CTU FEE Moodle
Visualization
This is a grouped Moodle course. It consists of several separate courses that share learning materials, assignments, tests etc. Below you can see information about the individual courses that make up this Moodle course.
Visualization (Main course) 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
Visualization BE4M39VIZ
| Credits | 6 |
| Semesters | Summer |
| Completion | Assessment + Examination |
| Language of teaching | English |
| 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
No data.
Course outlines
1. Motivation for data visualization, history, categories of visualization 9infovis, scivis, software visualization,..)
2. Visualization of scalar data (visualization pipeline, data reduction)
3. Visualization of vector data (problems of visualization in 2D, 3D,..)
4. Visualization of volume data (marching cube, cuberille)
5. Visualization of volume data (volume data rendering, topological problems of volume data rendering,..)
6. Visualization of dynamic data (animation, time scale,..)
7. Information visualization (HomeFinder, TreeMaps, hyperbolic geometry)
8. Perception and interpretation of visualized data (context, human perception, psychology of perception)
9. Simulation and visualization (e.g. simulation and visualization of technological processes)
10. Visualization of medical data (tomography. Operation planning)
11. Technical illustration, medical illustration
12. Software visualization (visualization of software behavior, visualization of software maintenance ,..)
13. Problems of visual data mining. Applications of visual data mining (relation to neural computing)
14. Reserve
2. Visualization of scalar data (visualization pipeline, data reduction)
3. Visualization of vector data (problems of visualization in 2D, 3D,..)
4. Visualization of volume data (marching cube, cuberille)
5. Visualization of volume data (volume data rendering, topological problems of volume data rendering,..)
6. Visualization of dynamic data (animation, time scale,..)
7. Information visualization (HomeFinder, TreeMaps, hyperbolic geometry)
8. Perception and interpretation of visualized data (context, human perception, psychology of perception)
9. Simulation and visualization (e.g. simulation and visualization of technological processes)
10. Visualization of medical data (tomography. Operation planning)
11. Technical illustration, medical illustration
12. Software visualization (visualization of software behavior, visualization of software maintenance ,..)
13. Problems of visual data mining. Applications of visual data mining (relation to neural computing)
14. Reserve
Exercises outlines
1. Semestral project assignement
2. Semestral project assignement
3. Consultations to semestral project
4. Consultations to semestral project
5. Consultations to semestral project
6. Consultations to semestral project
7. Checkpoint of semestral project
8. Consultations to semestral project
9. Consultations to semestral project
10. Consultations to semestral project
11. Consultations to semestral project
12. Semestral project presentation
13. Semestral project presentation
14. Semestral project assessment
2. Semestral project assignement
3. Consultations to semestral project
4. Consultations to semestral project
5. Consultations to semestral project
6. Consultations to semestral project
7. Checkpoint of semestral project
8. Consultations to semestral project
9. Consultations to semestral project
10. Consultations to semestral project
11. Consultations to semestral project
12. Semestral project presentation
13. Semestral project presentation
14. Semestral project assessment
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
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
Requirements
No data.
Responsible for the data validity:
Study Information System (KOS)