TU Delft
Year
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NEDERLANDSENGLISH
Organization
2016/2017 Electrical Engineering, Mathematics and Computer Science Master Computer Science
IN4086-14
Data Visualization
ECTS: 6
Responsible Instructor
Name E-mail
Prof.dr. E. Eisemann    E.Eisemann@tudelft.nl
Dr. A. Vilanova Bartroli    A.Vilanova@tudelft.nl
Contact Hours / Week x/x/x/x
0/4/0/0 + lab
Education Period
2
Start Education
2
Exam Period
2
3
Course Language
English
Required for
Master course MKE/ST/DS
Expected prior knowledge
IN2905-A/IN4152/TI1806 Computer Graphics (recommended, not required).
Basic programming skills are expected, but all relevant topics will be introduced.
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 improve insight, understanding and/or communication of data. Data visualizations uses a combination of methods from variety of disciplines: perception, computer graphics, human computer interaction, algorithmics, image processing, machine learning, numerical analysis, optimization,

As a computer science course affinity to algorithmic thinking and programing skills will be needed.

Topics covered: models of the visualization process; colour models and use of colour; information visualization; representation and processing of data; volume visualization; interactive visual data analysis; visualization of vector fields and flows. Guest lectures might be given on selected topics.

Study Goals
The goal of the course is to get knowledge on the basic fundaments that are part of data visualization. The main principles and techniques that are the basis of generating effective visual representations of data.
Techniques and cases of data visualization are discussed.There are several applications for the techniques, medical, engineering, finances, economics, game analytics.


After the course, the student has knowledge and understanding of a wide range of general visualization techniques, perception principles, mathematical foundations, algorithms, and relevant data representations. Furthermore, the student is able to adapt, and develop suitable techniques for a given practical visualization problem.



Education Method
Lectures, practical assignments, self-study, and projects.
Literature and Study Materials
Course slides, instructions for projects, and selected literature.
All available in electronic form via Blackboard.
Assessment
The final grade is a weighted average based on two visualization projects, an exam that might contain multiple choice questions. The projects will be developed in couples and are evaluated based on the effectiveness of the results, reasoning/justification of the techniques used based on the material given at the course, technical contribution or implementation, quality of the documentation and presentation.
Special Information
It is necessary that you register/enroll on Blackboard for this course.

In the first lecture, details on the evaluation and practical information on the course will be given.
Judgement
The grade consists of 3 elements: Information Visualization project, Volume Visualization Project and an exam.

The two projects will be developed in couples and will represent 70% of the mark together. All projects, which are handed in late will be evaluated with a zero and impact the part of the mark that corresponds to the project.

Additionally, an exam will be held, which will represent 30% of the mark. The exam might contain multiple-choice questions .

The project is evaluated based on the developed result, its documentation and presentation.

Final Mark = 0.7 (InfoVis Project and VolVis Project ) + 0.3 Exam

The course is passed if the final grade is 6 or higher in average.

The exam will get a resit. No resit will be provided for the projects unless the mark on the exam and the other project are above 7.5