Data Visualisation (20 credits)
| Code | School | Level | Credits | Semesters |
| COMP3087 | Computer Science | 3 | 20 | Spring UK |
- Code
- COMP3087
- School
- Computer Science
- Level
- 3
- Credits
- 20
- Semesters
- Spring UK
Summary
Data Visualisation is an important part of the Data Science toolbox. It complements computational analysis methods, such as statistics and machine learning, and integrates human capability (visual cognition and analytical reasoning) and domain expertise (such as medical knowledge) to solve complex data science problems. Data visualisation is an interdisciplinary research field that is at the intersection of Computer Graphics, Human-Computer Interactions, Data Analysis, and closely related to Cognitive Psychology and Graphics Design. This module provides the foundation and practical skills for effective Data Visualisation design. You will learn about the different approaches to constructing visualisations, including the various representations for different types of data, including relational/tabular, time-series, and graph/network data. You will also learn about cognitive and visual perception theories that help explain how humans process information visually, and how to apply these to the design of effective visualisations. On the practical side, the module will cover the popular tools for data processing and interactive visualisation. All these will be applied to and assessed in a visual data analysis project and an in-lab quiz.
Target Students
Third-year BSc and MSci students and MSc students within the School of Computer Science.
Assessment
Assessed by end of spring semester
Educational Aims
To develop student's knowledge and skills in designing and implementing effective data visualisations.Learning Outcomes
To explain and apply the foundational theories and framework for data visualisation.
To apply the main principles of effective visualisation design and generate and analyse related empirical results.
Use visualisation and interaction techniques.
Choose appropriate visualisation techniques for their targeted data type and user task.
Use practical tools for data pre-processing and analysis.
Use practical tools for implementing interactive data visualisation.