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MSc in Statistics

A broad and flexible postgraduate course in statistical science

The programme aims to cover topics most relevant to a career as a professional statistician. This training opens the way to employment in many sectors of the economy and public services including medical, health and life sciences, marketing, insurance, banking and pharmaceutical industry, quality management and analytics for business and manufacturing, national and local government.

Prior knowledge of basic statistical theory and methods is assumed, such as would be covered in a typical first degree in mathematics or a joint degree between statistics and some other discipline. Students taking this course will already have a degree in mathematics or in statistics or in a subject containing a substantial mathematics component (e.g. in sciences or social sciences with a strong quantitative emphasis).

Course content

For the nine month period from October to June, all the students will be engaged in attending a set of courses ranging across the spectrum of the most fundamental areas of Statistics and Probability.

After completing the taught portion of the Master's the student will have acquired sufficient knowledge and understanding of topics in statistical theory and practice and in probability to provide a basis for academic research or a career as a statistician. Master's students will then continue for a further three months to put their knowledge into practice in the dissertation.

Some recent MSc dissertations

The MSc course includes undertaking a major project, resulting in a substantial dissertation. Students usually find this a particularly rewarding aspect of the course. The MSc projects vary widely in nature, from theoretical to applied. Some recent project titles include:

  • Bayesian analysis and model comparison in mathematical epidemiology: Influenza A (H1N1)pdm09 in households
  • The Cramer-Lundberg model
  • Adequacy of Bayesian massive multiple testing control
  • Cluster and feature models in statistical machine learning.
  • Distributional properties and inferential power
  • Visualisation and inference for distance data with applications to epigenetics
  • Large deviations and its applications in queuing theory