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.
This course is accredited by the Royal Statistical Society