Data Visualisation



The textbook for the course is Information Visualization by Robert Spence, published by Addison-Wesley, ISBN 0-201-59626-1.

Assessment will be 70% examination, 30% coursework. The coursework will be split into a data analysis problem (15%) and an essay (15%).


Example Exam Paper An example exam paper is available here

Lecture 1 - Introduction (Powerpoint)
Useful links: Lab 1 - Writing instructions is difficult! (Pdf)

Lecture 2 - Rearrangement and Interaction (Powerpoint)
Useful links: Lab 2 - Data Rearrangement (Pdf)
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Lecture 3 - Interpretation of Quantative Data (Powerpoint)
Useful links: Lab 3 - Handling quantitative data (Pdf)
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Lecture 4 - Representation (Powerpoint)
Useful links: Lab 4 - Data Representation (Pdf)

Lecture 5 - Dynamic Exploration (Powerpoint)
Useful links: Lab 5 - Mind Mapping (Pdf)
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Lecture 6 - Internal Models (Powerpoint)
Lab 6 - The Human Brain - A User's Guide (Pdf)
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Lecture 7 - Connectivity (Powerpoint)
Useful links: Lab 7 - Connectivity (Pdf)

Lecture 8 - Document Visualisation (Powerpoint)
Useful links: Lab 8 - Working with texts (Pdf)
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Lecture 9 - Data Presentation (Powerpoint)
Useful links: Lab 9 - Putting it all together (Pdf)
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Lecture 10 - Models and Autonomous Processes (Powerpoint)
Useful links: Lab 10 - Revision
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Learning Outcomes Grid


Learning Outcome Exceptional Meritorious Highly Competent Competent Pass Fail
Demonstrate an understanding of the advantages of data rearrangement Produces innovative rearrangement techniques Can perform more complex data rearrangement to solve more complex problems Can perform more complex data rearrangement to solve simple problems Can perform basic data rearrangement to solve more complex problems Can perform basic data rearrangement to solve simple problems Cannot rearrange data effectively
Demonstrates an ability to select and use appropriate display methods for different data types Produces innovative and effective visualisations Uses complex and efficient visualisation methods Uses more complex visualisation methods Uses simple but efficient visualisation methods Uses simple but inefficient visualisation methods Uses inappropriate display methods
Apply appropriate techniques for one-, two-, three- and hyper-dimensional data Produces innovative and effective visualisations Uses complex and efficient visualisation methods Uses more complex visualisation methods Uses simple but efficient visualisation methods Uses simple but inefficient visualisation methods Uses inappropriate display methods
Demonstrates an understanding of the cognitive principles involved Designs effective visualisation that that take into account appropriate cognitive principles Creates visualisations designed to work as part of cognitive collages Demonstrates an understanding of the use of mental models for multiple purposes Demonstrates an understanding of the use of simple mental models Takes account of basic capabilities such as 7±2 Takes no account of human cognitive capabilities
Identify problems with visualisations Produces innovative and effective alternative revisualisations Identifies major and minor problems in design and can provide the best alternatives Identifies major and minor problems in design and can provide better alternatives Identifies major problems in design and can provide better alternatives Identifies major problems in visualisation design Cannot identify problems in data display