This course is, by design, multidisciplinary and follows a centred approach. In first year, the foundations are laid for mathematics, programming and communication, which are then further developed in second and third year.
The course delivery brings together scientific method, teamwork, communication, collaboration and creative problem solving. These skills will be blended in data science applications such as modelling, simulations, creating mock data sets and optimisation questions. You will use basic illustrative examples and motivational real-world case studies to understand how this foundational knowledge underpins advanced data science tools and techniques – and how they could be applied to help solve grand challenges too.
You will also have the chance to tailor your course with a selection of optional modules – from statistics, applied and pure mathematics to a range of exciting discovery modules – and get involved in your own 40-credit research project in third year.
The programme will aim to equip you to
• Be able to explain and apply data science core concepts, tools and techniques from mathematical modelling, machine learning, programming, software engineering and communication.
• Be analytical and able to critically evaluate different data science-related approaches, arguments and analyses, and to perform rigorous, robust and reproducible mathematical and computational analyses yourself.
• Be able to conduct independent research or projects - self-managing and appropriately drawing on different sources for information and support - synthesising and integrating the findings critically.
• Be able to appropriately apply fundamental principles of communication in context and to communicate advanced data science concepts, conclusions and recommendations to specialist and non-specialist audiences.
• Be able to express your ideas computationally and to apply principles of programming and software engineering to create, implement or use software appropriately in the analysis and visualisation of big data.
• Apply, blend and contribute your interdisciplinary skills and insights in multiprofessional and multidisciplinary teams, working collaboratively, creatively and professionally in diverse teams, on complex problems from industry to global challenges (e.g. sustainability).
• Be able to articulate how mathematics and data science relate to society and common practices, discuss ethical questions around data science, and act with professional integrity in accordance with ethical professional codes of practice as well as your own values.
• Be a strategic and reflective thinker, being able to identify, articulate and evidence past development and achievements, and plan future strategic goals, such as personal development, with reference to external frameworks.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH1000 | Core Mathematics | 40 | Semesters 1 & 2 (Sep to Jun) | PFP |
| MATH1013 | Computational Mathematics and Modelling | 20 | Semesters 1 & 2 (Sep to Jun) | |
| MATH1603 | Data Science & Communication | 20 | Semester 1 (Sep to Jan) | PFP |
| MATH1604 | Modelling for Big Data | 20 | Semester 2 (Jan to Jun) | |
| MATH1700 | Probability and Statistics | 20 | Semesters 1 & 2 (Sep to Jun) |
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH2130 | Further Linear Algebra and Discrete Mathematics | 20 | Semester 1 (Sep to Jan) | |
| MATH2603 | Graphs, Networks and Systems | 20 | Semester 2 (Jan to Jun) | |
| MATH2604 | Machine Learning and Object-Oriented Programming | 20 | Semesters 1 & 2 (Sep to Jun) | |
| MATH2701 | Statistical Methods | 20 | Semester 2 (Jan to Jun) | |
| MATH2702 | Stochastic Processes | 10 | Semester 1 (Sep to Jan) | |
| MATH2703 | Time Series | 10 | Semester 2 (Jan to Jun) |
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH2003 | Investigations in Mathematics | 10 | Semester 1 (Sep to Jan) | |
| MATH2350 | Vector Calculus and Partial Differential Equations | 20 | Semester 1 (Sep to Jan) |
Depending on how many optional module credits are selected, candidates will then be required to study between 0 and 20 credits of Discovery modules in Semester 1.
Candidates must study a total of 120 credits in their second year. These should be distributed evenly across the semesters.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH3602 | Project in Data Science | 40 | Semesters 1 & 2 (Sep to Jun) | |
| MATH3603 | Deep Learning and Explainable AI | 20 | Semester 1 (Sep to Jan) | |
| MATH3604 | Data Curation and Governance | 20 | Semester 2 (Jan to Jun) |
Candidates are required to select one of the following optional Semester 1 modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH3125 | Groups and Symmetry | 20 | Semester 1 (Sep to Jan) | |
| MATH3300 | Methods of Applied Mathematics | 20 | Semester 1 (Sep to Jan) | |
| MATH3701 | Statistical Modelling | 20 | Semester 1 (Sep to Jan) |
Candidates are required to select one of the following optional Semester 2 modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| MATH3115 | Graph Theory and Combinatorics | 20 | Semester 2 (Jan to Jun) | |
| MATH3150 | Numbers and Codes | 20 | Semester 2 (Jan to Jun) | |
| MATH3425 | Mathematical Biology | 20 | Semester 2 (Jan to Jun) | |
| MATH3430 | Entropy and Quantum Mechanics | 20 | Semester 2 (Jan to Jun) | |
| MATH3702 | Multivariate Analysis and Classification | 20 | Semester 2 (Jan to Jun) |
Alternatively, candidates can replace one of the above 20 credit optional modules with a maximum of 20 credits of Discovery modules in that same semester.
Candidates must study a total of 120 credits in their third year. These should be distributed evenly across the semesters.
Last updated: 12/05/2026 16:42:23
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