Description

The objective of this course is to give you the opportunity to discover the python modules commonly used in physics and chemistry. The course is a core course for L3. It is therefore aimed at both students with a good background in computer science and beginners. The lectures are organized into 20 30-minutes session. The 15 first ones cover the simplest concepts of algorithmic and the basics of Python (loops, lists, strings, regular expressions ...). The 5 last are a deep dive into the most common Python module used in Sciences : numpy, scipy, matplotlib, pandas, sympy, scikit-learn, etc. All the lecture are online so students can do them at their own pace. Practicals are organized in 10 90-minutes sessions with one specific exercise per session. The 5 first practical are more on numerical computation (stochastic and deterministic methods to evaluate an area, integration and derivation, differential equations, resolution of a set of linear equations, stochastic simulation) while the 5 last are more on data processing (data importation and exportation, curve fitting, string parsing, statistics, machine learning, data mining).

Compétences requises

None.

Compétences visées

Knowledge on the available modules and methods that might be used to tackle issue in physics and chemistry.

Disciplines

• Informatique

Syllabus

The lectures are organized into 20 30-minutes session. The 15 first ones cover the simplest concepts of algorithmic and the basics of Python (loops, lists, strings, regular expressions ...). The 5 last are a deep dive into the most common Python module used in Sciences : numpy, scipy, matplotlib, pandas, sympy, scikit-learn, etc.

All the lecture are online so students can do them at their own pace. Practicals are organized in 10 90-minutes sessions with one specific exercise per session. The 5 first practical are more on numerical computation (stochastic and deterministic methods to evaluate an area, integration and derivation, differential equations, resolution of a set of linear equations, stochastic simulation) while the 5 last are more on data processing (data importation and exportation, curve fitting, string parsing, statistics, machine learning, data mining).

Informations complémentaires

Key words: Computer Science ; Numerical Computation ; Modelling and Simulation ; Data Processing ; Machine Learning

Bibliographie

Online official documentation of Python and most common modules

Contacts

Responsable(s) de l'enseignement

• Morgan Madec