CSC8626 : Data Visualization

Semester 1 Credit Value: 10
ECTS Credits: 5.0


Data visualization systems create visual representations of datasets designed to help people carry out tasks more quickly or more effectively. These systems form a critical link between data analytics outputs and the human perception and cognition of the meaning of those outputs.

The aim of this module is to introduce students to the theoretical underpinnings of the subject and allow them to build skills in the practice of creating data visualizations.

Outline Of Syllabus

The syllabus will cover topics from:
•       Data abstraction – the types and semantics of data.
•       Task abstraction – the uses for and targets of visualization.
•       Human perception and cognition and how it influences the design of visual representations of data.
•       Approaches to the visualization of categorical, ordinal and numerical data.
•       The visualization of geographic and time series data.
•       Dashboards, reasons for creating, layout design.
•       Critical evaluation of the speed and effectiveness of visualization techniques.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion161:0016:00Two short formative exercises
Scheduled Learning And Teaching ActivitiesLecture71:007:00Lectures
Guided Independent StudyAssessment preparation and completion10:300:30Oral Examination
Guided Independent StudyAssessment preparation and completion40:302:00Preparation for oral examination
Guided Independent StudyDirected research and reading201:0020:00Background reading
Guided Independent StudyDirected research and reading71:007:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesPractical171:0017:00Practical sessions
Guided Independent StudyProject work301:0030:00Main assessed project
Scheduled Learning And Teaching ActivitiesDissertation/project related supervision20:150:30Up to two 15 min drop-in sessions
Teaching Rationale And Relationship

Lectures explain the underpinning principles for the module and technologies that support data visualisation. Lectures are complemented by supervised practical sessions to guide the application of these principles using suitable computational tools. The practical work builds up experience working with a computational toolset that is used to complete a substantive project that is documented in a report that constitutes the main submission for the module.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Written report details the design, implementation & evaluation of the visualisation. Word count: up to 2,000 words
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral ExaminationMStructured discussion inc. a software demonstration and reflection on the key learning objectives of the project work-up to 20 mins
Assessment Rationale And Relationship

The report tests the students’ ability to apply visualisation techniques to solve a problem to a given specification. The semi-structured interview facilitates a reflective discussion about how individual students have met the learning objectives of the module and how the principles of visualisation are embedded in the functionality of the application they developed.

Reading Lists