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CSC8636 : Complex Data Visualization

  • Offered for Year: 2023/24
  • Module Leader(s): Dr Sara Fernstad
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus
Semester 2 Credit Value: 10
ECTS Credits: 5.0


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 experimental evaluation of visualization approaches. Through project work, students will experience the full lifecycle from design of interactive visualization to experimental 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:
Design studies;
Experimental design;
Usability studies.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture81:008:00Lectures delivered as PIP (with underpinning online material).
Guided Independent StudyAssessment preparation and completion40:302:00Preparation for oral examination
Scheduled Learning And Teaching ActivitiesPractical181:0018:00Practical sessions in computer lab, with set exercises and coursework support
Guided Independent StudyDirected research and reading391:0039:00Lecture follow-up and background reading
Scheduled Learning And Teaching ActivitiesSmall group teaching11:001:00Oral Examination, PIP in small groups.
Guided Independent StudyProject work301:0030:00Main assessed project
Guided Independent StudyReflective learning activity40:302:00Formative exercise and peer assessment
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 zero weight exercise builds on the lecture work, and peer assessment develops reflection and critical thinking around the application of visualization principles. The practical work builds up experience working with advanced visualization challenges using 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
Report2M100Written report details the design of visualization analysis + reflection on alternative approaches based key learning Max 2,000 word
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral ExaminationMStructured discussion + Presentation including demonstration +reflection on the key learning objectives of project work (max 10 min)
Assessment Rationale And Relationship

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

Reading Lists