We discuss modern statistical methods for data analysis, including methods for data exploration, prediction and inference. We pay attention to algorithmic aspects, theoretical properties and practical considerations. The class is hands-on and methods are applied using the statistical programming language R.

Introduction to basic methods and fundamental concepts of statistics and probability theory for practicioners in natural sciences. The concepts will be illustrated with some real data examples and applied using the statistical software R.

"Statistics Lab" is an Applied Statistics Workshop in Data Analysis. It provides a learning environment in a realistic setting.Students lead a regular consulting session at the Seminar für Statistik (SfS). After the session, the statistical data analysis is carried out and a written report and results are presented to the client. The project is also presented in the course's seminar.

Causality is dealing with fundamental questions about cause and effect. The student seminar covers statistical and mathematical aspects of causality ranging from fundamental formalization of concepts to practical algorithms and methods.

The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ACVF and ACF, Estimation of trend and seasonal component, Linear processes, ARMA processes, Forecasting and estimation of a missing value, the Innovation Algorithm.

The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ARMA processes, Spectral Analysis, Forecasting, Nonstationary Models, ARIMA Models and an introduction to GARCH models.