Module manager: Dr. Roger Beecham
Email: r.j.beecham@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2026/27
If students have not taken GEOG1400: Digital Geographies, it is recommended that they have some understanding and demonstrable experience of quantitative data analysis methods and practice.
This module is not approved as a discovery module
It is now taken-for-granted that data and models are everywhere: in the media, in political and policy debate, in our everyday interactions and behaviour. As the volume and complexity of data increase — especially with the rise of outputs from tools like Gen-AI — the capacity to effectively interpret and communicate with data has become critical. This module equips students with the conceptual understanding and technical skills necessary to engage with this modern data landscape through visualization. Using a wide set of examples, underpinned by real social-environmental datasets, students will deploy visual methods to effectively explore complex structure in social-environmental data, to generate and evaluate exploratory statistical models on that data and to communicate quantitative findings persuasively, with integrity. Conceptual content draws from modern literature in Information Visualization and Cartography, with special focuses on uncertainty analysis and data-driven storytelling. Students will enhance their technical skills by increasing their knowledge of the R statistical programming language, enabled via computational notebooks and using accessible code templates.
The primary aim is to equip students with concepts and techniques to deploy data visualization in exploring, reasoning about and communicating with data from the social and environmental sciences. Learning activities, which consists of 1-hour lectures and 2-hour labs, are designed to:
Establish foundations in data visualization literacy: Lecture materials introduce foundational theory in data graphic design and implementation, supported by technical implementations in one of the most widely-used open source visualization toolkits in academia and industry.
Develop skills in visual data analysis: Using real-world data from across the social and environmental sciences, learning activities demonstrate how data graphics can help expose complex structure and relations that could not be achieved via non-visual means.
Integrate data visualization with modern statistical practice: Unique material will be covered on uncertainty quantification and analysis and visualization, again demonstrated through social and environmental science examples.
Inspire students to communicate data-driven stories: Covering high-profile examples, students will transition from simply using graphics as data analytic tools, to their acting as devices for communication. On completion of this module, students should be able to ...
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Reflection: The ability to recognise and express knowledge and understanding.
2. Critical thinking: The ability to weigh up different arguments and perspectives, using supporting evidence to form opinions, arguments, theories and ideas.
3. Technical/IT skills: The ability to use technology appropriately and ethically to enhance efficiency, identifying appropriate technologies for tasks.
4. Academic language: The ability to use the oral, written, auditory, and visual language proficiency needed to be able to learn effectively and demonstrate understanding.
5. Effective communication: Effectively communicates key facts and ideas, for example, verbally and in written form. Respects and considers the needs and perspectives of others. Engages in interdisciplinary discussion to inform their thinking about sustainable futures.
6. Time management: The ability to plan time, prioritise tasks and organise academic and personal commitments effectively.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Identify the characteristics of effective data graphics and explain design choices, using established theory in Cartography and Information Visualization.
2. Deploy core data visualization techniques to explore and reveal complex structure and relations in social and environmental datasets.
3. Appreciate how statistics and data graphics can be used in tandem to advance a data analysis and address real-world research questions.
4. Understand how modern, empirical statistics and data visualization methods can be used to quantify and represent uncertainty, and critically evaluate different approaches to communicating that uncertainty.
5. Leverage visual narrative devices in order to communicate data-driven stories, with integrity and empathy, navigating the difficult tension between acknowledging complexity and communicating a clear message.
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 1 | 10 |
| Practical | 10 | 2 | 20 |
| Private study hours | 170 | ||
| Total Contact hours | 30 | ||
| Total hours (100hr per 10 credits) | 200 | ||
Recap quiz at the start of each lecture
Continuous feedback and individual support during labs
Labs have dedicated assignment drop-in session
Post assignment 1 generic class feedback
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 30 |
| Coursework | Coursework | 70 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team