After successful completion of the course, students are able to explain statistical methods that are important in the field of actuarial mathematics, and to apply them to problems of practical importance.

• Data Science (incl. data collection, data structures, databases, visualization, numerical and ethical as well as regulatory aspects)
• non-parametric data analysis, balancing methods
• Deep Learning
• Stochastic risk modelling, copulas
• Deepening of statistical modelling of damage figures
• Deepening of the credibility theory
• Application of regression and classification together with the above techniques to actuarial problems, in particular:
• Risk classification
• Bonus-malus systems
• Calculation bases (incl. transition probabilities, Kaplan-Meier and Nelson-Aalen estimators, etc.)
• Life tables (adaptation/compensation, estimation, generational life tables, projection, disaster scenarios, etc.)

Lecture, solving of problems

Grading of exercise problems and oral exam