Visual Analytics for Data Science
level of course unit
Learning outcomes of course unit
Graduates have basic knowledge of data visualization and visual communication. They can independently develop visualizations and use these for communication purposes. Graduates can work with various illustration tools and illustration libraries in order to depict data and analysis results in a meaningful manner. They also know how to use visual analytics in order to test hypotheses and access data.
prerequisites and co-requisites
Students learn how to deal with various illustration tools and illustration libraries. They also learn about the fundamentals of visual communication and visual analytics.
The course content specifically encompasses these topics:
-Evaluation tools with visual orientation, e.g. BI tools such as MS PowerBI, tableua, QlikView
-Illustration libraries, e.g. matplotlib.pyplot, gglot2
-Rules for visual communication, e.g. Hichert SUCCESSS
recommended or required reading
- Chang, W. (2013) R Graphics Cookbook: Practical Recipes for Visualizing Data. 1. Auflage, O´Reilly, Farnham (ISBN: 978-1449316952).
- Chen, C.; Härdle, W. K.; Unwin, A. (2008) Handbook of Data Visualization. 1. Auflage, Springer, Berlin (ISBN: 978-3-662-50074-3).
- Murray, S. (2017) Interactive Data Visualization for the Web: An Introduction to Designing with D3. 2. Auflage, O´Reilly, Farnham (ISBN: 978-1491921289).
- Rahlf, T. (2017) Data Visualisation with R: 100 Examples. 1. Auflage, Springer, Wiesbaden (ISBN: 978-3319497501).
assessment methods and criteria
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
semester/trimester when the course unit is delivered
name of lecturer(s)
Head of studies
year of study
recommended optional program components
course unit code
type of course unit
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