TU Delft
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2013/2014 Electrical Engineering, Mathematics and Computer Science Master Computer Science
Data Visualization
Responsible Instructor
Name E-mail
Prof.dr. E. Eisemann    E.Eisemann@tudelft.nl
Contact Hours / Week x/x/x/x
Education Period
Start Education
Exam Period
Course Language
Required for
Master course MKE
Expected prior knowledge
IN2905-A Computer Graphics (recommended, not required)
Optional: Fundamentals of 3D computer graphics (Catch-up lectures, 2 hours)
for students with little background in computer graphics
Course Contents
Data visualization is the visual representation of large quantities of data by computer generated images. The data sets can be results of numerical simulations or measurements (scientific visualization), or other data collections such as large databases (information visualization). The goal is to promote insight and communication. Theory and general principles are discussed and illustrated by practical examples from application areas in science, engineering, and medicine.
Topics covered: models of the visualization process; colour models and use of colour; information visualization; representation and processing of data; volume visualization; medical visualization; interactive visual data analysis; visualization of vector fields and flows; feature extraction; and virtual reality for visualization. Guest lectures will be given on selected topics.
Study Goals
In this course, techniques and cases of data visualization are discussed: models, algorithms, and data representations for conversion of large data sets into visual images, and associated interactive techniques. Main application is visualization of scientific, engineering, and medical data sets (eg. CT or MRI scan data).
After the course, the student has knowledge and understanding of a wide range of general visualization techniques, their mathematical foundations, their algorithmic form, and relevant data representations, so that (s)he can choose, adapt, and develop suitable techniques for a given practical visualization problem. Also, the student can describe numerous practical examples and cases of visualization in many application fields. Finally, the student can work with an advanced visualization software system, and can implement modular extensions to such a system.
Education Method
Lectures, practical assignments, self-study of academic literature, projects.
Computer Use
Practical assignments will be taught in C++.
For the projects, software systems and programming languages can be selected by the student.
Literature and Study Materials
Course slides, instructions for projects.
All available in electronic form via Blackboard.
For each paper read, an in-depth comment / question has to be submitted on time. These are all checked for pass / fail grade. Student participation in class discussion is also checked.

Some of the smaller projects (up to 3) are checked for pass / fail.

The final project, including paper and presentation, are graded. Course is passed if grade is 6 or higher AND all other components have received the pass grade.