[1] Telea, Alexandru C. Data visualization: principles and practice. Second edition. 617 s., A K Peters/CRC Press, ISBN 978-146-6585-263 , 2014.
[2] Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019.
Data Visualization
| Language of instruction | angličtina, čeština |
|---|---|
| Code | 460-4166 |
| Abbreviation | VD |
| Course title | Data Visualization |
| Coordinating department | Department of Computer Science |
| Course coordinator | Ing. Tomáš Fabián, Ph.D. |
Summary
The aim of the course is to acquaint students with the problems of visualization and interpretation of various types of scientific, technical and abstract data. The basic principles and procedures of displaying interdisciplinary data are described and the acquired knowledge is used in the implementation of practical tasks in the field of visualization. Emphasis is placed on the adequacy of the chosen visualization tools and on the practical use of the acquired knowledge in the implementation of their own graphic outputs providing the most complete and undistorted picture of the processed data. Theoretical knowledge gained during the analysis of partial tasks serves as a basis for the practical implementation of specific examples in exercises. The exercises therefore correspond closely with the lectures and the practical implementation of the mentioned topics is assumed, especially in the C++, Python, and JavaScript languages. Part of completing the course also involves developing and presenting a practical project.
Students will have basic theoretical and practical knowledge in the field of visualization of non-trivial scientific, technical and abstract data. They will be able to create graphic outputs from the processed data for the purposes of their further analysis, creation of professional publications and presentations for the general public.
After completing the course, students will be able to:
- enumerate and characterize methods and means for visualization of scientific, technical and abstract data,
- find the optimal form of visual presentation for a specific type of data and audience,
- define the necessary software tools for implementing the proposed visualization,
- implement interactive visualizations, e.g., in a web browser environment,
- graphically analyze multidimensional data and project it into lower-dimensional spaces,
- design a color palette according to the nature of the data and with regard to its correct interpretation,
- convert complex data into a comprehensible and visually attractive form,
- critically evaluate the quality and accuracy of visualization,
- use the acquired skills in the implementation of projects, final theses, and subsequently also in practice.
Students will have basic theoretical and practical knowledge in the field of visualization of non-trivial scientific, technical and abstract data. They will be able to create graphic outputs from the processed data for the purposes of their further analysis, creation of professional publications and presentations for the general public.
After completing the course, students will be able to:
- enumerate and characterize methods and means for visualization of scientific, technical and abstract data,
- find the optimal form of visual presentation for a specific type of data and audience,
- define the necessary software tools for implementing the proposed visualization,
- implement interactive visualizations, e.g., in a web browser environment,
- graphically analyze multidimensional data and project it into lower-dimensional spaces,
- design a color palette according to the nature of the data and with regard to its correct interpretation,
- convert complex data into a comprehensible and visually attractive form,
- critically evaluate the quality and accuracy of visualization,
- use the acquired skills in the implementation of projects, final theses, and subsequently also in practice.
Literature
Advised literature
[1] Edward R. Tufte, The Visual Display of Quantitative Information, Second Edition, ISBN: 978-1930824133 , 197 pages, Graphics Pr, 2001.
[2] Edward R. Tufte, Envisioning Information, ISBN: 978-0961392116 , 126 pages, Graphics Pr, 1990.
[3] Chun-houh Chen, Wolfgang Härdle, Antony Unwin, Handbook of Data Visualization, ISBN: 978-3-540-33036-3 , 936 s., Springer, 2008.
[4] Ware, Colin. Information Visualization: Perception for Design (Interactive Technologies), Fourth edition, 560 s., Morgan Kaufmann, ISBN 978-0128128756 , 2020.
[5] Charles D. Hansen and Chris Johnson. The visualization handbook. 2004, 962 s.. ISBN 978-012-3875-822 .
[6] Tamara Munzner, Visualization Analysis and Design, ISBN: 978-1466508910 , 428 s., AK Peters, 2014.
[7] Casey Reas and Ben Fry, Processing: A Programming Handbook for Visual Designers and Artists, ISBN: 978-0262182621, 712 s., MIT Press, 2007.
[8] Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Second Edition, ISBN: 978-0970601971 , 371 s., Analytics Press, 2012.
[2] Edward R. Tufte, Envisioning Information, ISBN: 978-0961392116 , 126 pages, Graphics Pr, 1990.
[3] Chun-houh Chen, Wolfgang Härdle, Antony Unwin, Handbook of Data Visualization, ISBN: 978-3-540-33036-3 , 936 s., Springer, 2008.
[4] Ware, Colin. Information Visualization: Perception for Design (Interactive Technologies), Fourth edition, 560 s., Morgan Kaufmann, ISBN 978-0128128756 , 2020.
[5] Charles D. Hansen and Chris Johnson. The visualization handbook. 2004, 962 s.. ISBN 978-012-3875-822 .
[6] Tamara Munzner, Visualization Analysis and Design, ISBN: 978-1466508910 , 428 s., AK Peters, 2014.
[7] Casey Reas and Ben Fry, Processing: A Programming Handbook for Visual Designers and Artists, ISBN: 978-0262182621, 712 s., MIT Press, 2007.
[8] Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Second Edition, ISBN: 978-0970601971 , 371 s., Analytics Press, 2012.