After completing the course, students will be able to

• derive relevant research questions concerning business objectives and assess them in terms of management relevance;
• assess different research designs about their suitability in relation to the research objective (description, prediction, causal inference);
• analyze relationships between variables based on observational data;
• understand the basics of methods from the fields of supervised and unsupervised statistical learning (e.g. regression, classification, cluster analysis);
• implement analysis methods using the state-of-the-art statistical software R;
• interpret the results of statistical analyses and translate them into relevant implications as a decision-making aid for managers;
• present the results of statistical analyses and communicate them to various stakeholders.

The course integrates theoretical knowledge with practical applications through a combination of interactive lecture units with discussions, analysis of business cases, a group project, and home computer exercises. Please note that the teaching of the content is partly based on the flipped classroom principle, i.e., the content must be worked on as preparation for the units in self-study based on the materials provided. In particular, an online script with explanatory texts and videos is provided for this purpose. In order to be prepared for the weekly units, the content of the respective week must be worked through. During the on-campus units, we focus on problem-solving and practical applications. For the participation grade, it is important to actively participate in the units and the discussions and to ask questions about the material if necessary. 

The weekly materials are supplemented by case studies and computer exercises aimed at knowledge transfer. The home computer exercises must be submitted via the Canvas platform by the specified deadline. In addition, the course includes a group project in which students have to analyze a provided data set to draw managerial recommendations. There will be an online forum to facilitate interaction between students and to clarify questions.

The course includes practical applications of data analysis with the statistical software R. R is an open-source software and can therefore be installed and used free of charge. It is recommended to use R via the integrated development environment and graphical user interface RStudio:

• Download R: https://cran.r-project.org/ 
• Download R Studio: https://www.rstudio.com/products/rstudio/download/ 

Please also use the various free online materials (e.g., http://r4ds.had.co.nz/). 

It is recommended to review courses on statistics and marketing.