Statistics

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.

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Accreditation

This course is accredited by the Royal Statistical Society

Royal Statistical Society (RSS)

Modules

Explore core modules

Teaching

There are lectures, tutorials, computing sessions and group work. Most statistics lectures are recorded so you can watch them again later.

Distance learning option

This is taught online with support via email and an online forum. Distance learners also come to the University for residential weeks.

You'll need to be in Sheffield for a few days between late May and early June each year for your exams. You're expected to spend around 20 hours each week on your studies if you're doing the two-year version of the course, and around 12 to 15 hours each week if you're doing the three-year version.

Assessment

Some modules may be continuously assessed through ongoing project work with no examination but most taught modules are assessed by a mixture of examinations and coursework. The assessment of the dissertation module is based entirely on your submitted dissertation.

Duration

• 1 year full-time
• 2-3 years part-time by distance learning

Your career

Students graduate with the specialist modelling and analysis skills employers need to interpret the complex datasets that underpin many 21st century professions – from business, manufacturing and marketing to policymaking, science and healthcare.

If you get a second-class degree or better, you automatically qualify for the Royal Statistical Society Graduate Statistician award – a stepping stone to full professional membership of the RSS and Chartered Statistician status.

Student profile

"This MSc course has equipped me with not only statistical skills but also professional skills, including teamwork and time management"

Yiyang Yan

Masters student, Statistics MSc

Entry requirements

We usually ask for a 2:1 honours degree, or equivalent, with substantial mathematical and statistical components or a pass at an equivalent level in the Graduate Certificate.

Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent. 

"I think there is a good learning atmosphere and all the teachers and staff are very friendly and responsible"

Xiaowen Gu

Masters student, Statistics MSc