Institut Polytechnique de Paris
A grouping of leading engineering schools — École Polytechnique, ENSTA, ENSAE, Télécom Paris, Télécom SudParis and École Nationale des Ponts et Chaussées — Institut Polytechnique de Paris enriches the programme with engineering sciences.
Detailed educational programme for Institut Polytechnique de Paris
This course introduces students to the fundamental methods of statistical analysis and data processing in Python.
The practical sessions use the most widely used numerical libraries (numpy, pandas, scikit-learn) and include a paired project applied to real datasets. The most advanced students can experiment with implementing simplified deep learning models.
This module combines statistical rigor, scientific programming practice, and the use of modern machine learning tools.
This course deepens the fundamental techniques of data processing, analysis, and modeling. It introduces advanced statistical methods, basic machine learning algorithms, as well as computer tools for managing and exploiting large datasets.
The emphasis is on practical implementation and critical interpretation of results in various contexts.
This course offers an advanced approach to statistical methods, supervised and unsupervised learning techniques, and the management of large databases.
It emphasizes the rigorous application of models, critical evaluation of results, and interpretation of complex data. Students develop strong programming skills and use specialized software to address real-world problems in various fields.
The objective of this course is to introduce students to the analysis of social phenomena using quantitative data. It combines sociological concepts and statistical tools to measure behaviors, inequalities, and social dynamics.
Students learn to collect and process data, use analysis methods such as descriptive statistics and regression, and interpret the results for practical applications such as social mobility or inequalities.
This course in computational sociology explores quantitative and computational methods applied to the study of social phenomena. It introduces the fundamental concepts of modeling, social network analysis, and the processing of large databases resulting from human interactions.
The course highlights the use of digital tools for the collection, analysis, and visualization of social data.