Stefan has applied data science to business, investment and policy decisions since 2001. He has built an early warning system for financial crises, and worked as advisor to Central Banks and the World Bank before moving into fintech, where he focused on data-driven product and market strategy at Rêv Worldwide, a payments tech company with operations in 15 countries. In 2013, he co-founded Infusive Ltd, a global consumer good investment company, and built the investment intelligence and predictive analytics capabilities behind several equity funds and VC investments. He also founded Applied AI that leverages ML algorithms for the finance and consumer industries, and co-founded Digital Natives to introduce state-of-the-art technology education in Mexico.
Stefan mostly uses Python for data science, machine learning and web development, alongside SQL, R and Stata, as well as the Hadoop stack and Spark when the data gets bigger. He holds Master degrees in Quantitative Economics from the Free University of Berlin and Harvard University, is a CFA Charterholder, and has worked in six (natural) languages.
In this part-time course, students learn to build robust predictive models, test their validity, and clearly communicate resulting insights.
This 10-week course will teach you how to use large datasets to make critical decisions.