This course will teach students the theories behind a variety of statistical techniques, and how to apply them in scenarios that professional statisticians face every day. Students will develop a detailed working knowledge of important statistical techniques and concepts, including linear and generalised linear modelling, Bayesian statistics, time series and machine learning. They will learn how to analyse and draw meaningful conclusions from data, and develop their 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 the dissertation. This may focus on investigating a data set, or a more theoretical or methodological topic. The aim is to give students skills to include on their CV, such as planning and researching a project, data acquisition, problem specification, analysis and reporting their 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.
| Level | Masters |
| Discipline | Mathematics |
| Duration | 12 months |
| Intakes | Jan, Sep |
| Application Fees | GBP 0 |
| Tuition Fees | GBP 23250 |
| Campus | Main |
| Language proficiency (minimum) | |
| IELTS | 6.5 |
|---|---|
| TOEFL | 88 |
| PTE | 61 |
| Duolingo | Not Required / Waiver |
| Exam proficiency (minimum) | |
| SAT | Not Required / Waiver |
|---|---|
| ACT | Not Required / Waiver |
| GRE | Not Required / Waiver |
| GMAT | Not Required / Waiver |
Minimum GPA - 73%
105
105
151-200
129
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