BSc Mathematical Sciences
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 Mathematical Sciences, a mathematics degree centred on quantitative techniques, abstract structures, and computational methods. Core topics include Programming, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, Project Management, Environmental Analysis, and Artificial Intelligence & Machine Learning. The course includes a compulsory final-year project providing an opportunity to independently research an area of mathematics. The first year establishes foundations through compulsory modules such as Calculus I, Calculus II, Computational Mathematics, Dynamics and Relativity, Introduction to Statistics, Linear Algebra I, Linear Algebra II, and Number Theory. The second year introduces core analytical techniques and differential equations via modules like Analysis and Partial Differential Equations, alongside optional subjects covering algorithms, data science in R, and statistical modelling. In the third year, students complete a compulsory Mathematics Project and can select from a wide range of advanced options spanning machine learning, mathematical biology, fluid dynamics, and financial mathematics.
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
- Astronomy For Everyone
- Fields and Fluids
- Fundamentals of Data Science in R
- Geometry and Topology
- Global Sustainability Challenges
- 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