Financial Analytics is an in-depth course designed to explore the analysis, exploration, and simplification of large and complex datasets. This course arms students with the essential skills to model and derive insights from data, enabling the development of robust predictive and classification models. The curriculum encompasses core concepts and methodologies, such as hypothesis testing, confidence intervals, linear and logistic regression, model selection, multinomial and binary regression, clustering, factor models, and decision trees. A strong emphasis is placed on practical computational skills and the fundamental principles underpinning these methods. Students will actively engage with actual financial datasets, applying their knowledge to develop tailored methodologies for specific applications.
