Mathematics with Data Science
(MMath) Mathematics with Data Science
Entry for 2025
FHEQ level
This course is set at Level 7 (Masters) in the national Framework for Higher Education Qualifications.
Course Aims
The Mathematics with Data Science (MMath) degree programme aims to provide:
1. teaching in the mathematical and data sciences that is advanced and broad-based and, where appropriate, informed by a research base of international standard;
2. a programme structure which allows transfer between certain programmes at appropriate stages, and a guided choice of courses to meet students' developing interests;
3. a coherent set of courses grouped for intellectual and vocational reasons, based on a mathematics, statistics and data science core building progressively on advanced skills and knowledge acquired during the programme;
4. a sound preparation for further training and research and for a career requiring advanced mathematical, statistical or data science knowledge and understanding;
5. an admissions policy which gives access to students with special needs and to mature and other prospective students who may have unconventional academic backgrounds;
6. provision for students to develop personal, transferable and intellectual skills, enabling them to compete successfully on the employment market.
Course learning outcomes
Cognitive Skills: demonstrate in depth knowledge and understanding of a core of analysis, algebra, applied mathematics and statistics, much of which is at (or is informed by) the forefront of the discipline; demonstrate knowledge and understanding of some choice of advanced topics.
Cognitive Skills: demonstrate knowledge of advanced topics in data science and machine learning.
Cognitive Skills: demonstrate enhanced ability to understand and use mathematical argument and deductive reasoning as well as awareness of the importance of mathematical and statistical assumptions.
Practical Skills: demonstrate mastery in the use of mathematical methods and techniques in problem solving and modelling, explore, and where feasible solve, mathematical problems, by selecting appropriate techniques and demonstrate in depth knowledge and understanding of the process of mathematical or statistical modelling.
Practical Skills: demonstrate knowledge and understanding of some processes of mathematical approximation and of sources of numerical errors, exhibit advanced skills of numeracy, involving use of quantitative concepts and arguments, where appropriate, at all stages of work.
Professional Competencies: use one or more mathematical and statistical computer packages and be highly proficient in computer programming.
Transferable Skills: take decisions in complex and unpredictable contexts; apply a selection of mathematical, computational, numerical and statistical skills to evaluate different responses.
Transferable Skills: communicate scientific information orally and in writing.
Transferable Skills: take responsibility for their own learning, and manage time appropriately.
Cognitive Skills: understand and critically evaluate current research and, where appropriate, suggest new ideas.
Full-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.