Data science surrounds us in contexts as diverse as climate change, air pollution, route-finding, genomics, market manipulation, and movie recommendations. To open the "data-science-black-box" for lower secondary school students, we developed a data science teaching unit focusing on the analysis of environmental data, which we embedded in a ninth-grade computer science class. In this unit, students experience a new insight-driven programming approach, using Jupyter Notebook and the programming language Python for their data analysis. In this paper, we evaluate the second cycle of this project, report how the students coped with the Jupyter Notebooks for doing statistical investigations and describe the insights they gained.