Statistics
Summary
In the world of data, it is always important to know what insights can be gained from a data set and which methods are particularly suitable for this. How to visualize my data and which theoretical models to describe them? How to estimate the parameters of these models? These are some of the questions the profile "Statistics" deals with. For example, students learn about different regression models (e.g., with penalization, transformation, binary data, nonparametric, nonlinear, random forest) and analyze their explanatory power. Students will learn which methods are best suited for classifying (e.g. Decision Trees, SVM) or clustering (e.g. K-Means) data. Students are guided to work independently with statistical data and models and learn to interpret the results appropriately.
For more detailed information, please refer to the German version of this page!