Oral examination (written exam if there are a large number of participants)

As biological datasets increase in size and complexity, we are moving more
and more from an hypothesis-driven research paradigm to a data-driven one.
As a result, the visual exploration of that data has become even more crucial
than in the past. The aim of this lecture is to familiarize the participants
with modern methodologies of Information Visualization and Visual Analytics.
Information Visualization is concerned with methods for the visualization of
abstract data that has no inherent spatial structure (the visualization of spatial
data is covered in INF3145 - Scientific Visualization). The lecture imparts
how to apply these methods to biological data using practical examples and
provides hands-on training during the tutorials. Questions such as ‘what is data
visualization’, ‘what is visual analytics’, and ‘how can we visualise (biological)
data to gain insight in them, so that hypotheses can be generated or explored
and further targeted analyses can be defined’ are discussed. No prior knowledge
of biology is required, that is, the lecture is also suitable for students from other
fields such as computer science or media/medical informatics.

Students understand the visual analysis process. They know basic methods of
information visualization and the ‘do’s’ and ‘don’ts’ of visualization. The know
methods to visualize diverse biological data like genomics or transcriptomics
data. They are able to chose suitable visualizations based on the type of data
and the given analysis task. The students will be able to design and develop
complex, interactive visual analytics applications in small teams.

Lecture slides will be provided for download. Tamara Munzner ‘Visualization
Analysis and Design’, A K Peters, 2014. Nature Methods Supplement ‘Visualizing
biological data’, various Nature Methods ‘Points of View’ articles.