Foundations of Data Science — Editorial Board Home

This is a new, experimental "Editorial Board Home" for a given topic. It is intended to provide a single place for maintainers and editorial board members to find out key information about their topic and identify action items.

Editorial Board

orcid logoFotis E. Psomopoulos avatar Fotis E. Psomopoulosorcid logoYvan Le Bras avatar Yvan Le Brasorcid logoBérénice Batut avatar Bérénice Batutorcid logoHelena Rasche avatar Helena Rascheorcid logoSaskia Hiltemann avatar Saskia Hiltemann

Action Items

Item Status Why you should do this
Summary Done ✅ Provide a sufficiently detailed summary of the topic to let learners know what they're learning about in this topic.
Sufficient Editorial Board Members Done ✅ (5 members) Having multiple people sharing the burden of being responsible for a specific topic can reduce board member burn-out in the long term.
Enable Subtopics Done ✅ Subtopics help organize the content and make it easier to navigate.
Annotate Funders Done ✅ (4 funders) By annotating the funders of your topic's materials, you make it easier to write your grant reports later
Learning Pathway CTA Pending ❌ By providing a Learning Pathway CTA, we can help guide learners to the best resources for learning about this topic.
Review Feedback Received Ongoing 🔁 Review all feedback received for the Foundations of Data Science topic and create GitHub issues or PRs where needed.
Review open Pull Requests Ongoing 🔁 View all open PRs in the Foundations of Data Science topic and review, comment and merge!
Review open GitHub Issues Ongoing 🔁 View all open issues related to the Foundations of Data Science topic and comment, fix, or ask others for help!

Topic Materials

Material Contributions v2 Pre-requisites Follow up trainings Data on Zenodo Notebook Server Compatibility
Data visualisation Olympics - Visualization in R ✅ ✅ ✅
Data Manipulation Olympics - SQL ✅ ✅ ✅
Data Manipulation Olympics - JQ ✅ ✅
Data Manipulation Olympics ✅ ✅
Advanced CLI in Galaxy ✅ ✅ ✅ ❌ ✅
CLI Educational Game - Bashcrawl ✅ ✅ ✅ ❌ ✅
CLI basics ✅ ✅ ✅ ❌ ✅
Introduction to Python ✅ ✅ ✅ ✅ ✅
Python - Warm-up for statistics and machine learning ✅ ✅ ✅ ✅
Advanced Python ✅ ✅ ✅ ✅ ✅
Plotting in Python ✅ ✅ ✅ ✅ ✅
Python - Math ❌ ✅ ✅ ✅ ✅
Python - Functions ❌ ✅ ✅ ✅ ✅
Python - Basic Types & Type Conversion ❌ ✅ ✅ ✅ ✅
Python - Lists & Strings & Dictionaries ❌ ✅ ✅ ✅ ✅
Python - Flow Control ❌ ✅ ✅ ✅ ✅
Python - Loops ❌ ✅ ✅ ✅ ✅
Python - Files & CSV ❌ ✅ ✅ ✅ ✅
Python - Try & Except ❌ ✅ ✅ ✅ ✅
Python - Introductory Graduation ❌ ✅ ✅ ✅ ✅
Conda Environments For Software Development ✅ ✅ ✅ ✅ ✅
Python - Argparse ✅ ✅ ✅ ✅
Python - Testing ✅ ✅ ✅ ✅ ✅
Python - Type annotations ✅ ✅ ✅ ✅ ✅
Virtual Environments For Software Development ✅ ✅ ✅ ✅ ✅
Python - Globbing ❌ ✅ ✅ ✅ ✅
Python - Subprocess ❌ ✅ ✅ ✅ ✅
Python - Coding Style ✅ ✅ ✅ ✅ ✅
Python - Multiprocessing ✅ ✅ ✅ ✅ ✅
R basics in Galaxy ✅ ✅ ✅ ❌
Advanced R in Galaxy ✅ ✅ ✅ ❌
dplyr & tidyverse for data processing ✅ ✅ ✅ ✅ ✅
Advanced SQL ❌ ✅ ✅ ✅ ✅
Introduction to SQL ❌ ✅ ✅ ✅ ✅
SQL Educational Game - Murder Mystery ✅ ✅ ✅ ❌ ✅
SQL with Python ❌ ✅ ✅ ✅ ✅
SQL with R ❌ ✅ ✅ ✅ ✅
Make & Snakemake ❌ ✅ ✅ ✅
A (very) brief history of genomics ✅ ✅
Introduction to sequencing with Python (part one) ✅ ✅ ✅
Introduction to sequencing with Python (part two) ✅ ✅ ✅
Introduction to sequencing with Python (part three) ✅ ✅ ✅
Introduction to sequencing with Python (part four) ✅ ✅ ✅
Data manipulation with Pandas ✅ ✅ ✅
Versioning your code and data with git ✅ ✅
Basics of using Git from the Command Line ✅ ✅ ✅
Learning about one gene across biological resources and formats ✅ ✅
One protein along the UniProt page ✅ ✅ ❌
Variant Calling Workflow ✅ ✅
Version Control with Git ✅ ✅

Topic Workflows

Material Workflow Updated Version Tests Reports Comments
Data Manipulation Olympics GTN Tutorial: Data manipulation Olympics - all steps and exercises Sep 28, 2026 1 ❌ ❌ ❌

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Learning Pathways using materials from this Topic

GTN logo with a multi-coloured star and the words Galaxy Training Network
From R to Machine Learning; an introductory course

This learning path aims to teach you the basics of Machine Learning using R. Initially you will learn how to code in R within the Galaxy platform, gaining some familiarity into how to wrangle and analyze data. Then, you will be guided through the various types of Machine Learning techniques, developing some simple models using R.

beginner statistics data-science
ELIXIR logo
Artificial Intelligence and Machine Learning in Life Sciences using Python

Artificial intelligence (AI) has permeated our lives, transforming how we live and work. Over the past few years, a rapid and disruptive acceleration of progress in AI has occurred, driven by significant advances in widespread data availability, computing power and machine learning. Remarkable strides were made in particular in the development of foundation models - AI models trained on extensive...

elixir ai ml
GTN logo with a multi-coloured star and the words Galaxy Training Network
Introductory Python

This is an introductory course of Python, as it was taught in Avans Hogeschool in the Netherlands.

python real-course data-science
GTN logo with a multi-coloured star and the words Galaxy Training Network
Introductory SQL

This is an introductory course of SQL, as it was taught in Avans Hogeschool in the Netherlands.

python real-course data-science
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Events using materials from this Topic

TODO once this is merged: https://github.com/galaxyproject/training-material/pull/4963

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