Chair of Spatial Data Science and Statistical Learning

Teaching

Courses in the winter semester 2024/25

In this course, the first introductions to in-depth inference approaches and modeling techniques are discussed. This includes an excursion into Bayesian statistics and various learning methods, as well as modeling approaches beyond linear models, such as quantile regression.
This course offers an introduction to stochastic processes and spatial statistics.
This seminar discusses recent developments in the field of component-wise gradient boosting. The course is aimed at Master's students on the Applied Statistics course in the 3rd or 4th semester.
In the seminar, the students will get an introduction to a programming concept (like e.g. RShiny), which allows to implement interactive online tools for presenting statistical models. They will then work on and present their implementation of a project which has been chosen at the beginning of the semester. It will entail both, a complex data set and an advanced statistical method.
This seminar is designed for master's students with an interest in spatial modeling and Bayesian inference. It covers topics ranging from the approximation of Gaussian random fields or Thin Plate Splines to computational methods for spatial interpolation/surface reconstruction.

Courses in the summer semester 2024

This course is aimed at Master's students in the 3rd or 4th semester of the Master's program in Applied Statistics and deals with various topics from the field of multivariate statistics
Among other things, this course teaches content from the fields of generalized linear models, distribution regression and non-parametric regression and is aimed at students of the Master's program in Applied Statistics.
The aim of this course is to provide an introduction to the use of the statistical software R. First, the basic operation and use of the software and its syntax will be described. The possibilities of statistical analysis are then explained.
Students learn the basic concepts of descriptive, explorative and inductive statistics. They will be able to critically scrutinize the assumptions on which the methods are based and, based on this assessment, select a suitable method for a given problem and will be able to implement the methods covered in statistical software, interpret the results obtained and communicate the results to cooperation partners.
As part of the statistical modeling practical course, students work on an application problem in groups of up to four people using statistical modeling methods. The statistical modeling internship is usually carried out in cooperation with a practice partner.
At the conclusion of the course, students will have the ability to know fundamental concepts in statistical computing and address large-scale problems by employing efficient methods, considering the computational resources available.

Courses in the winter semester 2023/24

The statistics lecture in the Bachelor's degree programs in economics provides a general overview of statistical methods and is the basis for many advanced courses.
In this course, the first introductions to in-depth inference approaches and modeling techniques are discussed. This includes an excursion into Bayesian statistics and various learning methods, as well as modeling approaches beyond linear models, such as quantile regression.
This seminar discusses important work in the field of geostatistics and spatial modeling. The course is aimed at Master's students on the Applied Statistics course in the 3rd or 4th semester.
This course offers an introduction to stochastic processes and spatial statistics.

Courses in the summer semester 2023

This course is aimed at Master's students in the 3rd or 4th semester of the Master's program in Applied Statistics and deals with various topics from the field of multivariate statistics
Among other things, this course teaches content from the fields of generalized linear models, distribution regression and non-parametric regression and is aimed at students of the Master's program in Applied Statistics.

Courses in the winter semester 2022/23

This course offers an introduction to more advanced inference approaches and modeling techniques. This includes an excursion to bayesian statistics, various methods from the field of statistical learning, as well as modeling approaches beyond linear models, such as quantile regression.
This seminar covers important works in the area of geostatistics and spatial modelling. The course is aimed at students of the master's program in Applied Statistics in the 3rd or 4th semester.
This course introduces stochastic processes and spatial statistics.

Courses in the summer semester 2022

The course is aimed at students of the master's program in Applied Statistics in the 3rd or 4th semester and introduces topics from the field of multivariate statistics.
This course deals with topics from the fields of generalized linear models, distributional regression as well as non-parametric regression and is aimed at students of the master's program in Applied Statistics.
The statistics course aimed at students of the faculty of business and economics provides a general overview of statistical procedures and lays the foundation for many advanced courses.

Courses in the winter semester 2021/22

This course offers an introduction to more advanced inference approaches and modeling techniques. This includes an excursion to bayesian statistics, various methods from the field of statistical learning, as well as modeling approaches beyond linear models, such as quantile regression.
The statistics course aimed at students of the faculty of business and economics provides a general overview of statistical procedures and lays the foundation for many advanced courses.
This seminar covers important works in the area of statistical boosting. The respective articles deal with the development of the first boosting-based classification algorithms as well as modern model-based gradient boosting methods. The course is aimed at students of the master's program in Applied Statistics in the 3rd or 4th semester.
This course introduces stochastic processes and spatial statistics.

past courses


  • Multivariate Statistics (SoSe 2021; Dozent: Dr. Benjamin Säfken, Dr. Manuel Carlan)
  • DATA Science II: Statistik (Bachelor (SoSe 2021; Dozent: Dr. Benjamin Säfken)
  • Current Topics in Applied Statistics (WiSe 2020/2021; Prof.Dr. Philipp Otto)
  • Statistik (Bachelor) (WiSe 2020/2021; Prof.Dr. Philipp Otto, Dr. Alexander Silbersdorff, Anne Berner, Markus Fülle)
  • DATA Science II: Statistik (Bachelor) (SoSe 2020; Prof.Dr. Philipp Otto)
  • Statistik (Bachelor)(SoSe 2020; Prof.Dr. Philipp Otto, Dr. Alexander Silbersdorff)