Centre for Research Informatics Training
Computational Research Skills Programme
Clinical Academic Training Office trainees (2026-2027)
CATO is offering access to a large set of computational research skills training events, which are organised by the Centre for Research Informatics Training (CCRIT).
We have a wide range of participants at CATO, which includes trainees from the following programmes:
- Academic Clinical Fellows (ACF)
- Specialised Foundation Programme (SFP)
- Clinical Lectureships (CL),
- Nurses, Midwifes and Allied Health Professionals (NMAHP)
Registration onto these courses happens via a separate sign-up process, for which you’ll receive an individual email from CCRIT. Please note that some of the courses have prerequisites. Below is a list of courses that you have access to. The details are also given during the sign-up process, but make sure you bookmark this page for easy access afterwards.
If you are interested in further training not listed below, please review the main Events Overview or visit the CCRIT website for more information. If you find a course on the events overview that you would be interested in attending, please contact CATO directly to discuss fund approval before requesting a booking.
Please check that you have met the prerequisites for courses before requesting a booking. This is the order of courses to ensure prerequisites are met. Further details are available on each event, linked below.
- Data analysis in R (DAR) / Data analysis in Python (DAP)
- Core statistics (CS) - requires attending either DAR / DAP courses or relevant experience, confirmed to CRIT via email
- Survival analysis - requires DAR and assumes sufficient statistical knowledge or attendance on CS
- Generalised linear models (GLM) - requires attendance on CS and DAR / DAP or relevant experience, confirmed to CRIT via email
- Linear mixed effects models - requires DAR, CS and GLM or equivalent experience, confirmed to CRIT via email
For any additional information email the administration team
Information updated 2026-09-15.
| Event | Start date | Status | |
|---|---|---|---|
| Core statistics | Mon, 21 Sep 2026 | Optional | |
| Data analysis in R | Thu, 24 Sep 2026 | Optional | |
| Survival (time-to-event) analysis | Tue, 6 Oct 2026 | Optional | |
| Generalised linear models | Wed, 2 Dec 2026 | Optional | |
| Data analysis in R | Mon, 7 Dec 2026 | Optional | |
| Linear mixed effects models | Tue, 8 Dec 2026 | Optional | |
| Data analysis in Python | Wed, 9 Dec 2026 | Optional | |
| Data analysis in R | Mon, 1 Feb 2027 | Optional | |
| Data analysis in Python | Thu, 11 Feb 2027 | Optional | |
| Core statistics | Fri, 19 Feb 2027 | Optional | |
| Linear mixed effects models | Tue, 16 Mar 2027 | Optional | |
| Generalised linear models | Wed, 24 Mar 2027 | Optional | |
| Data analysis in R | Mon, 12 Apr 2027 | Optional | |
| Survival (time-to-event) analysis | Wed, 14 Apr 2027 | Optional | |
| Data analysis in Python | Thu, 15 Apr 2027 | Optional | |
| Core statistics | Tue, 11 May 2027 | Optional | |
| Survival (time-to-event) analysis | Tue, 8 Jun 2027 | Optional | |
| Data analysis in R | Thu, 10 Jun 2027 | Optional | |
| Data analysis in Python | Mon, 14 Jun 2027 | Optional | |
| Generalised linear models | Tue, 15 Jun 2027 | Optional | |
| Linear mixed effects models | Tue, 22 Jun 2027 | Optional |