Good data visualisation is of key importance in research for initially exploring and analysing your data, and later for communicating your results to various audiences; however, in many disciplines, the basics of good visualisation are not taught.
In this course, we use the programming language Python as a tool to introduce you to some key concepts of good data visualisation to support your research. We will create a selection of common plot types for different data types using popular Python libraries and open datasets.
Course content
In this one-day course, we will discuss:
- Ten rules for good scientific data visualisation;
- Tailoring your graphics to your audience, presentation medium, and message, including creating publication-quality figures;
- Improving accessibility of your visualisations;
- Avoiding misleading visualisations and unintentional communication;
- Delving into Python documentation
- Using external visualisation libraries such as Matplotlib and Seaborn;
- Writing modular and reusable visualisation scripts.
Suitability
Research postgraduate students and above from all research domains with some Python experience. Please see the prerequisite section for an indication of the required Python knowledge.
Prerequisites
Basic experience in Python (e.g., having attended SWD1a: Introduction to Python programming or being comfortable with the topics covered in the course).
Duration
1 full-day session
Materials
Our SWD7 materials are available for self-paced learning here: https://arctraining.github.io/swd7-12-2025
These materials are regularly updated to reflect best practices and user feedback.
How to Book
Booking for this course is through the IT Training Unit (login required).
Bespoke Courses
Want us to deliver a bespoke version of this course in your department?
Submit a Research Computing Query (login required) and we’ll be in touch to discuss your requirements.
