Treffer: Innovative Learning:Data Science Education with Local Knowledge and Real-World Data

This presentation outlines a project-based framework for teaching mathematics and data science using Python, Jupyter Notebooks, and cloud platforms like Google Colab. By leveraging real and locally relevant datasets—such as climate data from Norway and weather station data from Uganda—the approach fosters contextual learning, critical thinking, and interdisciplinary engagement. AI-assisted coding environments streamline development, allowing students to focus on validation and interpretation. The framework supports both education and research, with applications ranging from epidemiological modelling to fractal generation. Recognizing the challenge of limited data access in many African contexts, the paper highlights publicly available socio-economic data sources such as ECAStats, MyNBER, Statistics Norway (SSB), Uganda Bureau of Statistics (UBOS), and Tanzania’s National Bureau of Statistics (NBS). These resources offer opportunities to expand the scope of data-driven learning and research. The framework aligns with the goals of the MATH4SDG project and demonstrates how open-source tools, AI integration, and local data can enhance mathematics education and support sustainable development.