BSc Data Science
Entry requirements
A level: AAA including Mathematics. If you are studying towards a fourth A level, we will make an alternative offer of AABB including grade A in Mathematics. IB: 36 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
BSc (Hons) Data Science, a computer science degree focusing on computational and analytical methods for complex datasets. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. Students can optionally add a placement year to gain workplace experience before graduation. The first year covers fundamental principles through Software 1: Foundations of Programming for Computer Science, Foundations and Calculus, and DATA: Introduction to Data Science. The second year introduces more advanced concepts with modules such as Systems and Devices 2: Operating Systems, Security, and Networking, Intelligent Systems: Machine Learning and Optimisation, and Statistical Inference and Linear Models. The final year allows students to study specialised areas including Cloud Based Data Analysis, Deep Learning, Large Language Models, and Natural Language Processing, alongside completing a Computer Science project and a Mathematics project.
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
- Computer Science project
- Mathematics project
- 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