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Visual Data Analytics

Module IN2026

This Module is offered by TUM Department of Informatics.

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Module version of WS 2018/9 (current)

There are historic module descriptions of this module. A module description is valid until replaced by a newer one.

Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.

available module versions
WS 2018/9SS 2015WS 2011/2

Basic Information

IN2026 is a semester module in English language at Master’s level which is offered in winter semester.

This Module is included in the following catalogues within the study programs in physics.

  • Focus Area Bio-Sensors in M.Sc. Biomedical Engineering and Medical Physics
  • Catalogue of non-physics elective courses
Total workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Content, Learning Outcome and Preconditions


Visualization pipeline (data acquisition, filtering, display), information visualization vs. scientific visualization, grids and grid construction (Delaunay triangulation), interpolation in grids (inverse distance weighting, radial basis functions), discretization aspects, visualization of scalar fields (color coding, iso-contours and iso-surfaces, volume rendering, vector field visualization (particle-based visualization, line integral convolution, topological approaches), terrain rendering including adaptive meshing techniques and hierarchical data representations using quadtree and octrees.

Learning Outcome

After successful completion of the module, the students have gained advanced knwowledge concerning the visualization pipeline, ranging from data acquisition to the final image of the data. This includes knowledge about the application specific data representations, data interpolation and approximation techniques for discrete data sets, data filtering techniques like convolution, as well as the final mapping stage to generate a renderable representation from the data. The students know the common methods which are used in information visualization to graphically depict abstract data, and in scientific visualiztion to graphically depict 2D and 3D scalar and vector fields, including isocontouring, direct volume rendering, flow visualization, and terrain rendering. They can analyse and categorize availaible techniques in terms of quality, efficiency, and suitability for a particular data type, and they can model and develop new approaches considering application-specific requirements. In the practical exercises the student learn about the functionality of commonly used visualization tools, they can evaluate available tools based on their functionality, and they can apply these tools to create own visualizations of given data sets.



Courses, Learning and Teaching Methods and Literature

Courses and Schedule

Learning and Teaching Methods

The modul consists of the lecture and an accompanying practical exercise. In the lecture, the lecturer conveys to the students the area-specific knowledge, points towards relevant articles and ecourages the students to read and put into relation the presented approaches, and gives examples demonstrating the application of these approaches. In the practical exercises, state-of-the-art tools for scientific visualization are demonstrated online. The students are introduced to these tools so that they can use them on their own. The students are supposed to apply some of the tools for the visualization of 3D data sets from a number of different application domains. They learn to differentiate common visualization techniques regarding the data modailities they are suited for. Small tasks using public domain visualization tools assess the ability to apply suitable visualization techniques to specific kinds of data and let the students become familiar with common visualization options.


Powerpoint course slides, white board exercises, online tutorials and demonstrations


Schumann, Müller: Visualisierung - Grundlagen und allgemeine Methoden, Springer Verlag
C. Hansen, C. Johnson (Ed.): The handbook of Visualization, Academic Press

Module Exam

Description of exams and course work

The exam takes the form of a written test of 75 minutes. Questions allow to assess acquaintance with concepts and algorithms of scientific visualization and visual data analysis, and the application domains where visualization methods are used.

Exam Repetition

The exam may be repeated at the end of the semester.

Current exam dates

Currently TUMonline lists the following exam dates. In addition to the general information above please refer to the current information given during the course.

Visual Data Analytics
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