COMP30750 Information Visualisation -DS

Academic Year 2022/2023

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:

On completion of this module, the learner will be able to

* Demonstrate an understanding of how humans perceive the world around them on a general level, and absorb complex data/information on a specific level

* Understand and be able to apply principles involved in information visualization

* Understand and be able to apply a variety of existing techniques and methods in information visualization

* Provide a research survey of an area of information visualisation based on relevant research articles

* Critically evaluate different visualization techniques as applied to particular tasks

* Understand be able to demonstrate the design of new and innovativevisualization methods

* Understand the programming constructs needed to encode information visualisation algorithms

* Design and implement programs using these constructs to solve complex problems

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


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Continuous assessment exercises performed throughout the duration of the module Varies over the Trimester n/a Graded No

100

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer 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