BSc Data Science
Entry requirements
A level: A*AA (A* in Mathematics required. Further Mathematics preferred. If you are studying both then the A* can be in either subject. Other preferred subjects include Chemistry, Economics, Physics and Statistics.) IB: 39 points (A total of 19 points in three higher level subjects including grade 7 in Mathematics, with no higher level score below 5. The programme will accept either ‘Mathematics: Analysis and Approaches’ or ‘Mathematics: Applications and Interpretation’ at higher level. Further Mathematics, Chemistry, Economics, Physics and Statistics preferred.)
About this course
BSc in Data Science, a mathematics and statistics degree focused on the statistical foundation and computational methods of modern data analysis. Core topics include Programming, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. Students complete a major final-year project. The first year covers foundational concepts through Database Systems A, Calculus and Linear Algebra, Introduction to Probability and Statistics, and Programming Fundamentals. The second year advances core knowledge with Advanced Linear Algebra, Probability and Inference, Regression Modelling, and Algorithms and Data Structures, while offering optional modules such as Mathematical Analysis. The final year involves compulsory modules like Statistical Machine Learning and Inference at Scale, alongside optional choices spanning software engineering, financial methods, and medical statistics.
Modules
- Database Systems A
- Calculus and Linear Algebra
- Calculus in Several Dimensions
- Introduction to Probability and Statistics
- Further Probability and Statistics
- Introduction to Practical Statistics
- Programming Fundamentals
- Advanced Linear Algebra
- Probability and Inference
- Regression Modelling
- Computing for Practical Statistics
- Algorithms and Data Structures
- Statistical Design and Data Ethics
- Mathematical Analysis
- Introduction to Stochastic Processes
- Decision and Risk
- Project
- Statistical Machine Learning
- Inference at Scale
- Software Engineering
- Database and Information Management Systems
- Numerical Methods
- Introduction to Stochastic Processes
- Statistical Inference
- Stochastic Systems
- Forecasting
- Decision and Risk
- Stochastic Methods in Finance
- Medical Statistics 1