Teaching experience

I have many years of experience in giving basic and advanced lectures on Statistics, Data Science and Mathematics for students of various disciplines. In these lectures, it is essential for me to get students excited about Statistics and its applications. I embed business and industrial collaborations into my teaching to help students understand the practical relevance of the methodology.

I have taught at various institutions, including the University of Hamburg, Leuphana University, Hamburg University of Applied Sciences, Harz University of Applied Sciences, and Toulouse School of Management. Evaluations of my lectures consistently achieve very good results, and for my commitment to university teaching I have been awarded the 10,000€ Excellence in Higher Education Award twice.

B.Sc. introductory lectures (up to 400 students)

• Preparatory course for Mathematics and Statistics

• Preparatory course for higher Mathematics

B.Sc. basic lectures (up to 1,200 students)

• Economics

• Mathematics for Business and Economics I

• Mathematics for Business and Economics II

• Microeconomics

• Quantitative Methods

• Statistics for Business and Economics I

• Statistics for Business and Economics II

B.Sc. advanced lectures (up to 50 students)

• Adaptive Methods of Statistical Inference

• Business Intelligence & Data Analytics

• Data Science

• Evaluation and Statistical Forecasting

• Introduction to Quantitative Risk Management

• Marketing Research

• Methods for Decision Making

• Multivariate Statistics

• Regression Analysis

• Statistical Learning for Business and Economics

• Time Series Analysis

M.Sc. lectures (up to 50 students)

• Actuarial Models and Methods in Non-Life Insurance

• Empirical Economic Research and Economic Policy

• Financial Econometrics

• Quantitative Finance

• Quantitative Methods

• Quantitative Risk Management

• Stochastic Claims Reserving

Seminars (up to 20 students)

• Industrial Data Science

• Multivariate Statistics for Business and Economics

• Predictive Analytics

• Quantitative Risk Management

• Regression Models with Applications in Insurance and Finance

• Statistical Learning

• Statistical Literacy – Misuse of Statistics and its Consequences

• Time Series Models with Applications in Business and Economics

Method-supporting software (up to 20 students)

• Advanced LaTeX

• Introduction to LaTeX

• Introduction to MATLAB

• Statistics with R

• Statistics with SPSS