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Lecture notes and notebooks for statistical data analysis and machine learning in Earth science

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Earth data science

Description

The class Earth data science is a master-level class of the institut de physique du globe de Paris. This course is a legacy of the course of the same name by Antoine Lucas. The lectures are taught by Léonard Seydoux and the practicals by Antoine Lucas, Alexandre Fournier, Éléonore Stutzmann and Léonard Seydoux.

The goal of this course is to introduce students to the basics of scientific computing and to the use of Python for solving geophysical problems. The course mostly consists in practical sessions where students will learn how to use Python to solve problems related to the Earth sciences mith statistical and machine learning methods. The course and notebooks rely on the Python scikit-learn library, pandas, pytorch, and the deep learning book by Ian Goodfellow, Yoshua Bengio and Aaron Courville.

Course content

The course contains a 8-hour lecture followed by 20 hours of practical sessions made with Jupyter notebooks. The lecture notes are available in the lectures folder and the practicals in the labs folder. You can find an introductory README file in each folder.