A guide through the basics of the file systems and the shell.

Track code changes, undo mistakes, collaborate. This module is a must.

Get started with an incredibly popular programming language.

Introduction to the Secure Shell (SSH), your number one tool for remote computing

Make science prettier with beautiful plots!

The most famous data analysis framework used in HEP.

Track code changes, undo mistakes, collaborate. This module is a must.

Learn to work with branches and more with this interactive webpage.

Continuous integration and deployment with gitlab: automatically run unit tests and more for every commit that you push on gitlab.

Continuous integration and deployment with GitHub actions: automatically run unit tests and more for every commit that you push on GitHub.

Introduction to the docker container image system. Docker allows to consistently run your code in any environment or on any machine, making it an important ingredient to analysis preservation.

Introduction to containerization with Singularity/Apptainer. Singularity is a containerization tool (similar to Docker) that is particularly useful for HPC environments.

Unit testing in python.

Advanced bits of python (testing, debugging, logging, and more)

This course covers various best practices like testing, pytest, object oriented programming, packing, CI, and more.

A full introduction to C++ based on a series of slides and exercises.

Building code is hard. CMake makes it easier.

A collection of packages for particle physics analyses in Python.

The most famous data analysis framework used in HEP.

Open ROOT files in Julia!

Run containerised data analysis pipelines on remote compute clouds.

Learn about ROOT, RooFit, machine learning with TMVA, and physics simulations.

pyhf user guide and tutorial

Learn how to easily and efficiently process your data with array-oriented programming!

Test your skills on data manipulation by solving puzzles using array-oriented programming!

Learn about neural networks in the context of particle physics.

Speed up your machine learning using massive parallelization! It is highly recommended to first go through the "Deep learning for particle physicists" tutorial.

Track code changes, undo mistakes, collaborate. This module is a must.

Continuous integration and deployment with gitlab: automatically run unit tests and more for every commit that you push on gitlab.

Continuous integration and deployment with GitHub actions: automatically run unit tests and more for every commit that you push on GitHub.

Introduction to the docker container image system. Docker allows to consistently run your code in any environment or on any machine, making it an important ingredient to analysis preservation.

Introduction to containerization with Singularity/Apptainer. Singularity is a containerization tool (similar to Docker) that is particularly useful for HPC environments.

Run containerised data analysis pipelines on remote compute clouds.

Unit testing in python.

This course covers various best practices like testing, pytest, object oriented programming, packing, CI, and more.

Advanced bits of python (testing, debugging, logging, and more)

Learn about ROOT, RooFit, machine learning with TMVA, and physics simulations.

From python, shell, and git to reproducible analyses with Snakemake. Written for LHCb, but applicable to everyone.

An introduction to Julia for HEP, especially for those familiar with Python (or C++)

Open ROOT files in Julia!

Una colección de paquetes para el análisis de física de partículas en Python.