Data Science and Artificial Intelligence

Advanced information visualization

Integrated course, 2.50 ECTS

 

Course content

Part 1: Basics of visualization
- Basics of human processing of visual information
- Pitfalls and distortions in visualizations
- Standardization in the field of visualization
- Report and chart types and their properties
- Classic diagram types (area, bar, column, line, network diagrams, boxplots, scatterplots etc.)
- Modern diagram types (heat maps, tree maps, stream graphs, chord and sunburst diagrams etc.)
- Special types of diagrams (speedometer, waterfall diagrams, maps etc.)
- Text-based visualizations (word clouds, infographics, etc.)
Part 2: Advanced topics
- Animated visualizations
- Interactive visualizations
- Automated dynamic reporting

Learning outcomes

The students have profound knowledge in the field of information visualization. They are familiar with all relevant forms of visualization and are also able to create these visualizations based on existing data. In particular, they are also able to generate interactive visualizations and automated dynamic reports.

Recommended or required reading and other learning resources / tools

Recommended literature or books:
- Hichert, R. (2019). Solid, outlined, hatched: How visual consistency helps better understand reports, presentations and dashboards. Vahlen, 1st edition.
- Kieran, H. (2019). Data Visualization: A Practical Introduction. Priceton University Press, 1st edition.
- Lovelace R., Nowosad J :, Muenchow J. (2019). Geocomputation with R. Taylor & Francis Ltd., 1st edition.
- McCandless, D. (2014). Knowledge is beautiful. Harper Collins Publ. UK, 1st edition.
- Ohser, J. (2018) Angewandte Bildverarbeitung und Bildanalyse: Methoden, Konzepte und Algorithmen in der Optotechnik, optischen Messtechnik und industriellen Qualitätskontrolle. Carl Hanser Verlag GmbH & Co. KG, 1. Auflage.
- Skiena, S. (2017). The Data Science Design Manual (Texts in Computer Science). Springer, 1st edition.
- Squire, M. (2015). Clean Data. Packt Publishing, 1st edition.
- Van Burren, S. (2018). Flexible Imputation of Missing Data, Second Edition (Chapman & Hall / CRC Interdisciplinary Statistics). Taylor & Francis Ltd., 2nd edition.
- Van der Loo, M., De Jonge, E. (2018). Statistical Data Cleaning with Applications in R. Wiley, 1st edition.
- Wickham, H. (2017). R for data science. O'Reilly UK Ltd., 1st edition.
- Wiedemann, J. (2018). Understanding the World. The Atles of Infographics. TASCHEN, 1st edition (multilingual).
- Wiedemann, J. et al (2018). Information Graphics. BAGS, reissue.
- Winston, C. (2018). R Graphics Cookbook: Practical Recipes for Visualizing Data. O'Reilly UK Ltd., 2nd edition.
- Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley, 1st edition.
Recommended journals or selected articles:
Relevant journals and articles will be announced in the courses.

Typical software for this module:
R / RStudio, Python / Spyder / PyCharm, Matlab / Octave / Scilab etc.

Mode of delivery

1,25 ECTS Lecture, 1,25 ECTS Exercise

Prerequisites and co-requisites

module 2,3,4 and 5

Assessment methods and criteria

Lecture: final exam, Exercise: examination character