BSc Mathematics and Economics
Entry requirements
A level: AAB IB: 34 points
About this course
BSc Honours in Mathematics and Economics, combining mathematical methods with economic analysis. Core topics include Statistics, Mathematical Modelling, Financial Analysis, Programming, Data Analysis, and Artificial Intelligence & Machine Learning. Modules include Stochastic Processes, Data Visualisation, and Principles of Quantum Mechanics alongside economic theory. The programme combines these disciplines equally, with tuition from the School of Mathematics, Statistics and Physics and a Business School holding triple-crown accreditation.
Modules
- Economic Analysis
- Economic Applications
- Introductory Algebra
- Real Analysis
- Introduction to Probability and Statistics
- Introductory Calculus and Differential Equations
- Multivariable Calculus
- Microeconomic Analysis
- Macroeconomic Analysis
- Frontiers in Data Science A
- Linear Algebra
- Complex Analysis
- Groups and Rings
- Curves and Surfaces
- Coding Theory
- Numerical Methods with Python
- Statistical Inference
- Stochastic Processes
- Data Visualisation
- Probability
- Regression
- Principles of Quantum Mechanics
- Vector Calculus
- Differential Equations, Transforms and Waves
- Fluid Dynamics I
- Advanced Microeconomics
- Advanced Macroeconomics
- Behavioural Economics and Experimental Methods
- Monetary Economics
- Public Economics
- Financial Economics
- Economics of Risk and Uncertainty
- Health Economics
- Industrial Economics and Policy
- Game Theory
- Development Economics
- Happiness Economics
- Environmental Economics
- Clinical Trials
- Decision Modelling for Health Data Science
- Topics in Medical Statistics and Health Data Science
- Curves and Surfaces
- Coding Theory
- Numerical Methods with Python
- Global Education in Mathematics and Statistics
- Group Theory
- Linear Analysis
- Matrix Analysis
- Metric Spaces and Topology
- Number Theory and Cryptography
- Matrix Representations of Groups
- Stochastic Financial Modelling
- Experimental Design
- Foundations of Machine Learning
- Extreme Value Theory
- Time Series
- Survival Analysis
- Statistical Genetics
- Mathematical Statistics
- Statistical Modelling
- Bayesian Statistics and Decision Theory
- Markov Processes
- Principles of Quantum Mechanics
- Mathematical Biology
- Advanced Quantum Mechanics
- Classical Fields
- Quantum Information
- Methods for Differential Equations
- Fluid Dynamics II
- Relativity and Fundamental Particles
- Hydrodynamic and Climate Instabilities
- Career Development for final year students