MMath Mathematics
Entry requirements
A level: AAA or AABB including Mathematics (grade A) IB: Pass, with 36 points overall with 18 at Higher Level, including 6 points from Higher Level Mathematics (Preferred Mathematics module is Analysis and Approaches, but Applications and Interpretation also considered)
About this course
Master of Mathematics in Mathematics, a mathematics degree centred on advanced mathematical and statistical concepts. Core topics include Programming, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, Research Methods, Project Management, Environmental Analysis, and Artificial Intelligence & Machine Learning. The programme shares its first three years with related mathematics degrees and features compulsory individual research projects in the third and fourth years. The first year covers foundational methods through compulsory modules including Calculus I, Calculus II, Computational Mathematics, and Linear Algebra I. The second year progresses into specialized areas with compulsory modules such as Analysis and Partial Differential Equations, alongside optional modules including Algorithms, Financial Mathematics, and Fundamentals of Data Science in R. In the third year, students undertake a compulsory Mathematics Project alongside advanced optional studies in fields such as Machine Learning, Mathematical Biology, and Quantum Information. The fourth year centers on an MMath Project and advanced study options encompassing modules like Advanced General Relativity, Data Mining and Analytics, and Differential Geometry and Lie Groups.
Modules
- Calculus I
- Calculus II
- Computational Mathematics
- Core Skills for Mathematicians I
- Dynamics and Relativity
- First Year Mathematics Workshop
- Introduction to Statistics
- Linear Algebra I
- Linear Algebra II
- Number Theory
- Analysis
- Core Skills for Mathematicians II
- Partial Differential Equations
- Algorithms
- Fields and Fluids
- Financial Mathematics
- Fundamentals of Data Science in R
- Geometry and Topology
- Graph Theory
- Group Theory
- Introduction to Operational Research
- Numerical Analysis
- Statistical Inference
- Statistical Modelling I
- Stochastic Processes
- Vector Calculus and Complex Variable Theory
- Mathematics Project
- Actuarial Mathematics I
- Actuarial Mathematics II
- Advanced Fluid Dynamics
- Advanced Partial Differential Equations
- Algebraic Topology
- Complex Analysis
- Complex and Integral Transform Methods
- Computational Statistical Inference
- Design and Analysis of Experiments
- Further Number Theory and Cryptography
- Galois Theory
- Geometry and Data
- Hilbert Spaces
- Infinite Groups
- Learning and Teaching Mathematics
- Machine Learning
- Mathematical Biology
- Mathematical Finance
- Mathematical Programming
- Numerical Partial Differential Equations
- Optimization
- Project Management
- Relativity, Black Holes and Cosmology
- Statistical Methods in Insurance
- Statistical Modelling II
- Structure and Dynamics of Networks
- Survival Models
- MMath Project
- Actuarial Mathematics I
- Actuarial Mathematics II
- Advanced General Relativity
- Advanced Operational Research Methods
- Advanced Partial Differential Equations
- Complex And Integral Transform Methods
- Computational Machine Learning and Optimisation
- Data Mining and Analytics
- Design of Experiments
- Differential Geometry and Lie Groups
- Financial Mathematics
- Flexible Regression
- Geometric Group Theory
- Gravitational Waves
- Harmonic Analysis and Quantum Information
- Homotopy and Homology
- Hyperbolic Geometry
- Introduction to Semigroup Theory
- Machine Learning
- Modelling with Differential Equations
- Modules and Representations
- Numerical Partial Differential Equations
- Structure and Dynamics of Networks