BSc Mathematics, Statistics and Data Science
Entry requirements
A Level: AAB to include Mathematics or Further Mathematics.. IB: 6,6,5 at Higher Level, including Mathematics, with a minimum of 32 points overall.. BTEC: only considered when combined with other qualifications.
About this course
Bachelor of Science in Mathematics, Statistics and Data Science, a mathematics degree focusing on analytical methods and computational techniques. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. Students complete a final-year project. The first year covers core foundations through Differential Equations and Mechanics, Mathematical Discovery: Problem Solving and Programming, Mathematical Foundations, Calculus and Linear Algebra, and Probability, Data and Statistics. The second year introduces Data Science and Machine Learning, Mathematical Challenge: Problem Solving and Programming in Business and Industry, Statistics, and Artificial Intelligence. In the final year, students undertake a Mathematical Project alongside optional modules such as Deep Learning and Neural Networks, Medical Statistics, and Number Theory and Cryptography.
Modules
- Differential Equations and Mechanics
- Mathematical Discovery: Problem Solving and Programming
- Mathematical Foundations, Calculus and Linear Algebra
- Probability, Data and Statistics
- Mathematical Methods and Applications
- Real Analysis
- Data Science and Machine Learning
- Mathematical Challenge: Problem Solving and Programming in Business and Industry
- Statistics
- Multivariable and Vector Calculus
- Applied Linear Algebra and Optimisation
- Artificial Intelligence
- Graphs and Algorithms
- Groups, Rings and Fields
- Mathematical Modelling with Differential Equations
- Real Analysis
- Mathematical Project
- Combinatorics and Computation
- Continuum Mechanics
- Deep Learning and Neural Networks
- Graph Theory and Applications
- Mathematics of Financial Derivatives
- Medical Statistics
- Number Theory and Cryptography
- Optimisation
- Partial Differential Equations and Applications
- Statistical Machine Learning
- Statistical Modelling