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Lecturer(s)
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Schlecht Miroslav, doc. Ing. Ph.D.
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Course content
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1. History of Data Visualization 2. Visual Encoding and its Perception 3. Principles of a Good Design of Information Visualization 4. Visualization of Time Series 5. Visualization of Data with a Geolocation 6. Visualization of Multidimensional Data 7. Interaction and Animation 8. Visualization of Uncertainty 9. Exploration of Multidimensional Data 10. Visualization of Hierarchies and Graphs 11. Story-telling 12. Scientific Data Visualization 13. Reserve, Advanced Topics of Data Visualization
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Learning activities and teaching methods
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- Undergraduate study programme term essay (20-40)
- 36 hours per semester
- Presentation preparation (report in a foreign language) (10-15)
- 12 hours per semester
- Contact hours
- 52 hours per semester
- Preparation for an examination (30-60)
- 30 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 |
| 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 commonly used in information visualization, e.g., bar chart, line chart, histogram, scatter plot, Tukey box plot, violin plot, maps, parallel coordinates, and semantic networks |
| describe and explain key visualization techniques commonly used in scientific visualization, e.g., colour maps, iso-lines and iso-surfaces, glyphs, streamlines and streaklines |
| describe approaches to visual analytics of large multidimensional data, including interactive exploration using scatter-plots, parallel coordinates, heatmaps, etc. |
| Skills |
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| visualize multidimensional data using Microsoft Power BI or Tableau |
| visualize scalar and vector fields in 2D and 3D using visualization tools such as ParaView |
| visualize relationships (graphs and hierarchies) using standard tools, e.g., Gephi |
| Competences |
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| 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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| Practicum |
| Individual study |
| Project-based instruction |
| Competences |
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| Lecture supplemented with a discussion |
| Discussion |
| Individual study |
| 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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