Summary of Study |
Summary of Branches |
All Subject Groups |
All Subjects |
List of Roles |
Explanatory Notes
Instructions
Web page:
https://moodle.fel.cvut.cz/course/B4M39VIZ
Anotation:
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 method serves as an external representation, with which it is possible to quickly obtain data values or compare data. This frees up the memory and cognitive capabilities of the analyst to solve the problem that the data represents.
Study targets:
To master basic methods and tools for data visualization - in the fields of scientific visualization and information visualization.
Content:
In the seminars, students solve in pairs (or individually) assigned data visualization tasks. Each seminar focuses on a different area of data visualization (e.g. visualization of scalar fields, visualization of relational data). In two seminars, students write two tests verifying the learned material.
As part of the course, students work in pairs (or individually) on a semester project whose goal is to visualize the supplied data. First, students conduct a research on visualization methods suitable for the supplied data and write a report about it. Based on the research, students design and implement a suitable data visualization without using any visualization libraries. They write a second report about design and implementation of their visualization and about the achieved results. The students present the achieved results on the seminar.
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. | | Data mapping in Tableau Public |
| 4. | | Consultations of semestral works |
| 5. | | Visualization of scalar fields |
| 6. | | Visualization of volumetric data |
| 7. | | Visualization of vector fields |
| 8. | | 1st test |
| 9. | | Consultations of semestral works |
| 10. | | Visualization of n-dimensional data |
| 11. | | Visualization of relational data |
| 12. | | 2nd test |
| 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:
| Page updated 15.3.2026 07:52:04, semester: Z,L/2025-6, Z/2026-7, Z,L/2027-8, L/2026-7, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |