Data Science with Python

Télécom Saint-Etienne
2nd year, Engineer Telecom Saint-Etienne
15 hours
License CC BY-NC-SA 4.0 - Jordan Patracone - 2025

This course offers a solid foundation in Python programming while introducing essential concepts and practices of data science. Students will progressively learn how to manipulate, analyze, and represent data using modern Python tools.

Prerequisites

Participants are expected to have a proficient understanding of the following prerequisites:

Probability.
Statistics (descriptive and inferential).
Mathematical foundations (analysis, linear algebra, etc.).

Educational Goals

The course is designed to provide both programming practice and methodological grounding in data science:

Acquire strong fundamentals in Python programming and gain an introduction to data science.
Discover and apply key Python libraries for data analysis (NumPy, pandas, etc.).
Evaluate data quality and manage missing values, outliers, and class imbalance.
Apply appropriate feature transformations and selection techniques.
Employ methods to effectively represent, explore, and interpret data.

Course Materials

After a brief introduction, the course is structured into four 1.5-hour chapters, complemented by three practical sessions of three hours each.

Course Program Introduction Analyzing Real-World Data with Pandas and Plotly Numerical Computing with NumPy Feature Engineering with Scikit-Learn Vectorization and Dimensionality Reduction with Scikit-Learn

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License