Postgraduate taught 

Data Science MSc

Information Visualisation (M) COMPSCI5099

  • Academic Session: 2023-24
  • School: School of Computing Science
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No

Short Description

This course is a general introduction to the field of Information Visualisation, covering visualisation and analytic techniques, and current research in the area.



Excluded Courses





Students will sit an 70% class test. The coursework will entail the implementation and evaluation of a working information visualisation system, the deliverable being a written report that describes and justifies the system design, and presents the results of the evaluation (20%). Students will also be required to produce a critical literature survey of research in Information Visualisation (10%).

Course Aims

This course aims to introduce the broad field of Information Visualisation, with reference to theories of data abstraction and visual perception, current research in the area, and evaluation methods. The theoretical component of the course will be complemented by a practical exercise where students will implement and evaluate an information visualisation system. 

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Discuss the purposes and range of visualisation techniques;

■ Identify different data types and relate them to different visualisation techniques;

■ Explain how theories of visual perception relate to visualisation techniques;

■ Implement a visualisation system, justifying its design;

■ Evaluate a visualisation system with reference to specified data exploration tasks;

■ Describe, in general terms, the scope of information visualisation research, and critique current research papers in the area.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including class tests) of the course's summative assessment.