Lecture: Visual Data Analysis
Course
- Lecturer(s):
- Start: April 2, 2019
- Dates: Tue 10:00-11:30 and Wed. 10:30-12:00, INF/B-IT 0.109
- Curriculum: B-IT Master Media Informatics , Master
- Exams: 1st: July 10, 10:30-12:30 2nd: September 2, 10:00-12:00
Exercises
- Tutor(s):
- Start: April 10, 2019
- Dates: Wed. 12:00-13:30, B-IT 0.109
Description
Please In science, but also in business, journalism, and many other fields, ever-increasing amounts of measured, simulated, collected and stored data have turned the ability to make sense of large and complex data into a key qualification. The goal of visualization is to produce interactive visual representations of data. It provides an intuitive, effective, and thus very popular means of quite literally gaining insight into complex phenomena. This lecture introduces the main concepts and techniques of visual data analysis. Its topics include: Color and perception, visualizing multi-dimensional data, dimensionality reduction, graph visualization, machine learning and visualization, visualization design, rendering 3D volume data (such as medical CT or MRI data), extracting surfaces from volumetric data, visualizing and identifying meaningful structures in vector fields (such as flow fields from computational fluid dynamics) and tensor fields (as they arise in engineering and modern neuroimaging).
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Slides
- Introduction (PDF document, 2.7 MB)
- Color-and-Perception (PDF document, 3.7 MB)
- Multi-Dimensional-Data (PDF document, 2.3 MB)
- Dimensionality-Reduction (PDF document, 2.9 MB)
- Graph-Visualization (PDF document, 2.3 MB)
- Visualization-Design (PDF document, 2.0 MB)
- Neural-Networks (PDF document, 3.2 MB)
- SciVis-Foundations (PDF document, 3.2 MB)
- Indirect-Volume-Visualization (PDF document, 2.7 MB)
- Direct-Volume-Rendering (PDF document, 3.2 MB)
- Geometry-Based-Flow-Vis (PDF document, 4.0 MB)
- Image-Based-Flow-Vis (PDF document, 3.2 MB)
- Feature-Based-Flow-Vis (PDF document, 3.2 MB)
- Tensor-Vis (PDF document, 3.6 MB)
- Ridges-and-Valleys (PDF document, 1.8 MB)
Assignment Sheets
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Exercise 1: Installation Assignment sheet (PDF document, 138 KB) |
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Exercise 2: Color Assignment sheet (PDF document, 3.0 MB)
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Exercise 3: Multi-Dimensional-Visualization Assignment sheet (PDF document, 784 KB)
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Exercise 4: Dimensionality-Reduction Assignment sheet (PDF document, 188 KB)
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Exercise 5: tSNE-Graphs Assignment sheet (PDF document, 187 KB)
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Exercise 6: Graphs-Plotly-Dash Assignment sheet (PDF document, 351 KB)
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Exercise 7: Dash-Neural-Networks Assignment sheet (PDF document, 432 KB)
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Exercise 8: SciVis-Foundations Assignment sheet (PDF document, 370 KB)
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Exercise 9: Interpolation-Isosurfaces Assignment sheet (PDF document, 292 KB)
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Exercise 10: Transfer-Functions Assignment sheet (PDF document, 1.0 MB)
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Exercise 11: Flow-Visualization Assignment sheet (PDF document, 812 KB)
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Exercise 12: Trial-Exam Assignment sheet (PDF document, 300 KB) |
Additional Documents
- Results-First-Exam (PDF document, 18.8 KB)
- Results-Second-Exam (PDF document, 18.1 KB)
Literature
- Alexandru C. Telea: Data Visualization - Principles and Practice, CRC Press, Second Edition, 2015
- C.D. Hansen, C. Johnson: Visualization Handbook, Academic Press, 2004
- B. Preim, C. Botha: Visual Computing for Medicine: Theory, Algorithms, and Applications, Morgan Kaufmann, 2014 (officially available online within University of Bonn)
- K. Engel et al.: Real-Time Volume Graphics, A K Peters, 2006
- C. Ware: Information Visualization. Perception for Design, Morgan Kaufmann, 3rd edition, 2013
- M. Ward et al.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press, 2010