- Knowing and understanding basic principles, terminology and methods of statistics (k1,k2)
- Applying statistical methods and critically evaluating their results (k3,k4, k5)
- Knowing about common pitfalls (k3)
- Solving typical tasks in Data Science, including non-standard problems and data types (k2,k3)
- Using of statistical software
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- Exploratory data Analysis and Data Cleaning with R
- Methods of inference such as regression in different variants
- Classical versus Bayesian statistics
- Selected modern developments such as statistical learning, causal inference, text/image analysis, or dealing with large amounts of data
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