Mathematics
| Award | Attendance | Study | Duration | Start | Domestic fees | International fees |
|---|---|---|---|---|---|---|
| MSc | On-Campus | Full-time | 1 | September | find out | find out |
Please see our website for the most up-to-date fees information.
About Mathematics, MSc - at University of Sheffield
Build the foundations for a successful career by exploring the topics across pure mathematics, applied mathematics and statistics that align to your interests and goals.
Our MSc Mathematics course gives you the freedom to choose from a variety of advanced modules across pure mathematics, applied mathematics and statistics. You’ll have the opportunity to explore topics spanning algebra, topology, financial mathematics, machine learning, mathematical biology, medical statistics and time series.
Strong mathematical skills open all kinds of doors, and through our MSc Mathematics course you’ll develop the knowledge and experience needed for roles spanning finance and consultancy, data science, healthcare, computing, public administration, research and education.
Whether you want to advance your understanding of the topics you found most interesting during your undergraduate studies or gain the skills needed to achieve your goals, our flexible one-year course is designed to help you build the foundations for a successful career.
One of the biggest parts of your degree is your dissertation. You’ll investigate a real-world or pure maths problem of your choice, alongside an active researcher who is an expert in your chosen area. You’ll blend theoretical knowledge with practical skills, developing valuable project planning, data acquisition and analysis, problem-solving and software skills. You’ll also learn how to present mathematical, statistical and technical information, and gain experience communicating your findings verbally and in writing.
Examples of recent dissertation topics across mathematics and statistics include:
- Goedel’s Incompleteness Theorem
- Geometry of groups, Amenability and the Banach-Tarski paradox
- Synchronised Chaos
- Analysis of stochastic differential equations
- Spatio-temporal Modelling of Social Phenomena
- Feature selection for high dimensional data
- Modelling Sports Results
- Neural Networks with Python
- Financial modelling with Lévy processes
Entry requirements for this course
Contact University of Sheffield to find course entry requirements.
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