Lecture: Visual Data Analysis

Course

  • Lecturer(s):
  • Start: April 21, 2020 (online)
  • Dates: Tue 10:00-11:30 and Wed. 10:30-12:00 (online)
  • Curriculum: B-IT Master Media Informatics , Master
  • Exams: TBA

Exercises

Description

Important: This lecture will start on April 21 in an online only format. Until further notice, all lectures and exercises will be held via video conferencing. Make sure to externsubscribe to the lecture mailing list in advance to receive the access information.

Our ZOOM sessions are interactive, so it is encouraged to participate live. If, for some reason, you cannot manage this or you want to watch certain parts again, you can find externrecordings on sciebo.

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).

Slides

Assignment Sheets

Exercise 1: Installation
Assignment sheet  (PDF document, 138 KB)
Exercise 2: Color
Assignment sheet  (PDF document, 3.0 MB)
Exercise 3: Multi-Dimensional-Visualization
Assignment sheet  (PDF document, 816 KB)
Exercise 4: Dimensionality-Reduction
Assignment sheet  (PDF document, 203 KB)
Exercise 5: MDS-ISOMAP-LDA-tSNE
Assignment sheet  (PDF document, 182 KB)
Exercise 6: Graphs-Plotly-Dash
Assignment sheet  (PDF document, 376 KB)
Exercise 7: Dash-Neural-Networks
Assignment sheet  (PDF document, 448 KB)
Exercise 8: CNNs-ParaView-VTK
Assignment sheet  (PDF document, 602 KB)
Exercise 9: Foundations-Isocurves
Assignment sheet  (PDF document, 368 KB)
Exercise 10: Volume-Visualization
Assignment sheet  (PDF document, 1.2 MB)
Exercise 11: Trial-Exam
Assignment sheet  (PDF document, 303 KB)

Additional Documents

Literature

  • Alexandru C. Telea: Data Visualization - Principles and Practice, CRC Press, Second Edition, 2015
  • B. Preim, C. Botha: Visual Computing for Medicine: Theory, Algorithms, and Applications, Morgan Kaufmann, 2014 (externofficially available online within University of Bonn)
  • 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