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In this paper, we will describe an introduction to Data Science for secondary school students. We will report on the design and implementation of an introductory unit on “Data and data detectives with CODAP” in which secondary school students used the online tool CODAP to explore real and meaningful survey data on leisure time activities and media use (so‐called JIM‐PB data) in a statistical project setting as a starting point for data science. The JIM‐PB data set served as a valuable data set that offered meaningful and exciting opportunities for data exploration for secondary school students, and CODAP proved to be a valuable tool for the first explorations of this data.

In the setting of design-based research, the second version of an experimental course on data science is implemented accompanied by research. The three modules of the course focus on “data and data detectives”, “machine learning” and a combination of both in working on a final project. In this paper, we will focus on the topic “decision trees” which is part of “machine learning”. The students learn approaches of how to build decision trees manually from data using the tree plugin of CODAP. Further on, they learn to design and code an algorithm with Python that automatically generates trees. Afterwards, the algorithm is applied to real data sets with the support of Jupyter Notebooks. The instructional approach provides a deep content knowledge, which also serves as a basis for discussing the difference between humans’ and machines’ building decision trees and the societal implications of implementing them in practice.