Data Science
(MSc) Data Science
Entry for 2025
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course learning outcomes
Acquisition of the following knowledge and understanding: (1) basic probability theory and statistics, (2) computer programming, (3) data infrastructure, (4) machine learning, (5) mathematical modelling, (6) software development also for High Performance Computing and Grid
To command intellectual skills in interpretation of phenomena into mathematical model
To command intellectual skills in development of numerical models for the analysis of large data sets
To command intellectual skills in analysis and interpretation of the current research literature in applied data science and in relevant disciplines
To develop a range of practical skills in programming project design and analysis
To develop a range of practical skills in real-life interpretation of numerical and graphical model results
To develop a range of practical skills in computer programming
To develop a range of practical skills in use and development of data analysis tools
To develop a range of transferable skills in effective oral presentation on a series of platforms
To develop a range of transferable skills scientific and technical writing skills
To develop a range of transferable skills in communication of scientific results on large data sets to experts and wider audiences
Full-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Algorithmic Data Science (969G5) | 15 | 7 |
| Core | Data Science Research Methods Autumn (L7) (970G1A) | 15 | 7 | ||
| Option | Advanced Methods in Molecular Research (806C7) | 30 | 7 | ||
| Algorithmic Approaches to Mathematics (817G5) | 15 | 7 | |||
| Applied Natural Language Processing (955G5) | 15 | 7 | |||
| Data Analysis Techniques (890F3) | 15 | 7 | |||
| Linear Statistical Models (L7) (971G1) | 15 | 7 | |||
| Programming through Python (823G5) | 15 | 7 | |||
| All Year Teaching | Core | Dissertation (Data Science) (844G1) | 45 | 7 | |
| Spring Semester | Core | Data Science Masters Research Proposal (806G1) | 15 | 7 | |
| Core | Machine Learning (934G5) | 15 | 7 | ||
| Core | Wider Topics in Data Science (L7) (981G1) | 15 | 7 | ||
| Option | Advanced Natural Language Processing (968G5) | 15 | 7 | ||
| Genomics and Bioinformatics (C7120) | 15 | 6 | |||
| Image Processing (521H3) | 15 | 7 | |||
| Machine Learning and Statistics for Health (L7) (974G1) | 15 | 7 | |||
| Monte Carlo Simulations (L7) (865G1) | 15 | 7 | |||
| Network Science (981G5) | 15 | 7 | |||
| Statistical Inference (L.7) (867G1) | 15 | 7 | |||
| Wearable Technologies (867H1) | 15 | 7 | |||
| Web Applications and Services (944G5) | 15 | 7 |
Part-time course composition
Please note that the University will use all reasonable endeavours to deliver courses and modules in accordance with the descriptions set out here. However, the University keeps its courses and modules under review with the aim of enhancing quality. Some changes may therefore be made to the form or content of courses or modules shown as part of the normal process of curriculum management.
The University reserves the right to make changes to the contents or methods of delivery of, or to discontinue, merge or combine modules, if such action is reasonably considered necessary by the University. If there are not sufficient student numbers to make a module viable, the University reserves the right to cancel such a module. If the University withdraws or discontinues a module, it will use its reasonable endeavours to provide a suitable alternative module.