Insights into the design of an introductory course for data science and machine learning for engineering students

Katharina Bata (1) , Angela Schmitz (1) , Andreas Eichler (2)

1 Universität zu Köln = University of Cologne
2 University of Kassel

Due to their interdisciplinary nature, data science methods, such as machine learning, can be taught in many different ways. This paper presents an approach that takes advantage of the close content connection to statistics and of the mathematical structure of data science methods to develop an introductory course for engineering students. Following the research methodology of design research, we discuss the theoretical motivation and methodological implementation of the design principles for the course and show first insights into empirical results from the design cycles.

Mots clés: Data Science; Machine Learning; Design Research; SRLE

Domaines: Mathématiques [math]

Katharina Bata, Angela Schmitz, Andreas Eichler. Insights into the design of an introductory course for data science and machine learning for engineering students. Twelfth Congress of the European Society for Research in Mathematics Education (CERME12), Feb 2022, Bozen-Bolzano, Italy. ⟨hal-03751807⟩