BSc Data Science (with a year in industry)
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 (with a year in industry), a computer science degree focusing on computational, mathematical and statistical methods for managing information. Core topics include Programming, Data Analysis, Statistics, Mathematical Modelling, and Artificial Intelligence & Machine Learning. Students complete a compulsory year in industry between the second and final years. The first year covers core fundamentals through modules such as Software 1: Foundations of Programming for Computer Science, Introduction to Probability and Statistics, and Foundations and Calculus. The second year progresses to topics like Systems and Devices 2: Operating Systems, Security, and Networking, Intelligent Systems: Machine Learning and Optimisation, and Statistical Inference and Linear Models. The final year involves advanced study and projects through options such as Computer Science project, Cloud-Based Data Analysis, Deep Learning, and Large Language Models.
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 in 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