Subject description - BE4M39VIZ
Summary of Study |
Summary of Branches |
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List of Roles |
Explanatory Notes
Instructions
| BE4M39VIZ | Visualization | ||
|---|---|---|---|
| Roles: | PV, PS, PO | Extent of teaching: | 2P+2C |
| Department: | 13139 | Language of teaching: | EN |
| Guarantors: | Čmolík L. | Completion: | Z,ZK |
| Lecturers: | Čmolík L. | Credits: | 6 |
| Tutors: | Čmolík L., Pavlovec V. | Semester: | L |
Web page:
https://moodle.fel.cvut.cz/course/B4M39VIZAnotation:
In this course, you will get the knowledge of theoretical background for visualization and the application of visualization in real-world examples. The visualization methods are aimed at exploiting both the full power of computer technologies and the characteristics (and limits) of human perception. Well-chosen visualization methods can help to reveal hidden dependencies in the data that are not evident at the first glance. This in turn enables a more precise analysis of the data or provides a deeper insight into the core of the particular problem represented by the data.Study targets:
To master basic methods and tools for data visualization - in the fields of scientific visualization and information visualization.Course outlines:
| 1. | Introduction to visualization | |
| 2. | Data and task categorization | |
| 3. | Principles of data visualization | |
| 4. | Interaction in visualization | |
| 5. | Visualization of scalar fields | |
| 6. | Visualization of volumetric data | |
| 7. | Visualization of vector fields | |
| 8. | Visualization of tabular data | |
| 9. | Visualization of relational data | |
| 10. | Text and software visualization | |
| 11. | Visualization of geographic data | |
| 12. | Time and its visualization | |
| 13. | Visual data mining, visual analytics, big data | |
| 14. | Spare lecture |
Exercises outline:
| 1. | Introduction to the course | |
| 2. | Introduction to Paraview | |
| 3. | Introduction to Tableau Public | |
| 4. | Visualization of scalar data | |
| 5. | Visualization of volumetric data | |
| 6. | Visualization of vector data | |
| 7. | 1st test | |
| 8. | Presentations of STAR reports | |
| 9. | Visualization of n-dimensional data | |
| 10. | Visualization of relational data | |
| 11. | 2nd test | |
| 12. | Visual analytics | |
| 13. | Presentations of semestral works | |
| 14. | Spare seminar |
Literature:
| 1. | Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series, CRC Press, 2014. | |
| 2. | Alexandru C. Telea. Data Visualization: Principles and Practice (2nd edition). CRC Press, 2014. |
Requirements:
Keywords:
Data visualization, Scientific visualization, Information visualization, Visual analytics Subject is included into these academic programs:| Program | Branch | Role | Recommended semester |
| MEOI9_2026 | Data Science | PS | 2 |
| MEOI9_2018 | Data Science | PO | 2 |
| MEOI3_2026 | Computer Graphics | PV | 2 |
| MEOI1_2018 | Human-Computer Interaction | PO | 2 |
| MEOI3_2018 | Computer Graphics | PO | 2 |
| MEOI1_2026 | Extended Reality and Game Development | PV | 2 |
| Page updated 13.9.2026 05:55:06, semester: Z,L/2026-7, L/2025-6, L/2029-30, Z,L/2028-9, Z,L/2027-8, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |