Knowledge is best gained from hands-on experience and interactive tinkering.


We believe that knowledge is best built and solidified by fiddling, tinkering with code, algorithms, and formulas, to get a hands-on “feeling” for the abstract relations that govern complex systems. Our courses consist of interactive notebooks that run directly in the browser and allow the participants to completely reproduce the content of lectures and exercises on their own, and further to extend it, change it, and adapt it to their problems at hand. Especially for highly technical or mathematical topics, we believe that a code-first approach is superior to a lengthy discussion of mathematical backgrounds. We find that participants often understand the mathematical details of a problem better after coding and simulating the problem at hand. This technology is very flexible with regard to the format of the courses:

Self-study online courses: get access to the notebooks and start coding/learning with one click.

Online seminars: code alongside an experienced instructor who guides you through the notebooks.

Online / in-person training: work through the notebooks & exercises in a small group with an instructor.

Current course list

Courses are currently under preparation and stated lecture contents are subject to further changes. A free preview on selected lectures will be available shortly. Sign up for our newsletter to get informed about the release of new courses! All courses are offered in English or German language. If you have any questions about course contents or suggestions for new courses/lectures, do not hesitate to contact us!


Financial markets, businesses, and even every day life require us to make repeated decisions that are subject to uncertainty and that have some potential payoff or loss. How can we quantify the risk inherent to repeated decisions to ensure a positive long-term profit and avoid devastating losses? Financial markets are a prime example of an inter-connected complex system that shows anomalous statistical properties which cannot be adequately captured by standard approaches such as Modern Portfolio Theory. This draws on the concepts of extreme value theory, complex systems research, Bayesian modeling, and the Austrian investing approach. FIND OUT MORE


Probabilistic programming represents a very flexible, powerful approach to gain insights even into small data sets with a low signal-to-noise ratio. However, writing probabilistic programs that correctly capture the underlying data generation process can be confusing at first. This course provides a code-first, math-second introduction into the process of creating, running, and evaluating probabilistic programs written in Python and the probabilistic programming language PyMC. Sign up for out newsletter to get notified when this course will be available!

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