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2023/24 Undergraduate Module Catalogue

COMP3736 Information Visualization

10 creditsClass Size: 180

Module manager: Prof Roy Ruddle

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2023/24


COMP2811User Interfaces

This module is not approved as a discovery module

Module summary

Covers the principles of visualisation in decision making and knowledge discovery. You will learn how to design new visual representations and how to modify and apply existing tools to gain new insight about data.


On completion of this module, students should be able to:
- Understand the use of visualization in decision-making and knowledge discovery;
- Understand core technical concepts including data and processing models;
- Understand salient features of human perceptual and cognitive processing;
- Design new visual representations appropriate for a given task;
- Modify and apply visualization tools to obtain insight about a given dataset;
- Understand how to evaluate visualizations.

Learning outcomes
On completion of the year/programme students should have provided evidence of being able to:
1. understand and demonstrate coherent and detailed subject knowledge and professional competencies some of which will be informed by recent research/scholarship in the discipline;
2. deploy accurately standard techniques of analysis and enquiry within the discipline;
3. demonstrate a conceptual understanding which enables the development and sustaining of an argument;
4. describe and comment on particular aspects of recent research and/or scholarship;
5. appreciate the uncertainty, ambiguity and limitations of knowledge in the discipline;
6. make appropriate use of scholarly reviews and primary sources;
7. apply their knowledge and understanding in order to initiate and carry out an extended piece of work or project;


- Overview of visualization, scientific visualisation, information visualisation, visual analytics; big data;
- Visual design, examples of both appropriate and inappropriate representation;
- Perception: the eye, light and colour, pre-attentive processing, gestalt phenomena;
- Cognition, tasks and knowledge, semiotics of representations;
- Different types of data, and the link between data, task, and representation;
- Technical and user evaluation of visualization;
- Visualization as process, with Shneiderman's mantra and the need for interaction;
- Visual analytics for finding patterns in huge datasets;
- Practical visualization using tools such as excel and Tableau, provided through the Tableau for teaching program.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours80.00
Total Contact hours20.00
Total hours (100hr per 10 credits)100.00

Private study

Taught session preparation: 20 hours
Taught session follow-up: 20 hours
Self-directed study: 13 hours
assessment activities: 23 hours

Opportunities for Formative Feedback

Practical work and formative assessment.

Methods of assessment

Assessment typeNotes% of formal assessment
AssignmentCoursework 125.00
AssignmentCoursework 2 (in Groups)50.00
Total percentage (Assessment Coursework)75.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated.

Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)1 hr 30 mins25.00
Total percentage (Assessment Exams)25.00

The Exam will be a Computer-Based Exam. This module will be reassessed by Computer-based exam.

Reading list

The reading list is available from the Library website

Last updated: 26/09/2023


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