MSci Data Science
Entry requirements
A level: A*AA including Mathematics. If you are studying towards a fourth A level, we will make an alternative offer of AAAA including Mathematics. IB: 37 points overall, including grade 6 in Higher Level Mathematics (either Analysis and Approaches or Applications and Interpretations). BTEC: DDD plus grade A in A level Mathematics (or equivalent qualification) We consider a range of BTEC qualifications equivalent to 3 A Levels, or in combination with A Levels or other qualifications. For example: Distinction, Distinction in BTEC Level 3 National Diploma plus A in A Level Mathematics Distinction in BTEC Level 3 National Extended Certificate plus AA at A level including Mathematics Distinction, Distinction in 2 BTEC Level 3 National Extended Certificates plus A in A Level Mathematics
About this course
MSci (Hons) Data Science is an undergraduate computer science degree focused on computational and mathematical methods for information management. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. The programme includes a substantial final-year project and offers an optional placement year in industry. The first year establishes core foundations through modules such as Software 1: Foundations of Programming for Computer Science, Introduction to Probability and Statistics, and Foundations and Calculus. The second year expands into technical methods through Software and Systems Engineering, Probability and Markov Chains, and Intelligent Systems: Machine Learning and Optimisation. The third year advances into specialist areas with Cloud Based Data Analysis, Autonomous Robots, and Deep Learning. The final fourth year features advanced study alongside options such as High-Integrity Systems Engineering and Computational Finance with Python, culminating in major independent project work.
Modules
- Software 1: Foundations of Programming for Computer Science
- Introduction to Probability and Statistics
- Foundations and Calculus
- Software 2: Object Oriented Data Structures and Algorithms
- DATA: Introduction to Data Science
- Multivariable Calculus and Matrices
- Systems and Devices 2: Operating Systems, Security, and Networking
- Engineering 1: Software and Systems Engineering
- Probability and Markov Chains
- Intelligent Systems: Machine Learning and Optimisation
- Linear Algebra
- Statistical Inference and Linear Models
- Governance of Data Science
- Cloud Based Data Analysis
- AI Search and Logic
- Autonomous Robots
- Cryptography Theory and Practice
- Cryptography
- Engineering 2: Automated Software Engineering
- Deep Learning
- Interaction Design and Evaluation
- Qualitative Approaches to Investigating UX
- Quantum Computation
- Decision Theory and Bayesian Statistics
- Survival Analysis and Generalised Linear Models
- Mathematical Finance in Continuous Time
- Mathematical Finance in Discrete Time
- Advanced Regression and Multivariate Analysis
- Numerical Analysis
- Operations Research
- Statistical Data Science
- Time Series
- Engineering LLM-Based Agents and Applications
- Large Language Models
- Natural Language Processing
- Project: Mathematics & Computer Science
- Extended Independent Project: Mathematics
- AI Search and Logic
- Autonomous Robots
- Cryptography Theory and Practice
- Engineering 2: Automated Software Engineering
- High-Integrity Systems Engineering
- Deep Learning
- Interaction Design and Evaluation
- Qualitative Approaches to Investigating UX
- Quantum Computation
- Computational Finance with Python
- Decision Theory and Bayesian Statistics
- Survival Analysis and Generalised Linear Models
- Mathematical Finance in Discrete Time
- Mathematical Methods of Finance
- Advanced Regression and Multivariate Analysis
- Statistical Data Science