Data Science and Artificial Intelligence

Descriptive Statistics

Integrated course, 2.50 ECTS


Course content

Area 1: Introduction and parameters
- Overview of the sub-disciplines of statistics
- Level of measurements (scale of measure)
- Location, dispersion and association measures
- Basics of statistical visualization (especially boxplots and scatterplots)
Area 2: regression
- linear regression
- Linear transformable nonlinear regression
- Logistic regression
Area 3: Time series analysis
- Trends and seasonal components
- Autocorrelation
- Heteroscedasticity

Learning outcomes

Students have a profound understanding of important parameters and relationships in descriptive statistics. In addition, they are able to perform linear and linearly transformable nonlinear regressions and also to analyze time series fundamentally.

Recommended or required reading and other learning resources / tools

Recommended Literature and Books: - Arens, T., Hettlich, F. (2018). Mathematik. Springer Spektrum, 4. Auflage.
- Bronstein, I. N., Mühlig, H. (2016). Taschenbuch der Mathematik. Europa-Lehrmittel, 10. Auflage.
- Büning, H., Trenkler, G. (1994). Nichtparametrische statistische Methoden (De Gruyter Lehrbuch). De Gruyter, 2. Auflage.
- Field, A., Miles, H. (2012). Discovering Statistics Using R. Sage Publications Ltd., 1. Auflage.
- Hedderich, J., Sachs, L. (2018). Angewandte Statistik: Methodensammlung mit R. Springer Spektrum, 16. Auflage.
- Ugarte M. D., Miltino A. F. (2015). Probability and Statistics with R. CRC-Press, 2. Auflage.
Recommended journals and selected articles: All relevant journals and articles will be given in the class. Typical software for this module: R/RStudio etc.

Mode of delivery

1,25 ECTS Lecture, 1,25 ECTS Exercise

Prerequisites and co-requisites


Assessment methods and criteria

Lecture: final exam, Exercise: examination character