Course summary
Build up a set of statistical tools and techniques that you can use to solve problems in a range of industries. You'll develop the skills and knowledge a professional statistician needs. 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. Accreditation This course is accredited by the Royal Statistical Society Please see our University website for the most up-to-date course information.
How to apply
International applicants
Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
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). Mathematical Methods for Statistics: ideas and techniques from linear algebra, including multiple integration, differentiation, matrix algebra, the theory of quadratic forms. Probability and Probability Distributions: the laws of probability and of conditional probability, the concepts of random variables and random vectors and their distributions, the methodology for calculating with them; laws of large numbers and central limit phenomena. Basic Statistics: hypothesis testing; point estimation and confidence intervals; likelihood methods; linear modelling; use of statistical software, for example, R. Overall IELTS score of 6.5 with a minimum of 6.0 in each component, or equivalent.
Fees and funding
Tuition fees
No fee information has been provided for this course
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
Provider information
University of Sheffield
Western Bank
Sheffield
S10 2TN