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Data Visualization & Visual Analytics (elective)

level of course unit

English version available soon

Learning outcomes of course unit

The following learning outcomes are developed in the course:

- Students will have basic knowledge of data visualization and visual communication.
- Students can develop visualizations independently and use them for communication purposes.
- Students can work with different presentation tools and presentation libraries to present data and analysis results in a meaningful way.

prerequisites and co-requisites

English version available soon

course contents

The following content is discussed in the course:

- Evaluation tools with visual orientation, e.g. Bl tools such as MS PowerBl, Tableau, QlikView
- Display libraries, e.g. matplotlib. pyplot, gglot2
- Rules of visual communication, e.g. Hichert SUCCESSSS

recommended or required reading

- Chang, W. (2013): R Graphics Cookbook: Practical Recipes for Visualizing Data (Ed. 1), O'Reilly, Farnham (ISBN: 978-1449316952)
- Chen, C.; Härdle, W. K.; Unwin, A. (2008): Handbook of Data Visualization (Ed. 1), Springer, Berlin (ISBN: 978-3-662-50074-3)

- Dale, K. (2016): Data Visualization with Python and Javascript: Scrape, Clean, Explore & Transform Your Data (Ed. 1), O'Reilly, Farnham (ISBN: 978-1491920510)
- Murray, S. (2017): Interactive Data Visualization for the Web: An Introduction to Designing with D3 (Ed. 2), O'Reilly, Farnham (ISBN: 978-1491921289)

assessment methods and criteria

seminar thesis or written exam

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

The following methods are used:

- Lecture with discussion
- Interactive workshop
- Case studies

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dr. Michael Kohlegger

course unit code


type of course unit

integrated lecture

mode of delivery