Statistics | Graduate courses | University of Oxford
Department of Statistics
Oxford's Department of Statistics


The University's Department of Statistics is a world leader in research in probability, bioinformatics, mathematical genetics and statistical methodology, including computational statistics and machine learning. 

The department offers the DPhil in Statistics and a DPhil programme in statistical science, the latter delivered by the department's EPSRC and MRC Centre for Doctoral Training in Statistical Science (known as OxWaSP).

The department is currently in the process of bidding for an additional DPhil programme which would commence in October 2019: the EPSRC Centre for Doctoral Training in Modern Statistics and Statistical Machine Learning. This course would be jointly run with Imperial College London. A decision regarding funding is expected in December 2018.

It is also possible to study for an MSc by Research in Statistics. As a research student you can be actively involved in a lively academic community by means of seminars, lectures, journal clubs, and social events. Research students are offered training in modern probability, stochastic processes, statistical methodology, computational methods and transferable skills, in addition to specialised topics relevant to specific application areas. 

The department also offers a taught twelve-month MSc in Statistical Science, with a particular focus on modern computationally-intensive methods and their use in data analysis, which includes a dissertation component.  

Much of the research in the Department of Statistics is either explicitly interdisciplinary or draws motivation from application areas, ranging from genetics and finance to the social sciences. 

This is an exciting time for the department. In 2016 the department moved to a newly-renovated building in St Giles, providing excellent teaching facilities and creating a highly visible centre for statistics in Oxford. Oxford’s Mathematical Sciences submission came first in the UK on all criteria in the 2014 Research Excellence Framework (REF).


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