MSci Mathematics and Data Science
Entry requirements
A-Level: AAA-AAB IB: 36/666-34/665 BTEC: DDD
About this course
MSci in Mathematics and Data Science, a mathematics and computer science degree exploring mathematical techniques alongside applications in data science. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, Project Management, Critical & Analytical Thinking, Artificial Intelligence & Machine Learning. The curriculum is structured over four years, culminating at master level and including options for a commercial or industrial experience placement. The first year establishes core knowledge through Fundamentals of Machine Learning, Programming, Social and Professional Issues of the Information Age, Object-Oriented Programming, Foundations, Mathematical Methods, and Probability, Statistics and Data. The second year develops technical competence via Machine Learning and Data Science, Team Project, Differential Equations, Vector Calculus and Applications, and Statistical Modelling and Inference, alongside optional modules such as Computational Intelligence and Mathematics of Machine Learning and AI. The third year centres on the Individual Literature Review and Project, complemented by numerous optional subjects spanning Data Science at Scale, Probabilistic Machine Learning, Fluid Dynamics, Statistical Inference, and Stochastic Processes. Optional choices allow specialisation across diverse applied branches of mathematics and computational analysis in the final stages of the programme.
Modules
- Fundamentals of Machine Learning
- Programming
- Social and Professional Issues of the Information Age
- Object-Oriented Programming
- Foundations
- Mathematical Methods
- Probability, Statistics and Data
- Machine Learning and Data Science
- Team Project
- Differential Equations
- Vector Calculus and Applications
- Statistical Modelling and Inference
- Computational Intelligence
- Mathematics of Machine Learning and AI
- Individual Literature Review and Project
- Data Science at Scale
- Probabilistic Machine Learning
- Theory of Weather and Climate
- Mathematical Biology and Ecology
- Fluid Dynamics
- Partial Differential Equations
- Mathematics: History and Culture
- Graphs, Networks and Algorithms
- Stochastic Processes
- Statistical Inference
- Mathematics of Climate Change
- Computational Nonlinear Dynamics
- Bayesian Statistics, Philosophy and Practice
- Integral Equations
- Statistical Computing
- Dynamical Systems and Chaos
- Statistical Data Modelling
- Commercial and Industrial Experience