COMP30750 Information Visualisation -DS

Academic Year 2023/2024

This module is suitable for students interested in the fundamental and practical underpinnings of Information Visualisation. Information Visualisation is a research area that focuses on the use of graphical techniques to present data in an explicit form. Such static or dynamic presentations (pictures) help people formulate an understanding of data and an internal model of it for reasoning about. Such pictures of data are an external artifact supporting decision making. While sharing many of the same goals of Scientific Visualisation, Human Computer Interaction, User Interface Design and Computer Graphics, Information Visualisation focuses on the visual presentation of data without a physical or geometric form. As such it relies on research in mathematics, data mining, data structures, algorithms, graph drawing, human-computer interaction, cognitive psychology, semiotics, cartography, interactive graphics, imaging and visual design.

In this course we focus on major Information Visualisation research challenges which include:

* The ability to bring the human and computer together with interactive visualisations that couple the flexible pattern finding and adaptive decision-making human system with the computational power of networked computers coupled with their vast information resources.

* The ability to design a cognitively useful spatial mapping of a data set that is not inherently spatial.

* The ability to rely on the human visual system to perceive and process vast information and data sources.

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Curricular information is subject to change

Learning Outcomes:

1. Demonstrate an understanding of human visual perception & how it can be exploited to design effective visualisations
2. Identify visualisation approaches suitable for specific data types (including tabular data, spatial data, and network data).
3. Critically evaluate different visualisation approaches as applied to particular tasks
4. Implement interactive visualisation approaches using a programming language

Student Effort Hours: 
Student Effort Type Hours
Lectures

22
Computer Aided Lab

14
Autonomous Student Learning

75
Total

111
Approaches to Teaching and Learning:
Lectures
Labs
In class group activities
In class group discussion
Assigned reading
Assignments 
Requirements, Exclusions and Recommendations
Learning Requirements:

Students should have previously successfully completed the module “COMP30760: Data Science in Python - DS”.


Module Requisites and Incompatibles
Incompatibles:
COMP40610 - Information Visualisation, COMP47970 - Information Visualisation BD


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Designing and explaining a visualisation tool or series of visualisations Unspecified n/a Alternative linear conversion grade scale 40% No

60
Class Test: In class test Unspecified n/a Alternative linear conversion grade scale 40% No

40

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Autumn No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Name Role
Narod Kebabci Tutor
Hesam Nejati Sharif Aldin Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 23, 24, 25, 26 Mon 14:00 - 15:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26 Tues 14:00 - 15:50
Laboratory Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26 Wed 11:00 - 12:50
Spring