Statistics MSc 12 months Postgraduate Programme By The University of Sheffield |TopUniversities
Subject Ranking

# 101-150QS Subject Rankings

Programme Duration

12 monthsProgramme duration

Tuitionfee

27,670 GBPTuition Fee/year

Main Subject Area

Statistics and Operational ResearchMain Subject Area

Programme overview

Main Subject

Statistics and Operational Research

Degree

MSc

Study Level

Masters

Study Mode

On Campus

Our Statistics MSc 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. 

Our Statistics MSc includes modules on how to collect data and design experiments, and the role of statistics in clinical trials.  You’ll learn how to analyse and draw meaningful conclusions from data, and develop your programming skills using the statistical computing software R.

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. You’ll gain skills to help you stand out in the graduate job market, such as planning and researching a project, data acquisition, problem specification, analysis and reporting your findings.

External clients, such as pharmaceutical companies or sports modelling organisations, often provide dissertation topics. Distance learning students often come with projects designed by their employer. 

Recent examples of dissertation topics include:
  • Probabilistic Topic Modelling
  • Spatio-temporal Modelling of Social Phenomena
  • Feature selection for high dimensional data
  • Modelling Football Results


Accreditation This course is accredited by the Royal Statistical Society

Programme overview

Main Subject

Statistics and Operational Research

Degree

MSc

Study Level

Masters

Study Mode

On Campus

Our Statistics MSc 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. 

Our Statistics MSc includes modules on how to collect data and design experiments, and the role of statistics in clinical trials.  You’ll learn how to analyse and draw meaningful conclusions from data, and develop your programming skills using the statistical computing software R.

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. You’ll gain skills to help you stand out in the graduate job market, such as planning and researching a project, data acquisition, problem specification, analysis and reporting your findings.

External clients, such as pharmaceutical companies or sports modelling organisations, often provide dissertation topics. Distance learning students often come with projects designed by their employer. 

Recent examples of dissertation topics include:
  • Probabilistic Topic Modelling
  • Spatio-temporal Modelling of Social Phenomena
  • Feature selection for high dimensional data
  • Modelling Football Results


Accreditation This course is accredited by the Royal Statistical Society

Admission Requirements

6.5+
176+
88+
61+
Minimum 2:1 undergraduate honours degree in a relevant subject with relevant modules.

1 Year
Sep

Domestic
12,310 GBP
International
27,670 GBP

Scholarships

Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.

To help students navigate this challenging process, we recommend the following articles:

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