Statistics
Summary
In "Statistics" you will learn which methods are particularly suitable for analyzing data sets. You will deal with regression models (e.g. with penalization, transformation, binary data, non-parametric, non-linear, random forest) as well as with methods for classification (e.g. decision trees, SVM) and clustering (e.g. K-Means). By working independently with data, you will learn to classify and interpret results in a well-founded manner.
Typical Questions
- How do you model relationships between variables?
- Which methods can be used to quantify the influence of several variables on a specific variable?
- How do you estimate parameters of models and how certain can you be that these estimators are reliable?
Modules (Selection)
- Applied Multivariate Statistics (Prof. Dr. Okhrin)
- Data-Driven Multivariate Statistics (Prof. Dr. Okhrin)
- Theoretical Multivariate Statistics (Prof. Dr. Okhrin)
Career Prospects
As a graduate of "Statistics", you can work as a data analyst at economic research institutes, banks, in quantitative risk management or at insurance companies.