Strand Campus
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Overview
The Statistics MSc enables graduates and professionals with a strong mathematical background to research, develop and apply modern statistical modelling techniques to a range of practical settings. The programme will prepare students to critically think about real world data analysis problems arising in the public or private sector, for example in the financial, pharmaceutical or manufacturing sectors.
Key benefits
- A rigorous approach to statistical modelling and data analysis, including both the theoretical underpinnings and state-of-the-art computational tools.
- Acquiring the ability to design and evaluate data collection procedures and apply the most appropriate statistical methods to answer practical questions in a data-driven way.
- Exposure to data analysis applications arising in a variety of practical contexts.
- Lecturers on the course have extensive experience in a wide range of statistical problems and applications, for example in the design of experiments in industry, financial modelling, geostatistics and biostatistics.
- Flexible study course offering the opportunity to study part-time.
- The Department of Mathematics is a leading centre of research, with 100 percent of research classed as world-leading or internationally excellent (REF 2021).
Course essentials
The Statistics MSc is aimed to develop a deep knowledge and understanding of statistics and its mathematical foundations, and to enable students to think logically and objectively about data analysis and statistical learning. Students will have the opportunity to learn how to apply the most appropriate statistical models to answer practical questions, using modern statistical methods and software. The students will therefore learn a wide range of transferrable skills, both in data analysis and in how to present and communicate the results. This will particularly apply to the individual research project.
The individual research project will offer students the opportunity to learn to think independently about complex data and models. Students will combine the programming skills and statistical knowledge from the taught modules in order to fit statistical models to real world data. The datasets which will be modelled and analysed are expected to arise from many different sources. Examples may include medical data, financial time series, geographic data and data from social networks. Students will be supported in writing a scientific report of their finding and in presenting the report in a professional context.
Students must take modules totalling 180 credits to complete the course, and you will complete the course in two years.
Optional Modules and Clusters
Students can tailor their learning experience by selecting from various optional modules, creating coherent clusters of modules within the program. Please note, the cluster combinations are optional – students can still choose to combine modules in other manners and the choice of clusters does not impact on the degree awarded.
Examples include:
Data Science Cluster
This cluster emphasizes the methodological and practical aspects of statistical data science. Students will learn modern statistical and machine learning techniques in modules such as Elements of Statistical Learning and Machine Learning. They will apply these techniques to complex, high-dimensional datasets in their individual research projects.
Statistical Modelling Cluster
This cluster focuses on modelling and inference for the data-generating processes. Students will explore how to model complex problems and assess uncertainty in estimation and prediction through modules like Design of Experiments and Time Series Analysis. Their individual research projects will investigate the statistical properties of advanced inferential procedures
Key Information
Course type:
Master's
Delivery mode:
In person
Study mode:
Full time / Part time
Duration:
1 year full-time, 2 years part-time
Application status:
Open
Start date:
September 2026
Administrative bodies
Regulating body
Application closing date guidance
Base campus