Mestrado Bolonha em Investigação e Inovação para a Sustentabilidade

The curriculum for the Doto Science ond AI for Sustainability course is structured to align with learning outcomes, ensuring students acquire essential data analysis skills tailored to sustainability challenges. The course begins with an introduction to Python, its main libraries, and environments such as Jupyter Notebook and Spyder, while incorporating AI tools like Large Language Models (LLMs} and Generative AI to support programming and problem-solving. The integration of libraries like Pandas, NumPy, Matplotlib, and Seaborn strengthens proficiency in data manipulation and visualization, with a focus on sustainability data. From there, the course explores data importing from various formats and graphical representations to analyze environmental, social, and economic datasets. Subsequent sessions address data cleaning, transformation, and integration techniques, including handling missing data, outlier detection, and normalization. The curriculum also emphasizes exploratory data analysis, covering univariate and multivariate statistics applied to sustainabi/ity metrics such as carbon emissions, resource efficiency, and biodiversity trends. Finally, practical case studies with real-world sustainability data and group projects prepare students to apply their acquired skills to address pressing environmental and societal challenges.