The aims of this module are to:

• Provide a mathematical understanding of the Lebesgue measure and integration.
• Generalise concepts to abstract measure spaces.
• Build a solid rigorous mathematical background for students to proceed to stochastic analysis and financial mathematics.

The aim of this module is to:

• To provide students with a rigorous mathematical introduction to the modern financial theory of security markets in discrete and continuous time models.
• To give students a solid theoretical background in the derivatives industry in discrete and continuous time models.

The aim of this module is to provide students with an understanding of the main models used for macroeconomic policy formulation at the graduate level.

The aim of this module is to provide students with a solid foundation of the econometric techniques and skills that form the basis for the quantitative/econometric modules of their master's course. The module aims to provide students, through lectures and computer lab workshops, with the practical techniques economists often use to handle, analyse and interpret economic data.

The aim of this module is to introduce, explain, and implement numerical methods for solving partial differential equations.

This module introduces students to the emerging field of data science and equips them with the fundamental knowledge of using data to gain insights and support decision-making.

The module demonstrates and provides hands-on experience with cleaning, integrating, exploring, transforming and summarising data sets.

It teaches students to form questions and hypotheses from data; to utilise and apply a variety of statistical methods to effectively analyse data in a way that answers those questions or hypotheses; and to create suitable visualisations to communicate their analyses. By the end of the module students will in analysing and presenting data using R and RStudio.