Data Analytics in Practice

Module code: EC3027

This course is designed to equip you with both theoretical and practical skills in machine learning and artificial intelligence, particularly their applications in economics. The course is divided into two blocks.

In the first half, the focus is on the foundations of machine learning. Topics covered include classification, regression, model evaluation, random forests, and ensemble learning. Throughout this block, you will develop a rigorous understanding of core predictive modeling techniques and the methodological principles that underlie their application to economic data.

In the second half, the course shifts to the foundations of artificial intelligence. This section covers neural networks, large language models, and reinforcement learning. Building on the modeling foundations established earlier, you will discuss the theoretical aspects and practical implementation of these advanced architectures, particularly within the context of contemporary economic applications.

By the end of the module, you will have a solid foundation in several machine learning methods, enabling you to evaluate and apply these tools to economic analysis.