Teaching: Visualization (lecture+exercises)

• 3 SWS (2h lecture + 1h exercises), credits: 5
• See StudIP for details on time and place.
• The lecture is held in hybrid format, i.e. it can be attended in person or online and recordings will be made available. Interactive participation in the lecture is strongly encouraged.
• The exam is held in the form of small group projects / open world exam (groups of up to three, workload of approx 3 days)
• Admission to the exam requires participation in the exercises.
• Lanuguage: English

Content summary

The main subject of this lecture is the creation of good figures and visualizations, with a focus on scientific publications and exploratory data analysis. This is not a lecture on computer graphics and 3d rendering.
We will address:

• design principles and their connection to the human visual system
• how visualizations can be misleading, by accident or intention
• a zoo of visual data representations for a wide range of data types, covering basic and complex scenarios (examples: time series, point clouds, arrays, meshes, graphs, functions and maps,...)
• the challenge of visualizing high-dimensional data
• interactive visualization techniques

The lecture will be heavily based on concrete examples.

The creation of visualizations heavily relies on computational tools, but the rules for creating good visualizations are independent of a particular choice of software. So this lecture is not a software tutorial. However, as a prototypical software environment for concrete implementation the lecture will refer to Python/Matplotlib/Jupyter/LaTeX, which is prevalent in the scientific community.

Literature recommendations

• Alberto Cairo: The Functional Art, New Riders, Berkeley, 2013
• Alberto Cairo: How charts lie, W. W. Norton & Company, 2019
• Andy Kirk: Data Visualisation: A Handbook for Data Driven Design, SAGE Publications Ltd, London, 2019
• Robert Spence: Information Visualization, Springer, 2014
• Leland Wilkinson: The grammar of graphics, Springer, 2005

Online resources

• Online book "ggplot2: elegant graphics for data analysis"
• Online course "R for data science"
• Online course "Data visualisation" by the service for Digital Learning and Teaching of Uni Göttingen