Hart und Trocken
Interactive learning platform for mathematics, computer science, statistics, and data science – founded in 2019 together with Prof. Volker Reichenberger.
What is Hart und Trocken?
Hart und Trocken is an interactive learning platform for mathematics, computer science, statistics, and data science. The platform is aimed at students – particularly those in engineering and business programmes – who want complex technical topics presented in a structured and accessible way.
The name says it all: the content is technically precise and straight to the point. Instead of superficial simplifications, you get real mathematics – explained in a way that leads to genuine understanding.
Content
The platform offers instructional videos and interactive materials on the following topics:
- Mathematics 1 & 2 – Calculus, linear algebra, differential calculus
- Differential Equations – ordinary ODEs, initial value problems
- Computer Science 1 – fundamentals of programming and algorithms
- Statistics – probability theory, estimation theory, hypothesis testing
- Data Analysis I & II – in English, for international degree programmes
- Managerial Statistics – applied statistics in a business context
The videos are freely available on YouTube and can be accessed in a structured way through the platform.
Origins
Hart und Trocken was founded in 2019 by Prof. Dr. Dirk Schieborn and Prof. Volker Reichenberger at Reutlingen University. The starting point was the conviction that good teaching can – and should – reach beyond the lecture hall.
Both founders bring years of experience in research and industry: from quantitative risk management and AI applications in finance to the development of forecasting methods in industry.
In 2020, they were recognised for their innovative teaching approach with the Teaching Award of Reutlingen University.
Podcast
Alongside the platform, there is the “Hart und Trocken” podcast, which covers topics less suited to the video format – complex research questions, current developments in AI, and insights into the practice of data science.