Module: CPU5100-20 - Data Visualisation

Level: 5

Credit Value: 20

Module Tutor: Ron Herrema

Module Tutor Contact Details: r.herrema@bathspa.ac.uk

1. Brief description and aims of module:

Data visualisation uses a range of visual design methods to communicate information in a way that is easy to digest. We encounter forms of it everyday – in journalism, in the classroom, while keeping fit - and indeed rely on it to remain informed and to make decisions. Data visualisation continues to increase in importance across many sectors, given its capacity to construct narratives around data and help derive insights from complex data sets. Today there are many vendors within the data visualisation space (particularly related to business intelligence) that leverage a range of representation methods to facilitate data analysis. Furthermore, data visualisation as a form of storytelling – and in some cases, a means to democratise data - is becoming more commonplace in advertising, politics and education, leading to numerous employment opportunities for talented information designers.

This module aims to equip you with the contextual awareness, design understanding and technical skills needed to create convincing data visualisations. You take a content-led approach, which begins with considering how humans interpret data visualisation and by comparing the principles and approaches of leading information designers. You then explore practically the tools and design concepts behind effective data visualisation. Here you negotiate the relationship between function and form, evaluate the benefits of embedding interactivity, and apply methods of assessing the value of your data visualisation via audience feedback.

2. Outline syllabus:

Outline syllabus

• Historical underpinnings and contemporary approaches

• Data visualisation in industry

• How humans interpret data: knowing your audience

• Data visualisation and story (integrity, meaning, function, form)

• Truth, evidence and misrepresentation

• Interpreting datasets: knowing your content

• Tools for data visualisation

• When and how to embed interactivity

3. Teaching and learning activities:

Class Hours

The module includes a lecture component that introduces underlying concepts and principles, and supporting lab sessions to allow for application of specific tools and techniques. A series of database modelling, implementation and querying tasks are undertaken to audition the skills required to complete summative assessments.

Independent Learning

It is recommended that you develop an appropriate workflow on a personal computer to enable you to enhance your understanding of database design and implementation outside of class hours.

Assessment Item 1:

• Assessment Type: Course Work

• Description: Data visualisation review

• % Weighting: 25%

Assessment Item 2:

• Assessment Type: Course Work

• Description: Data visualisation collection with annotations

• % Weighting: 75%