Subject description - A4M39VIZ
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Instructions
A4M39VIZ | Visualization | ||
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Roles: | Extent of teaching: | 2P+2C | |
Department: | 13139 | Language of teaching: | CS |
Guarantors: | Completion: | Z,ZK | |
Lecturers: | Credits: | 6 | |
Tutors: | 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.Content:
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.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:
visualization, scientific visualization , information visualization
Subject is included into these academic programs:
Program | Branch | Role | Recommended semester |
Page updated 14.3.2025 17:51:17, semester: Z/2025-6, L/2024-5, L/2025-6, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs | Proposal and Realization: I. Halaška (K336), J. Novák (K336) |