Clinician Coders: Learn the basics of programming
Presented by UCL Academic Careers Office
Date: Tue 3 & Fri 6 March 2026*
Time: 9am-4pm
Suitability: All health professionals
Venue: Torrington Place Training Centre, 1-19 Torrington Place, London, WC1E 7LY
Tickets: £350
*
Attendance at both dates is mandatory
Are you planning to apply data science to your research? Would you like to do hands-on computer programming and get support from experts and peers? Then Clinician Coders is for you.
The Clinician Coders workshop is provided by the UCL Academic Careers Office. All healthcare professionals are welcome to register. Places will be offered on a first come, first served basis.
Coding language
The course uses the open-source statistical computing language
R, and a freely available application for working with R called
R studio.
• A single document, or script, encodes the steps necessary to take data from a spreadsheet form into R, and out into a simple analysis, or a beautiful data visualisation.
• When new data is entered, a single key stroke is needed to re-run the entire analysis and update the output. Sharing your work is as simple as sending a copy of your code to a collaborator.
Data
Participants will have access and gain experience working with a synthetic dataset based on Critical Care Health Informatics Collaborative data.
Content
The workshop will consist of eight sessions over two days, including time to go through your own project raw data with experienced R-savvy facilitators.
The topics covered will be:
- 1. Introduction to ‘R’ and coding for absolute beginners.
- 2. Advice on keeping a ‘clean’ dataset that is easier to analyse.
- 3. Introducing the concept of a dataframe and how to import data into ‘R’.
- 4. ‘Data wrangling’ and basic skills to sort and explore your datasets.
- 5. Visualising data. Advise on appropriate visualisation methods depending on the data.
- 6. Descriptive and basics of inferential statistics – Statistics in R primer.
- 7. Introducing control flow and looping as tools to support data analysis.
- 8. Creating, recording and reusing workflow coding – save time!
