BSc Mathematics and Statistics
Entry requirements
A level: AAA. This should include Mathematics grade A or Further Mathematics grade A. The overall offer grades will be lowered to AAB for applicants who achieve both Mathematics and Further Mathematics at grades AB, in either order. IB: 36 points overall with 16 points from the best 3 HL subjects including 6 in Mathematics HL (either analysis and approaches or applications and interpretations)
About this course
BSc Hons in Mathematics and Statistics, a mathematics and mathematics and statistics degree centred on the exploration of mathematical and statistical methods and their applications. Core topics include Programming, Signal Processing, Data Analysis, Statistics, Mathematical Modelling, Accounting, Critical & Analytical Thinking, Human Behaviour, Artificial Intelligence & Machine Learning. The curriculum begins with a fixed set of mandatory modules alongside broader options, transitioning through collaborative project work in the second year towards specialized paths in the final stage. The first year covers the foundations through Matrices and Calculus, Probability and Statistics, Logic and Discrete Mathematics, and Symmetry and Sequences, with additional choices available in subjects like Mathematical Modelling and Programming. The second year advances core knowledge through Multivariate Probability and Statistics, Applied Data Science, Project Skills, Linear Algebra, and Real Analysis, complemented by a single optional choice such as Mathematics of Artificial Intelligence. In the final year, students complete core studies in Statistical Inference and Statistical Learning and Prediction, while tailoring their degree through a wide selection of optional modules covering areas such as Medical Statistics, Changepoint and Time Series Analysis, Environmental Statistics, Mathematical Finance, Mathematics of Generative Modelling, Stochastic Processes, Dynamic Modelling, Graph Theory and Algorithms, Mathematical Cryptography, Mathematical Education, Mathematical Education Placement, Linear Systems, and Optimisation for Machine Learning.
Modules
- Matrices and Calculus
- Probability and Statistics
- Logic and Discrete Mathematics
- Symmetry and Sequences
- Mathematical Modelling and Programming
- Multivariate Calculus
- Computing Essentials: Programming
- Computing for Business Decision Making
- Managing Uncertainty in Business
- Principles of Macroeconomics
- Principles of Microeconomics
- The Physical Universe
- Fields, Matter and Quantum Physics
- Languages
- Foundations of Accounting and Finance
- Multivariate Probability and Statistics
- Applied Data Science
- Project Skills
- Linear Algebra
- Real Analysis
- Mathematics of Artificial Intelligence
- Abstract Algebra
- Complex Analysis
- Intermediate Accounting and Finance
- Statistical Inference
- Statistical Learning and Prediction
- Medical Statistics
- Changepoint and Time Series Analysis
- Environmental Statistics
- Mathematical Finance
- Mathematics of Generative Modelling
- Stochastic Processes
- Dynamic Modelling
- Graph Theory and Algorithms
- Mathematical Cryptography
- Mathematical Education
- Mathematical Education Placement
- Linear Systems
- Optimisation for Machine Learning