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MSc Applied Statistics In Health Sciences

  • DeadlineStudy Details: 12 months full-time

Masters Degree Description

Our MSc in Applied Statistics in Health Sciences is a conversion course, offering the opportunity to develop skills in statistics and data analysis even if you have never studied statistics before. You will be supported programme by members of staff who work directly with industry to develop skills which are relevant to current areas of research including population health and medicine, animal and plant health, finance and business.

Students will gain skills in:

problem solving
analysis and manipulation of complex data
the use of statistical software for data analysis and reporting
effective communication of statistics

Entry Requirements

Minimum second-class (2:2) Honours degree or overseas equivalent.

Mathematical training to A Level or equivalent standard.

Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply.

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Fees

For fees and funding options, please visit website to find out more

Student Destinations

We work closely with the University's Careers Service. They offer advice and guidance on career planning and looking for and applying for jobs. In addition they administer and publicise graduate and work experience opportunities.

There are many exciting career opportunities for graduates in applied statistics. The practical, real-life skills that you'll gain means you'll be much in demand in international organisations. A report by the Association of the British Pharmaceutical Industry identified statistics and data mining as “two key areas in which a 'skills gap' is threatening the UK's biopharmaceutical industry.”

Module Details

Core modules:

Foundations of Probability & Statistics
Data Analytics in R
Experimental Design
Multivariate Analysis
Medical Statistics

Optional modules:

Quantitative Risk Analysis
Bayesian Spatial Statistics
Survey Design & Analysis
Effective Statistical Consultancy
Financial Econometrics
Financial Stochastic Processes
Data dashboards with RShiny

Statistical Machine Learning

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