Visualization
Visualization B4M39VIZ
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C |
Annotation
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.
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 - both in the field of information visualization and scientific visualization as well.
Course outlines
1. Introduction to visualization
2. Data categorization
3. Principles of data visualization
4. Vizualizace skalárních dat
5. Vizualizace objemových dat
6. Vizualizace vektorových dat
7. Vizualizace n-rozměrných dat
8. Vizualizace relačních dat
9. Text and software visualization
10. Time and its visualization
11. User interface and interaction in visualization
12. Visual data mining, visual analytics, big data
13. Trends in visualization
14. Spare lecture
2. Data categorization
3. Principles of data visualization
4. Vizualizace skalárních dat
5. Vizualizace objemových dat
6. Vizualizace vektorových dat
7. Vizualizace n-rozměrných dat
8. Vizualizace relačních dat
9. Text and software visualization
10. Time and its visualization
11. User interface and interaction in visualization
12. Visual data mining, visual analytics, big data
13. Trends in visualization
14. Spare lecture
Exercises outlines
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
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. Fayyad, U., Grinstein, G.G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2002
2. Stasko,J., Domingue,J., Brown,M.H., Price, B.A.: Software Visualization, MIT Press, 1998
3. Chen, Ch.: Information Visualization and Virtual Environments,Springer, 1999
4. Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series, CRC Press, 2014.
5. Alexandru C. Telea. Data Visualization: Principles and Practice (2nd edition). CRC Press, 2014.
2. Stasko,J., Domingue,J., Brown,M.H., Price, B.A.: Software Visualization, MIT Press, 1998
3. Chen, Ch.: Information Visualization and Virtual Environments,Springer, 1999
4. Tamara Munzner. Visualization Analysis and Design. A K Peters Visualization Series, CRC Press, 2014.
5. Alexandru C. Telea. Data Visualization: Principles and Practice (2nd edition). CRC Press, 2014.
Requirements
Subject related pages:
https://moodle.fel.cvut.cz/course/view.php?id=2127
https://moodle.fel.cvut.cz/course/view.php?id=2127
Responsible for the data validity:
Study Information System (KOS)
