COMP40610 Information Visualisation

Academic Year 2021/2022

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.

This module explores the most important topics in information visualsiation addressing both practical and theoretical concerns. Specific tools for creating data visualsiations are also explored.

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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

* Design new and innovative visualization 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
Autonomous Student Learning

75

Lectures

24

Laboratories

16

Total

115

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

Prior knowledge of a specific programming language is not required. However all students must be comfortable programming in some language (e.g. R/Python/Java/C). Students should also be able to use a text editor to edit code.


Module Requisites and Incompatibles
Incompatibles:
COMP30750 - Information Visualisation -DS


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Practical Information Visualisation 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
Barbara De Kegel Tutor
Ms Shreya Tadas Tutor
Ms Swathi Ramachandra Upadhya Tutor
Mr Anjan Venkatesh 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) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Mon 10:00 - 11:50
Practical Offering 1 Week(s) - 19, 20, 21, 22, 23, 24, 25, 28, 29, 30, 31, 32 Wed 09:00 - 10:50
Spring