deadlines
- 13 October 2025: Project group deadline. If you have difficulties finding your partner, please write me an email.
- 27 October 2025: Kaggle challenge choices deadline.
- 17 Nomverber 2025: Notebook and report submission.
- 24 November 2025: Presentation
Failure to respect these deadlines might cause a reduction of $10 - 50\%$ of your final notes.
Experience from previous years: You should
- Start forming your project group and look for a Kaggle project as soon as possible.
- Discuss with me during classes so that your project is not either too simple or too difficult.
- Do not wait until the deadline since I will be surely flooded with questions around that period and could not help much.