This chapter presents the design and development of computer-based tools using Python to enhance the teaching and learning of econometrics for undergraduate students. Econometrics, by nature, requires a robust understanding of both economic theory and statistical techniques. However, students often struggle with the abstract and technical components of the discipline, particularly when faced with complex models and datasets. To address these challenges, we propose a set of interactive, modular tools built in Python, which enable students to visualize econometric concepts, manipulate data, and experiment with regression models in a user-friendly environment. The developed tools include visual dashboards for understanding ordinary least squares estimation, simulations for hypothesis testing, and modules that demonstrate the effects of multicollinearity, heteroscedasticity, and autocorrelation. Each tool is accompanied by guided exercises, promoting active learning and immediate application of theoretical concepts. These resources are designed to be easily integrated into standard econometrics curricula and can be adapted to suit different teaching styles and institutional contexts. The pedagogical approach underpinning this initiative is constructivist, emphasizing learning by doing. Through Pythons open-source ecosystem—notably libraries such as pandas, statsmodels, or matplotlib—students gain not only a deeper understanding of econometric principles but also valuable programming skills relevant to modern data analysis. This approach has the potential to democratize access to high-quality econometric instruction, particularly in institutions with limited resources or students with diverse academic backgrounds. Future work includes the expansion of the toolset and longitudinal studies to assess its impact on student performance and skill acquisition over time.