MSci Data Science (with a year in industry)
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 (with a year in industry), a computer science degree focusing on computational, statistical and mathematical methods for information analysis. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, Artificial Intelligence & Machine Learning. The programme incorporates a year-long professional industry placement. The first year covers fundamentals through Software 1: Foundations of Programming for Computer Science, Introduction to Probability and Statistics, Foundations and Calculus, and DATA: Introduction to Data Science. The second year progresses with Systems and Devices 2: Operating Systems, Security, and Networking, Engineering 1: Software and Systems Engineering, Probability and Markov Chains, and Intelligent Systems: Machine Learning and Optimisation. Fourth-year studies encompass Computer Science project, MMath group project, Governance of Data Science, and Cloud Based Data Analysis. The final fifth year involves advanced research through Advanced Project: Computer Science, Extended Independent Project: Mathematics, High-Integrity Systems Engineering, and Computational Finance with Python.
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
- MMath group 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
- Advanced Project: 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