Information Visualization

Course goals

This course aims to prepare students to act as professionals able to execute the design, construction, and evaluation of information visualization pipelines, corresponding the following learning objectives:

• identify what kind of problems visualization can solve
• explain why and when visualization works
• describe how to evaluate a visualization project: identify the elements of a project that need to be evaluated and strategies to carry out effective evaluations
• use suitable visualization techniques for problems based on tasks and data types
• be able to design and implement interactive data visualizations,  and argue for their effectiveness.

The goal of this course is to expose you to methods and techniques of information visualization that enhance comprehension, communication and decision making in the context of large and complex data. In the practical part of the course, the design of these visualization methods is in the focus and requires implementation (see requirements).

As such, this course is complementary to other courses, in particular data-science and machine-learning courses that train students on how to prepare datasets algorithmically.

Assessment
The grade will be composed as follows:

• lecture examination: written exam (40% of the final mark)
• practical examination: final project in groups (60%)

The final grade is the weighted average of these two grade components. 
To pass the course, the final grade has to be at least a 5.5 (rounded to 6 in OSIRIS).

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Content

Information visualization is an increasingly important discipline for numerous scientific and application domains concerned with analysis and decision making with data. 
The amount and complexity of data produced in science, engineering, business, and everyday human activity is increasing at staggering rates.
In this course you will learn how the human visual system – a uniquely powerful system in our brain – can support reasoning over complex data as well as how to apply effective data visualization practices and methods.

Course form
Lectures, self study, labs with homework, project work.