Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. New and exciting opportunities in industry, medicine, government commerce or research await the graduate who has gained the quantitative skills training provided by this MSc.
Mode of study
- Full-time 1 year
- Part-time 2 years
- UK/EU Full-time: £8,500
- UK/EU Part-time: £4,250
- Overseas Full-time: £16,750
- Overseas Part-time: £8,500
- All applicants: 15 March 2014
More details in Application section.
What will I learn?
The programme uses a broad-based approach to statistics, providing up-to-date training in the major applications and an excellent balance between theory and application. It covers modern ideas in statistics including applied Bayesian Methods, generalised linear modelling and object-oriented statistical computing, together with a grounding in traditional statistical theory and methods.
Why should I study this degree at UCL?
One of the strengths of Statistical Science at UCL is the breadth of expertise on offer; the research interests of staff span the full range from foundations to applications, and make important original contributions to the development of statistical science.
London provides an excellent environment in which to study statistical science, being the home of the Royal Statistical Society as well as a base for a large community of statisticians, both academic and non-academic.
The Statistics MSc has been accredited by the Royal Statistical Society. Graduates will automatically be granted the Society's Graduate Statistician status on application to the Society.
Students undertake modules to the value of 180 credits. The programme consists of a foundation module, four core modules (60 credits) four optional modules (60 credits) and a research dissertation (60 credits).
All MSc students undertake an independent research project, culminating in a dissertation of 10,000–12,000 words. Workshops provide preparation for this project, and run during the teaching terms, and cover the communication of statistics e.g. the presentation of statistical graphs and tables.
Teaching and Learning
The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. External organisations deliver technical lectures and seminars where possible. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.
Further details available on subject website:
Some studentship funding may be available from the Department of Statistical Science. Students wishing to apply for this should indicate their interest on the application form.
Further information about funding and scholarships can be found on the Scholarships and funding website.
A minimum of an upper second-class Bachelor's degree in a quantitative discipline from a UK university or an overseas qualification of an equivalent standard. Knowledge of mathematical methods and linear algebra at university level and familiarity with introductory probability and statistics is required. Relevant professional experience will also be taken into consideration.
Select your country for equivalent alternative requirements
English language proficiency level: Standard
How to apply
Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.
The deadline for applications is 15 March 2014.
Who can apply?
The programme is accessible to students with first degrees in a quantitative discipline, (such as mathematics, statistics, physics, chemistry, biology, computer science, engineering or economics) who wish to gain advanced training in statistical theory and applications to enable them to enter specialist employment or academic research.
What are we looking for?
When we assess your application we would like to learn:
- why you want to study Statistics at graduate level
- why you want to study Statistics at UCL
- what particularly attracts you to this programme
- how your academic background meets the demands of this programme
- where you would like to go professionally with your degree
Graduates typically enter professional employment across a broad range of industry sectors or have pursue further academic study.
Top career destinations for this programme
- Mann Investments, Qunatitative Research Associate, 2009
- Proctor & Gamble, Statistician, 2011
- MVA Consultancy, Principal Consultant, 2010
- JP Morgan, Credit Analyst, 2011
- UCL, PhD Statistical Science, 2011
The Statistics MSc provides skills that are currently highly sought after. Graduate receive advanced training in methods and computational toold for data analysis that companies and research organisations value. For instance, the new directives and laws for risk assesments in the banking and insurance industries, as well as the healthcare sector, require statistical experts trained at the graduate level. The large amount of data processing in various industries (known as "data deluge") also neccessitates cutting-edge knowledge in statistics. As a result, our recent graduates have been offered positions as Rsearch Analysts or Consultants, and job opportunities in these areas are ioncreasing.
For queries relating to this programme, please contact:
Dr Russell Evans
T: +44 (0)20 7679 8311
Apply for this programme through UCL's application portal:
Register your interest
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"The department offers a huge variety of seminars where I learn much more than just about my specific research topic. We are always motivated to participate at workshops and conferences. In under one year I have already been to three conferences, including one in Germany."
"UCL has an amazing portfolio of research. As a statistician I get to play with data from many different fields, which is exciting. Collaborating with other scientists at UCL enables me to contribute to a wide array of scientific disciplines."
Professor Sofia Olhede
Professor of Statistics
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