Structure of the MSc in Statistics
Course structure
Our curriculum covers the most relevant and in-demand topics for statisticians
Our program provides unparalleled flexibility, allowing students to tailor their learning to their ambitions through four distinctive options:
- MSc in Statistics
- MSc in Statistics with Data Science
- MSc in Statistics with Finance
- MSc in Statistics with Probability
You begin the MSc by taking a Statistics Refresher (pre-term in Welcome Week), and the Term 1 core module Statistical Methods and Practice. The programme then features different core modules depending on the chosen degree specialisation:
- for Data Science, the module Theory of Data Science
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for Probability and Finance, the module Introduction to Advanced Probability
In Term 2 you take the core module Advanced Topics in Statistics & Probability, which provides an introduction to current topics through case studies in Statistics, Data Science, Finance and Probability, exploring potential research topics for your dissertation. Each two-week case study is led by an expert lecturer in the fields and will make use of lectures, presentation, practicals, summary and discussion as appropriate to the area.
Regardless of the chosen degree specialisation, all students can choose approximately a third of their modules from any of those offered on the MSc, with the remainder dependent on the chosen degree.
The programme concludes with the Dissertation Project, which you work on under the supervision of a faculty member over the summer with submission in September.
Statistics
MSc in Statistics – General Route
Structure: The programme is built on a flexible, customisable structure. It establishes a broad statistical foundation through a compulsory 30 CATS core, followed by a personalised curriculum where students select 90 CATS (6 modules) from an extensive list of optional modules.
- Core Foundation (30 CATS): A dedicated statistics core covering essential theory, methods, and applications.
- Personalised Specialisation (90 CATS): Students construct their degree by choosing from a wide array of optional topics. The selection includes, but is not limited to, Bayesian statistics, medical statistics, statistical genetics, multivariate analysis, time series, stochastic processes, designed experiments, financial mathematics, statistical consulting, Monte Carlo methods, and advanced data science topics.
This structure is designed to produce versatile statisticians with comprehensive training in classical and modern methods, ensuring their expertise aligns directly with individual career goals and intellectual interests.
(Please note that optional modules may vary from year to year.).
Finance
MSc in Statistics with Finance
Structure: This route employs a focused, three-part structure designed to build a robust quantitative profile for finance. It begins with a shared statistical core, intensifies with a compulsory advanced probability module, and then branches into mathematical finance modules complemented by general statistical electives.
- Shared Statistical Core: Foundational modules providing a grounding in statistics.
- Theoretical Rigour Core: The compulsory module ST964 Introduction to Advanced Probability, offering a rigorous, measure-theoretic foundation.
- Finance Focus: A suite of modules dedicated to mathematical and statistical modelling in finance, covering areas like Stochastic Methods in Finance, Applications of Stochastic Calculus, Time Series Analysis, and Advanced Trading Strategies.
- Statistical Elective Pool: A selection of general statistical modules (e.g., Bayesian Statistics, Statistical Learning) to complete and broaden the quantitative skillset.
(Please note that optional modules may vary from year to year.)
Data Science
MSc in Statistics with Data Science
Structure: This specialised route follows a tiered structure that combines statistical rigour with computational data science. It progresses from a shared statistical core to a bespoke theoretical data science module, then to specialised statistical and complementary computational electives.
- Shared Statistical Core: Foundational modules in statistical theory, methods, and applications.
- Discipline-Specific Core: A compulsory, mathematically rigorous module on the Theory of Data Science.
- Advanced Specialisation: A choice of specialised statistics modules, such as Statistical Learning and Big Data, Bayesian Forecasting, and Monte Carlo Methods.
- Computational Enhancement: The option to select technical electives from Computer Science (e.g., High-Performance Computing, Natural Language Processing, Image & Video Analysis) to build practical computational expertise.
Outcome: This integrated structure ensures graduates master both the theoretical foundations of data science and the practical computational skills required to solve complex, real-world data challenges.
(Please note that optional modules may vary from year to year.)
Probability
MSc in Statistics with Probability
Structure: This route features a structure for specialisation in probability. It extends from a shared statistical core to a central, advanced probability theory module, then to applied stochastic modules and a broad selection of advanced pure/applied probability or statistics electives.
- Shared Statistical Core: Foundational modules in statistical theory, methods, and applications.
- Central Theoretical Pillar: The compulsory core module ST964 Introduction to Advanced Probability (measure-theoretic).
- Stochastic Modelling Applications: A selection of modules applying this theory to areas like Brownian Motion, Applied Stochastic Processes, and Dynamic Stochastic Control.
- Broadening Electives: Optional modules chosen from two streams: to deepen pure/applied probability knowledge via the Mathematics Institute (e.g., Stochastic Analysis, Random Graphs, Statistical Mechanics) or to strengthen complementary statistical knowledge (e.g., Bayesian Forecasting, Multivariate Statistics).
(Please note that optional modules may vary from year to year.)