Building Data Science Capability Across 100+ Researchers at the Francis Crick Institute
The Francis Crick Institute
Biomedical Research
Overview
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
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, and Why They’ve Stayed
The Institute has continued working with Jumping Rivers 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
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.
Training delivered across the partnership has included:
- Introduction to R and Python programming
- Programming with R and Python for applied research
- Data wrangling using the Tidyverse
- Data visualisation with ggplot2 and Python tools
- Advanced statistical modelling in R
- Advanced R object-oriented programming
- Advanced data visualisation and graphics in R
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
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 |
|---|---|
| Uneven programming capability across research teams | Structured onboarding training embeds best practices from day one |
| New starters reliant on ad-hoc self-learning to get up to speed | Faster time-to-competence for new and existing researchers |
| Limited ability to apply advanced analytical methods independently | Researchers independently applying advanced statistical methods and exploring new research approaches |
| Inconsistent tooling and workflows across teams | Repeatable training model supporting onboarding and continuous development across cohorts |
Work With Us
If your organisation needs to build long-term data science capability across teams, skill levels, and research domains, Jumping Rivers designs and delivers training programmes that grow with you. Get in touch to talk through what that could look like for your teams.