Visualisation facilitates the transformation of data into knowledge. With ever-increasing quantities of data we require supporting tools to help us make sense of, and create value from, the raw information at our disposal.
Data Visualisation is multidisciplinary, drawing upon several different areas of computer science (e.g. psychology, statistics, data mining, graphic design, information visualisation) to deliver meaningful solutions. This module provides students with a brief introduction to the theories underpinning data visualisation, best practice in using visualisations effectively, and practical skills in creating visualisations from datasets.
The emphasis of the module is human-centred rather than machine-centered as a central challenge in visualisation is choosing/designing the best visual interface for a task (as dictated by the expected audience).
As a foundational step, learning theories, cognitive science and epistemology will be briefly reviewed: how humans perceive the world; how we make sense of what we perceive; how we absorb information; how to interpret meanings in visualisations; and how we learn and memorise what we have perceived.
Lastly, this module will provide a practical introduction to the tools and techniques of data visualisation. Through practical instruction, labs and tutorials, students will be equipped to successfully implement some data visualisation techniques.
1. Overview/Fundamentals: The value and purpose of data visualisation
2. History and case-studies in data visualisation
3. Perception/Memory: graphical perception, visual communication
4. Data: Characteristics & dimensions, data and image models
5. Storytelling through visualisation, Visualization Design, ethical visualisation
6. Identifying Design Principles,
7. Accessibility in visualisation (multi-modalities)
8. Colour Theory for enhancing visualisations
9. Using Space Effectively for storytelling
10. Geo-spatial Visualization13. Interaction/Multiples
11. 3D data visualization and 3D interactive interfaces
12. Tools for Visualization
The learning methods used to achieve the module learning outcomes will involve a combination of lectures, discussions, case studies, problem-solving exercises, work-based learning, readings, project work, self-directed learning, computer-based learning and video.
Formal lectures will be balanced with labs and student participation. Students will be introduced to computer-based data visualisation tools and techniques and then expected to show initiative in acquiring skills necessary. Students will be expected to put theory into action by completing tutorials, exercises and independent experiments using a variety of data visualisation tools.
Lab based assignments will focus on providing practice using real-world datasets and real world problems.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 50 |
| Other Assessment(s) | 50 |