MSci Data Science
About this course
MSci in Data Science, a computer science degree centred on mathematical and computational approaches to data analysis and machine learning. Core topics include Programming, Computer Architecture, Data Analysis, Statistics, Mathematical Modelling, Project Management, Critical & Analytical Thinking, Artificial Intelligence & Machine Learning. Research projects feature in each academic year, and students can undertake an optional commercial or industrial work experience placement. The first year covers foundational concepts through compulsory modules such as Programming, Object-Oriented Programming, Data Structures and Algorithms, and Discrete Mathematics for Computer Science. The second year progresses to specialized topics through Machine Learning and Data Science, Team Project, Database Theory and Design, and Statistical Modelling and Inference, alongside optional choices like Computational Intelligence and Artificial Intelligence and Applications. The third year involves advanced study via Data Science at Scale, Probabilistic Machine Learning, and Individual Literature Review and Project, while offering optional modules such as Computer Vision, Social Networks and Text Analysis, and High-Performance Computing. The final fourth year allows students to pursue studies to Masters level with advanced module choices tailored to individual interests.
Modules
- Fundamentals of Machine Learning
- Programming
- Social and Professional Issues of the Information Age
- Object-Oriented Programming
- Computers and the Internet
- Data Structures and Algorithms
- Discrete Mathematics for Computer Science
- Computational Mathematics
- Machine Learning and Data Science
- Team Project
- Software Development
- Database Theory and Design
- Statistical Modelling and Inference
- Data Science in Society
- Computational Intelligence
- Artificial Intelligence and Applications
- Outside the box: Computer Science Research and Applications
- Data Science at Scale
- Probabilistic Machine Learning
- Individual Literature Review and Project
- Computer Vision
- Social Networks and Text Analysis
- Enterprise Computing
- Nature-Inspired Computation
- Computability and Complexity
- Algorithms that Changed the World
- High-Performance Computing
- Commercial and Industrial Experience
- Mathematics: History and Culture
- Stochastic Processes
- Statistical Inference
- Bayesian Statistics, Philosophy and Practice