COURSE CURRICULUM

The MSc Actuarial Management with Data Science program is structured to provide focused teaching across the first and second semesters, with students typically taking four courses per semester. The curriculum is designed to cover the core actuarial knowledge required for professional accreditation while integrating advanced data science skills. Students must complete courses linked to the exemption subject CP1, alongside two essential data science courses, Machine Learning for Risk and Insurance 1 and 2. In addition, students select courses to secure exemptions from two SP subjects, a requirement for qualification as a professional actuary. Optional courses allow students to tailor their studies and explore areas such as financial markets, derivatives, life and general insurance, pensions, quantitative risk analysis, and practical data science, providing flexibility to align with individual career goals. Across both semesters, students complete a total of eight courses, balancing core actuarial knowledge with machine learning applications for risk and insurance modelling. The program culminates in a dissertation undertaken in May, allowing students to apply their learning to a substantial, research-based project. Through this combination of actuarial theory, data science techniques, and practical project work, graduates are prepared for modern actuarial roles that demand analytical expertise, technical competence, and the ability to harness data-driven solutions in insurance, pensions, investment, and risk management sectors.