Learning resources

Courses, tutorials and tools to get started with geocomputing. Some are free for the UNIL community, some are open to all.

Courses and tutorials

Getting Started with Machine Learning: a list of resources to help environmental scientists get started with machine learning. It includes tutorials with code, literature reviews, textbooks (often including code), and datasets that are already pre-processed for machine learning applications.
Hands-on introduction to Multiple Point Statistics: an introduction to stochastic simulation using multiple point statistics, a modelling approach based on the use of training images with the aim of generating realistic heterogeneity characterising natural processes.
Solving PDEs in parallel on GPUs with Julia: ETH's course 101-0250-00L on solving partial differential equations (PDEs) in parallel on graphical processing units (GPUs) with the Julia programming language.
FGSE geoinformatics and machine learning teaching materials: course materials and notebooks on geoinformatics and machine learning for Earth and environmental sciences.
EPFL on Coursera: a free platform where EPFL professors and others share plenty of high-level classes across many fields.
YouTube: a less formal platform with a ton of very good resources; always check whether there are tutorials for your needs. A few examples: Christian Kaiser has a great channel for cartography (in French), the Bro Code for Python tutorials, The AI Guy for machine learning.

Packaging workshop (Python and Julia): from a Jupyter notebook to a package, with the Python and Julia material.
Geocomputing coffee: Git, GitHub and remote development basics from the first and second meet-ups.
Advanced geospatial data analysis: the R book of the course.

Julia: designed from the beginning for high performance.
Python: a programming language that lets you work more quickly and integrate your systems more effectively.
R: a free software environment for statistical computing and graphics.
Octave: a free alternative to MATLAB, useful for students who used that language during their studies.

For a more detailed comparison, read R vs. Python vs. Julia.

Git: software that helps developers and data scientists store and manage their code and other text-based resources, track and control changes, and collaborate with others. Several web services host Git repositories and make them accessible: GitHub is the best known, others are GitLab, Bitbucket and Codeberg.
Jupyter: free software, open standards and web services for interactive computing across all programming languages.
Google Colaboratory: a hosted Jupyter notebook service that requires no setup, with free access to computing resources including GPUs.