Course summary

As industries and public sector organisations integrate large datasets into decision-making, the demand for graduates who combine economic reasoning and causal analysis with data science tools has never been greater. Our MSc Economics, Artificial Intelligence and Data Science programme provides advanced training for those looking to progress into careers as a professional economist in the financial and public sectors, regulation, research, and consulting - or as a pathway to PhD study. What makes this course different? At the intersection of economics, artificial intelligence and data science lies the future of how we analyse markets, inform policy, and solve complex problems. This programme offers rigorous training in microeconomics and macroeconomics, alongside economics-focused data science and AI modules. It also delivers cutting-edge data science techniques through interdisciplinary collaboration with the School of Computer Science.

• Learn from top economists and computer scientists, gaining critical insights into economic theory, computational thinking, and real-world applications for competitive advantage.

• You'll master causal machine learning and economic analysis

• Develop the skills needed to understand research at the frontiers of the discipline

• Neural Computation Module: Master the fundamentals of neural computation and how it related to automated learning in computing machines. You'll gain practical experience of designing and implementing a neural network for a real world application.

• Data-Intensive AI Module: Learn how to collect, engineer, and deploy large datasets for training machine learning and artificial intelligence models. Through hands-on experience with tools and programming, you'll master the algorithms that power data-driven artificial intelligence while exploring critical issues of fairness, ethics, and responsible AI deployment across different data types.

• Machine Learning and Economics Module: This hands-on module equips you with the tools to analyse economic data, build causal models, and make informed decisions in complex environments.