Centre for Research Informatics Training
Visual data communication
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Event details
Sessions
- 15 January 2027 — 09:30 to 13:00 — Craik-Marshall
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About the course
A practical introduction to the principles of effective data visualisation and visual communication for researchers working with quantitative data.
This course teaches participants how to explore their data, identify important patterns, and present their findings through clear, informative visualisations using R or Python. The emphasis is on communicating scientific messages rather than simply producing attractive figures.
Participants learn how visualisation supports data exploration, how to select appropriate graphical representations for different types of data, and how to design figures that clearly answer research questions. Throughout the course, the focus is on developing a narrative from the data, mirroring the process of preparing figures and results for scientific publications.
By the end of the course, participants should be able to:
- explore datasets to identify important patterns and relationships
- create clear and effective visualisations using R or Python
- choose appropriate graphical representations for different types of data
- critically assess the strengths and weaknesses of data visualisations
- communicate research findings through figures that support a coherent scientific story
Teaching combines discussion of visual communication principles with practical, hands-on exercises. The course can be delivered as a half-day concepts-only session or as a full-day workshop, where participants apply the techniques to their own research data.
Intended audience
This course is suitable for:
- researchers and students who communicate quantitative research findings
- participants who wish to improve the clarity and impact of their figures
- people using R or Python for data analysis who want to strengthen their data visualisation skills
- anyone preparing figures for reports, presentations, or scientific publications
Prerequisites
Participants should have a working knowledge of either R or Python, ideally equivalent to completion of a Data Analysis in R or Data Analysis in Python course.
This is not an introductory programming course.
Related courses:
- Data analysis in R
- Data analysis in Python
Course fees
All fees are per full training day
| Category | Fee |
|---|---|
| Industry full charge | £130.00 |
| Academic / Government / charity concessionary | £65.00 |
| Cambridge University staff members / postdocs / visitors | £65.00 |
| Cambridge University registered students | Free |
| Special events | Per event |
Payment options will be provided in booking confirmation emails sent after registration.