MMath Applied Mathematics
Entry requirements
A level: from A*A*A* to A*AB in one set of exams (required: Mathematics at A*) IB: from 38 points with 766 at HL to 34 points with 765 at HL
About this course
MMath (Hons) in Applied Mathematics, a mathematics degree focused on advanced analytical techniques and quantitative problem-solving. Core topics include Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. Students can transfer between the Bachelor and Master routes up to the start of the fourth year, and study non-mathematical subjects externally during the first two years. The first year establishes university-level foundations through Introduction to Mathematics at University, Introduction to Mathematical Analysis, Linear Algebra 1, and Introduction to Data Science. The second year progresses with Linear Algebra 2, Elementary Probability and Statistics, Further Analysis and Several Variable Calculus, and Modelling and Computing. Year 3 expands into specialized branches with modules such as Statistics, Topology, Algebra, Analysis, Operational Research, Differential Equations, Numerical Analysis, Probability and Measure Theory, Financial Mathematics, Introduction to Number Theory, and Statistical Computing. The final year involves advanced study across applied mathematics, statistics, operational research, financial mathematics, mathematical biology, and mathematical education, alongside options like Stochastic Modelling, Mathematical Biology, Machine Learning in Python, and Applied Dynamical Systems.
Modules
- Introduction to Mathematics at University
- Introduction to Mathematical Analysis
- Linear Algebra 1
- Introduction to Data Science: you will learn to collect and explore data, before using models and predictions to make rigorous conclusions
- Fundamentals of Algebra and Calculus: an online, introductory course that provides extra preparation in key topics from advanced high school level mathematics and further supports your transition to university
- Linear Algebra 2
- Elementary Probability and Statistics
- Further Analysis and Several Variable Calculus
- Modelling and Computing
- Statistics
- Topology
- Algebra
- Analysis
- Operational Research
- Differential Equations
- Numerical Analysis
- Probability and Measure Theory
- Financial Mathematics
- Introduction to Number Theory
- Statistical Computing
- applied mathematics
- statistics
- operational research
- financial mathematics
- mathematical biology
- mathematical education
- Stochastic Modelling
- Machine Learning in Python
- Applied Dynamical Systems