Data Science and Artificial Intelligence (Level 7 apprenticeship)
(MSc) Data Science and Artificial Intelligence (Level 7 apprenticeship)
Entry for 2025
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course learning outcomes
Apply a comprehensive knowledge of mathematics, statistics, data and artificial intelligence principles to the solution of complex problems.
Formulate and analyse complex problems to reach substantiated conclusions, discussing the limitations of the techniques employed.
Select and apply appropriate computational and analytical techniques to model complex problems, discussing the limitations of the techniques employed.
Select and critically evaluate technical literature and other sources of information to solve complex problems.
Design solutions for complex problems that evidence some originality and meet a combination of societal, user, business and customer needs as appropriate.
Evaluate the environmental and societal impact of solutions to complex problems, with a view to minimizing adverse impacts.
Function effectively as an individual, and as a member or leader of a team. Evaluate the effectiveness of one’s own performance as an individual, or the performance of a team, in the context of Data Science and Artificial Intelligence.
Communicate effectively on complex engineering matters with technical and non-technical audiences, evaluating the effectiveness of the methods used.
Full-time course composition
| Year | Term | Status | Module | Credits | FHEQ level |
|---|---|---|---|---|---|
| 1 | Autumn Semester | Core | Programming through Python (990G5) | 15 | 7 |
| Core | Statistical Analysis and Probability (993G5) | 15 | 7 | ||
| Spring Semester | Core | Foundational Computer Science (for Data Science) (988G5) | 15 | 7 | |
| Core | Mathematics for Data Analysis (989G5) | 15 | 7 | ||
| Summer Teaching | Core | Databases (991G5) | 15 | 7 | |
| Core | Wider Topics in Data Science (992G5) | 15 | 7 | ||
| Year | Term | Status | Module | Credits | FHEQ level |
| 2 | Autumn Semester | Core | Applied Natural Language Processing (995G5) | 15 | 7 |
| Core | Machine Learning (994G5) | 15 | 7 | ||
| Spring Semester | Core | Computer Vision (996G5) | 15 | 7 | |
| Spring & Summer Teaching | Core | Synoptic Project (997G5) | 45 | 7 |
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.