BSc 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
Bachelor of Science in Mathematics, a mathematics degree focused on analytical, statistical, and computational methods. Core topics include programming, signal processing, data analysis, statistics, mathematical modelling, project management, environmental analysis, and artificial intelligence & machine learning. Students complete a compulsory final-year project on a chosen mathematical topic. The first year establishes core knowledge through modules such as Calculus I, Calculus II, Computational Mathematics, and Linear Algebra I. The second year introduces foundational studies like Analysis and Partial Differential Equations, alongside optional subjects including Algorithms, Geometry and Topology, and Statistical Inference. In the final year, students undertake the Mathematics Project and can select from various advanced options such as Machine Learning, Mathematical Finance, Advanced Fluid Dynamics, and Project Management.
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
- 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