Project-based learning with Python to analyze poverty in Ecuador

Abstract

This study designed and implemented a Project-Based Learning strategy for software engineering education, integrating Python and Jupyter with the analysis of real-world social data. The research employed the study of multidimensional poverty in Ecuador as an application case, using official datasets from the National Institute of Statistics and Censuses. The methodology combined a technical component, founded on a reproducible pipeline for data cleaning and visualization, with a pedagogical component implemented with a student cohort. Results indicated that the strategy effectively developed technical programming and data management competencies, while enabling students to produce meaningful analyses that identified relevant social patterns. The experience demonstrated that incorporating authentic social issues enhances engineering education by bridging technical practice with an understanding of national context. This model represents a viable alternative for educating professionals with substantial technical skills and heightened social awareness, and its replication in other educational settings is recommended.