Module Catalogue 2025/26

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

Data visualization can be described as the communication of complex data through the use of interactive visual interfaces. It is an increasingly important part of data science and aims to bridge the gap between the human and data, by supporting human perception and cognition to make sense of data analytics outputs.

The aim of this module is to familiarise students with the theoretical underpinnings of visualization techniques for complex data.

This module will introduce the research methodology which underpins visualization design and evaluation of visualization approaches.

Through project work, students will experience the full lifecycle from design of interactive visualization to evaluation of an advanced visualization approach.

Outline Of Syllabus

The syllabus will cover topics from:

• How, why and when to use visualization.

• The human in the loop – how human perception and cognition influence visualization design.

• Approaches to the visualization of complex data, including: heterogeneous, categorical, ordinal and numerical
data; multivariate and high dimensional data; uncertainty and incomplete data.

• Approaches for interactive and multiple coordinated views.

• Research methodology and experimental evaluation including validation; Design studies; Experimental design
and usability studies.

Intended Knowledge Outcomes

Knowledge of key topics in data visualization from:

• How, why and when to use visualization.

• The human in the loop – how human perception and cognition influence visualization design.

• Approaches to the visualization of complex data, including: heterogeneous, categorical, ordinal and numerical
data; multivariate and high dimensional data; and uncertainty and incomplete data.

• Approaches for interactive and multiple coordinated views.

• Research methodology and experimental evaluation, including validation, design studies, experimental design,
usability studies.

Intended Skill Outcomes

Demonstrate an ability to:

• Select appropriate visualization approaches for common types of multivariate data.

• Implement visualization using a current visualization toolset.

• Layout and implement a multiple views visualization displaying different aspects of data.

• Choose appropriate evaluation and methodology to ensure good performance and usability of a visualization.

Teaching Rationale And Relationship

Lectures explain the underpinning principles for the module and technologies that support visualization.

Lectures are complemented by practical sessions to guide the application of these principles using suitable computational tools.

The practical coursework builds up experience working with advanced visualization challenges using a computational toolset that is used to complete a substantive project. The practical work is documented in a reflective report that constitutes the main submission for the module.

The report builds on the lecture work and develops reflection and critical thinking around the application of visualization principles.

Assessment Rationale And Relationship

The formative assessment builds on the lecture work and provide opportunities for students to test and develop their understanding of the taught material.

The summative assessment then tests the students’ ability to apply visualization techniques to solve a problem to a given specification.

The report, which is part of the summative assessment, facilitates and develops reflection and critical thinking around the application of visualization principles and theory, and how they are embedded in the functionality of the application they have developed, as well as critical evaluation of their own work.

General Notes

N/A