Learning outcomes

By the end of this course, students will be able to develop programmes for the collection, processing, analysis and visualisation of data; apply basic data science methods; critically interpret the results obtained; and produce reliable, well-documented and reproducible code.

Goals

To equip students with the basic programming skills (Python) required for data analysis and exploitation in a scientific context.

Content

Variables, control structures, functions, modular programming, array and dataset manipulation, scientific libraries, data visualisation, introduction to data science methods.

Table of contents

The course modules are:

  • An introduction to programming using Python;

  • Object-oriented programming;

  • Python and topics in physics (astronomy, symbolic computation, etc.);

  • Regular expressions (how to ‘clean’ a data file, ‘isolate’ or ‘sort’ relevant data);

  • Introduction to artificial intelligence (the minimax theorem and basic neural networks).

Exercices

  • Attendance at practical sessions is compulsory recommended, the aim being for students to be able to take charge of an IT project themselves;

  • The exercises focus on classic and engaging problems in computer science.

Teaching methods

Traditional teaching with interactive demonstrations, including practical sessions in the pool.

Assessment method

General nature of the assessment:

Completion of a personal project based on a series of questions centred around a single issue.

Characteristics of the assessment

  • The assessment is open-book, with access to the internet and AI tools. Students complete their work at home;

  • The assessment takes the form of a project to be submitted;

  • The assessment focuses on key themes from the course: quality of the code provided, use of object-oriented programming and regular expressions;

  • The assessment covers both lectures and practical sessions;

  • The assessment also evaluates the originality and inventiveness of the project produced.

Sources, references and any support material

The syllabus sets out the topics covered; references are provided during lessons, as the range of books available in bookshops changes frequently.

Language of instruction

French
Training Study programme Block Credits Mandatory
Master in Physics Finalité spécialisée en physique et data 1 2 Yes