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This course aims to provide intermediate notions of R programming to people that already have basic experience with R. We will learn how to efficiently manipulate different data structures, compute basic summary statistics and visualize the results. This will allow you to learn how to do some basic analysis and visualization of your own data by the end of the course.  In this course we will combine explanations and examples with lots of hands-on that will allow you to get familiar with basic programming concepts and explore the different possibilities that R offers.

Learn how to write a Nextflow pipeline from scratch Join our beginner-friendly course on Nextflow, the powerful workflow management system for scalable and reproducible data analysis. This course covers the fundamentals of Nextflow, from writing your first pipeline to running it efficiently. You’ll learn how to be reproducible, using containers and how to automate complex analyses, and optimize workflows for high-performance computing (HPC).

This course is designed to teach the basics of everyday Linux Containers usage. Participants will learn what Linux Containers are and why they are relevant to today's scientific practice. They will learn hands-on Docker, the most popular container technology, and by the end of the course, they should be able to build simple container images by themselves. They will also be introduced to Singularity/Apptainer, a more suitable container software for HPC environments.

The aim of this course is to give a general overview on Nextflow, focusing on the execution, configuration and deployment of local and publicly available pipelines.

This course aims to provide basic notions of R programming to people that have NEVER worked with R and that want to learn how to use it for data analysis and visualization.  The Introduction to R course starts from the very basics of R language, all the way through learning how to create scripts, read and write files, manipulate different data structures and plot the results, which will allow you to learn how to do some basic analysis and visualization of your own data by the end of the course. In this course we will combine explanations and examples with lots of hands-on that will allow you to get familiar with basic programming concepts and explore the different possibilities that R offers.

This course is designed to teach the fundamental concepts and practical guidelines for ensuring that everyday data generation and management tasks fit into reproducible scientific workflows. The course emphasizes the importance of open data formats, and recommends using Markdown for documentation. Participants will also learn how to use Gitlab and Github, two data collaboration platforms, for tracking and managing data and documentation across different interfaces such as command line, IDEs and web browser. Git's underlying version control capabilities will be covered in detail during the hands-on sessions. 

The course will guide you to setup a development environment using VS Code together with Gitlab and creating a personal Git repository for the course. Afterwards, it will provide Python programming concepts, covering variables, functions, loops, lists, dictionaries, among others. During the process we will show you tools to help you keep the code clean and to enforce proper Python syntax and styling.  Later, we will cover the basics of file handling in Python, gaining the ability to read/write files and to manipulate data. Finally, we will visualize data by using the packages matplotlib and seaborn by using practical examples.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

Welcome to the "Complete Cluster  Course"! This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. The main message of the course is to embrace the parallelism available within the cluster and that pipelines should be made from lots of small independent pieces that are spread throughout the cluster rather than large monolithic long jobs that run on a single node. The course will show why this should be done and how to achieve it. Topics that are going to be addressed:   Video tour of the data centre  What is a cluster  Logging in  Queuing / the scheduler  What resource are available at the CRG cluster  Simple batch scripts - directives  Troubleshooting - what happened to my jobs?  Interactive sessions  Supercomputers, beowulf clusters, horizontal v vertical scaling  Hardware considerations  Multithreaded jobs, parallelism, Amdahl's Law  Job arrays  Job dependencies  Building a pipeline  Storage issues, treemap  Job stats, resource estimation  Scaling analysis 

This course provides an introduction to using the linux terminal. It is suitable for complete beginners who have never used the command line before.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. 

This course provides an introduction to using the linux terminal. It is suitable for complete beginners who have never used the command line before.

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course provides an introduction to using the linux terminal. It is suitable for complete beginners who have never used the command line before.

This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. 

This course will give a quick revision of essential general concepts for using a cluster well followed by specific examples utilising slurm to run jobs on the cluster. 

This course will consolidate material presented in the beginner cluster course and expand on the concepts to be aware of when trying to optimize use of the cluster. The main message of the course is to embrace the parallelism available within the cluster and that pipelines should be made from lots of small independent pieces that are spread throughout the cluster rather than large monolithic long jobs that run on a single node. The course will show why this should be done and how to achieve it.