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| Detailed information |
| Original study plan |
Master's programme Computer Science 2017W |
| Objectives |
VA is highly interdisciplinary and covers fields such as data mining, data management, visualization as well as human perception and cognition. In this course students will learn how large amounts of information, such as graphs, text, tables and maps can be effectively analyzed by a user.
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| Subject |
Introduction to visual analytics (definition, VA process, historical aspects), data foundations and management, data mining principles (clustering, PCA, etc.), visualization principles, interaction principles, VA Infrastructure (including processing frameworks like R and WEKA), quantitative & qualitative evaluation methods, biological data analysis, selected current research.
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| Criteria for evaluation |
Written exam (oral exam in exceptional cases)
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| Methods |
Slide presentation with case studies
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| Language |
English |
| Study material |
1. Illuminating the Path: The Research and Development Agenda for Visual Analytics, James J. Thomas and Kristin A. Cook, National Visualization and Analytics Ctr, ISBN-13: 978-0769523231, 2005.
2. Mastering the Information Age - Solving Problems with Visual Analytics, Daniel A. Keim, Jörn Kohlhammer, Geoffrey Ellis and Florian Mansmann, Eurographics Association, ISBN-13: 978-3-905673777, 2010. Free Download.
3. Interactive Data Visualization: Foundations, Techniques, and Applications; Matthew Ward, George Grinstein and Daniel Keim, A K Peters, ISBN: 978-1568814735, 2010.
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| Changing subject? |
No |
| Further information |
http://www.jku.at/cg/content/e48361/e174976/e163866/
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| Earlier variants |
They also cover the requirements of the curriculum (from - to)
921CGELVIAV13: VL Visual Analytics (2013W-2017S)
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