Application Deadline: 25th May 2026

Duration: 4 Full Days on campus.  Saturdays 10am - 4pm (10th, 17th, 31st October 2026, 7th November 2026)

What is Data Visualisation?

Apply the principles of data visualisation to the analysis, design and communication of information in order to provide insights, clarity and meaning.

The course is suitable for individuals involved in the collection, analysis or interpretation of data, as well as for those responsible for the communication of data to a range of audiences.

The course will use the data visualization application Tableau. In addition, the open source software environment R together with a number of its data visualization libraries including ggplot2, Plotly and the mapping library Leaflet will be used. No prior experience with Tableau or R will be assumed.

On successful completion of this course, you will be able to:
1. Identify and critically appraise the history of data visualisation, the work of key practitioners and the emerging themes and critical debates relating to a data-driven society.
2. Apply the grammar of graphics and appreciate its role in the design of appropriate visuals.
3. Create graphics that visualise data in one, two and higher dimensions using proprietary and open source software tools.
4. Explore the emerging role of geospatial analysis in data analysis and develop interactive map-based visualisations.
5. Appreciate the role of statistical reasoning in data analysis and develop surveillance models for visualising data collected over time.

This course is intended for students who:
• have successfully completed a primary degree at level 8 and are interested in learning more about the field of Data Visualisation, and/or
• may be working in a related field related to data science, information design or graphic design, and/or
• want to retrain or upskill in order to start a career the field of Data Visualisation.

Students may come from a range of backgrounds, with varying levels of prior design experience (e.g., computing, engineering, technology, business, marketing, design, creative arts, psychology, etc. graduates) for whom data visualisation is a key area of interest.

What will I study?

Topics covered in this course include:
• The grammar of graphics
• Visualising data in one, two and higher dimensions.
• Geospatial visualisation
• Interactive visualisation
• Dashboard design and development
• Data visualisation using open source software e.g., R.
• Review of key concepts associated with statistical reasoning
• Creating surveillance models based on data collected over time
• Publishing content using an open source framework.

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