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Full time | University of Sheffield | 1 year | SEP-25

Study mode

Full time

Duration

1 year

Start date

SEP-25

Key information
DATA SOURCE : IDP Connect

Qualification type

MSc - Master of Science

Subject areas

Statistics

Course type

Taught

Course Summary

Course description

This course will teach you the theories behind a variety of statistical techniques, and how to apply them in scenarios that professional statisticians face every day.

You’ll develop a detailed working knowledge of important statistical techniques and concepts, including linear and generalised linear modelling, Bayesian statistics, time series and machine learning. You’ll learn how to analyse and draw meaningful conclusions from data, and develop your programming skills using the statistical computing software R.

This course also includes modules on how to collect data and design experiments, and the role of statistics in clinical trials.

Around one-third of the course is devoted to your dissertation. This may focus on investigating a data set, or a more theoretical or methodological topic. The aim is to give you skills to include on your CV, such as planning and researching a project, data acquisition, problem specification, analysis and reporting your findings.

Dissertation topics are often provided by external clients – for example, pharmaceutical companies or sports modelling organisations. Distance learning students often come with projects designed by their employer.

Modules

This module introduces the Bayesian approach to statistical inference. The Bayesian method is fundamentally different in philosophy from conventional frequentist/classical inference, and has been the subject of some controversy in the past, but is now widely used. The module also presents various computational methods for implementing both Bayesian and frequentist inference, in situations where obtaining results analytically would be impossible. The methods will be implemented using the programming languages R and Stan, and programming is taught alongside the theory lectures.

Tuition fees

UK fees
Course fees for UK students

For this course (per year)

£12,070

International fees
Course fees for EU and international students

For this course (per year)

£26,350

Entry requirements

Minimum 2:1 undergraduate honours degree, with substantial mathematical and statistical components. In particular, you should have studied the following topics and performed well in assessments on them (for example, a score of at least 60 per cent).