Case study
Francis Crick Institute: Training 500+ Researchers in R and Python
How a four-year R and Python training partnership supports data-driven biomedical research.
- 4+ years of continuous partnership
- 500+ researchers trained
- 50+ courses delivered
Overview
Training partner to the Francis Crick Institute
The Francis Crick Institute is one of Europe’s leading biomedical research organisations, bringing together scientists working across genetics, statistics, and experimental biology. The Institute runs at a scale and pace where programming skills are not optional. It is infrastructure.
Jumping Rivers has been the Institute’s training partner for over four years, delivering a structured R and Python programme that has grown and adapted alongside the research teams it supports.
The challenge
Building programming skills across a large research institute
Biomedical research increasingly depends on data-intensive methods, but building programming capability across a large, multidisciplinary organisation is harder than it sounds. Researchers join at different skill levels. Teams have different tools, timelines, and priorities. And the work doesn’t stop while people upskill.
The Institute needed training that was technically rigorous, immediately applicable to their research workflows, and flexible enough to evolve as priorities shifted. Off-the-shelf courses, built for broad audiences, were not going to cut it.
Why Jumping Rivers
Why they've stayed
The Institute has continued working with us for over four years, not because of inertia, but because the training keeps earning its place. The courses are practical, precise, and built around the topics researchers are actively working on. When needed, content is customised to reflect biological data, specific research interests, or the skill levels of a particular cohort.
That kind of fit takes time to build. It comes from four years of iteration, feedback, and genuine alignment with how research teams work. A course catalogue cannot replicate it.
What we delivered
A structured learning pathway in R and Python
Jumping Rivers designed and delivered a long-term training programme spanning both R and Python, structured to support progression from foundations through to advanced topics, as part of a structured learning pathway.
As researchers at the Crick have upskilled, the programme has kept pace, expanding into more advanced analytical techniques, machine learning, Docker, modern workflows, and deeper methodological understanding.
R track
Foundations
Introduction to R
Take your first steps in using R with this introductory-level course. We'll demonstrate how to import, explore, modify and visualise your data using R.
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Core skills
Programming with R
This course covers fundamental techniques all R programmers should know. We cover creating functions, for loops and conditional expressions.
View the courseData Wrangling in the Tidyverse
Learn data wrangling in R using powerful Tidyverse toolkits. This course covers data manipulation, dealing with dates and times in R, cleaning data and dealing with factors.
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Advanced
Statistical Modelling with R
This course covers statistical techniques such as hypothesis testing, regression analysis, clustering and principal components analysis (PCA).
View the courseObject Oriented Programming in R
Interested in learning more about advanced functions, S3 and S4 classes? This course explores it all and gives us a sneak peak into the new {R6} package, used extensively in Shiny applications.
View the courseData Visualisation with ggplot2
Learn how to create beautiful data visualizations with the highly popular {ggplot2} package in this comprehensive course. Perfect for data analysts!
View the course- Introduction to Docker for R
Python track
Foundations
Introduction to Python
An introductory Python course on importing, summarising and visualising data. In this course you will gain familiarity with the popular Pandas and Matplotlib packages for data analysis and visualisation in Python
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Core skills
Programming with Python
This course covers fundamental Python techniques such as functions, loops and conditional expressions. After covering the theory, you will learn how to apply these techniques to real-world data wrangling tasks.
View the courseData Visualisation with Python
This course examines two popular Python libraries used for creating data visualisations: Matplotlib and Seaborn. We'll cover plotting basics, customisation of figures and complex statistical visualisations.
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Advanced
Python and Tensorflow
This course covers the main ideas of deep learning, working up from linear regression to fully realised neural networks. After covering the theory, you will learn how to apply these techniques with tensorflow.
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How it's delivered
Hands-on sessions built on research data
Courses have been repeated across cohorts to support new starters and reinforce learning across teams. Sessions are instructor-led, with live coding and discussion throughout. Materials and examples are aligned with biomedical and genetic research, not generic datasets.
To support consistent delivery, Jumping Rivers also provided a managed cloud-based training environment for all sessions, Jupyter notebooks and course materials, and custom training booklets aligned with research use cases. This infrastructure keeps setup time low and ensures every session runs reliably regardless of local machine configurations.
Impact
Researchers working confidently with data from day one
The most measurable shift has been in how new starters come into their roles. Researchers now receive structured training early, which means they engage with data more confidently from day one.
The time required to reach independent working has shortened, the need for ad hoc self-learning has reduced, and best practices are embedded earlier in a researcher’s career rather than picked up gradually or not at all.
Beyond individual upskilling, the programme has also supported more consistent analytical practices across teams. Researchers are now better equipped to apply new methods independently, explore alternative research approaches, and work confidently with complex datasets. That expands both the scope of questions they can tackle and the rigour of the methods they can apply.
Before
After
Before: Uneven programming capability across research teams
After: Structured onboarding training embeds best practices from day one
Before: New starters reliant on ad-hoc self-learning to get up to speed
After: Faster time-to-competence for new and existing researchers
Before: Limited ability to apply advanced analytical methods independently
After: Researchers independently applying advanced statistical methods and exploring new research approaches
Before: Inconsistent tooling and workflows across teams
After: Repeatable training model supporting onboarding and continuous development across cohorts
