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Lecturer(s)
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Kohout Josef, Doc. Ing. Ph.D.
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Course content
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1. The Value of Visualization 2. Visual Perception and Colours 3. Visualization Design and Redesign 4. Multidimensional Visualization and High Dimensional Visualization 5. Information Visualization for Knowledge Exploration 6. Visualization of Uncertainty 7. Time Series Data Visualization 8. Hierarchy, Tree and Graph Visualization 9. Interaction in Visualization Systems 10. Economic Data Visualization 11. Security Visualization 12. Text Data Visualization 13. Biomedical Data Visualization
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Learning activities and teaching methods
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- Preparation for an examination (30-60)
- 40 hours per semester
- Contact hours
- 52 hours per semester
- Presentation preparation (report) (1-10)
- 10 hours per semester
- Graduate study programme term essay (40-50)
- 50 hours per semester
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| prerequisite |
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| Knowledge |
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| demonstrate knowledge of the basic principles of the theory of differential and integral calculus of functions of one or more real variables (KMA/MA2 or KMA/M2) |
| understand the basic principles of linear algebra (KMA/LAA) |
| demonstrate knowledge of the basic statistical methods and approaches to data analysis (KMA/PSA) |
| demonstrate knowledge of basic data structures used in computer science (stack, queue, special search trees, dictionaries, hash tables, sets, graphs) (KIV/PT or KIV/ADS) |
| understand the basic principles of event programming, especially in the context of the user interface and programming of simple animations of vector objects (KIV/UUR, KIV/UPG or KIV/ZPG, KIV/PH, etc.) |
| Skills |
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| use English at least at level B2 of the Common European Framework of Reference for Languages (UJP / AEP4, etc.) |
| perform basic calculations in the field of differential and integral calculus, linear algebra and matrix calculus (KMA/MA1, KMA/LAA and similar courses) |
| use knowledge of basic statistical methods and approaches for data analysis (KMA/PSA) |
| design and implement more complex algorithms for processing heterogeneous data (KIV/PPA2 or KIV/ADS, KIV/ALG or KIV/PRO, KIV/PC, and other) |
| Competences |
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| N/A |
| N/A |
| learning outcomes |
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| Knowledge |
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| explain the principles of good data visualization design preventing misinterpretations |
| describe and explain key visualization techniques, including bar chart, line chart, histogram, scatter plot, Tukey box plot, violin plot, data map, treemap, timeline, parallel coordinates, semantic networks |
| describe approaches to visual analytics of large multidimensional data, including interactive exploration using scatter-plots, parallel coordinates, heatmaps, etc. |
| be familiar with the state-of-the-art methods for visualization of selected data from the field of financial informatics, IT security, natural language processing, and biomedicine. |
| Skills |
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| read and discuss research papers in the field of information visualization |
| explore datasets through visualization using Tableau |
| design and implement interactive visualization of large multidimensional data (based, e.g., on D3.JS) |
| Competences |
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| N/A |
| N/A |
| N/A |
| teaching methods |
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| Knowledge |
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| Lecture supplemented with a discussion |
| Interactive lecture |
| Self-study of literature |
| Individual study |
| Skills |
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| Self-study of literature |
| Practicum |
| Individual study |
| Project-based instruction |
| Competences |
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| Lecture supplemented with a discussion |
| Discussion |
| Self-study of literature |
| assessment methods |
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| Knowledge |
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| Continuous assessment |
| Individual presentation at a seminar |
| Combined exam |
| Skills |
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| Continuous assessment |
| Seminar work |
| Skills demonstration during practicum |
| Individual presentation at a seminar |
| Competences |
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| Seminar work |
| Continuous assessment |
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Recommended literature
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Selected readings from peer-reviewed related literature as specified on CourseWare.
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Munzner, Tamara. Visualization analysis & design. 2015. ISBN 978-1-4665-0891-0.
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Tufte, Edward R. Beautiful evidence. Cheshire : Graphics Press, 2006. ISBN 0-9613921-7-7.
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