The curricular unit builds upon the courses Statistical Data Analysis and Modeling (AMED) and Forecasting Methods (MP). The former introduces preprocessing, elementary statistics, and the application of some regression models. The latter focuses on time series analysis. This new course integrates and expands these foundations, with emphasis on classification, regression, and clustering through statistical and computational models. The objectives include:

• developing skills in critical analysis, model selection and evaluation;
• applying supervised and unsupervised techniques;
• implementing statistical and computational methodologies in R or Python;
• fostering autonomy and transversal competences in applied work.
 

Teaching combines theoretical exposition, guided practical exercises, case studies, and independent work, ensuring coherence between objectives and methodologies.