Educators:
Renato Alves, Lisanna Paladin, Christian Schudoma (HD-HuB)

Date:
27/01/2025 - 31/01/2025

Location:
EMBL Heidelberg and Virtual (Hybrid)

Contents:
Computation is an integral part of today’s research as data has grown too large or too complex to be analysed by hand. An ever-growing fraction of science is performed computationally but many scientists struggle with this aspect of research as they have not been properly trained in the necessary set of skills. The result is that too much time is spent doing steps manually or using inefficient tools when progress could be faster. This course provides training in several key tools, with a focus on good development practices that encourage efficient and reproducible research computing.

Introduction to the Unix shell
Version control and history tracking with Git
Introduction to scripting and basics of data analysis with Python
Multi-step analysis and pipeline management with Nextflow
Best practices for reproducible analysis in high performance computing environments

Learning goals:
After the workshop, participants will be able to:

interact with a command-line shell for local and remote computing needs
write and organise their own scripts for data analysis
use version control to keep track of, and revert changes to their files
implement efficient and reproducible pipelines that combine multiple tools and scripts
execute and troubleshoot command line tools and pipelines on a high performance computing cluster

Prerequisities:
This course is aimed at researchers in the life sciences who wish to use or design analysis pipelines on individual or clusters of computers. The target learner is familiar with some command line/programmatic computer usage, and wants to become more confident using these tools efficiently and reproducibly.

If you have written a for-loop in some language before, but do not know what Git or Nextflow is (or at least are not very comfortable using them), you may be the perfect fit for this workshop.

Keywords:
Software Carpentry, Git Python, Nextflow

Tools:
Git, Python, Nextflow