Statistical Science

The MSc in Statistical Science is a twelve-month full-time taught master’s degree running from October to September each academic year. The MSc has a particular focus on modern computationally-intensive theory and methods.

The MSc in Statistical Science will aim to train you to solve real-world statistical problems. When completing the course you should be able to choose an appropriate statistical method to solve a given problem of data analysis, implement the analysis on a computer and communicate your results clearly and succinctly.

Graduate destinations

Graduates of the MSc find employment in financial, economic, governmental, scientific and industrial areas. The MSc can also be a useful stepping stone for doctoral studies, with around a third of students on the course taking this route.

The MSc offers a broad high-level training in applied and computational statistics, statistical machine learning, and the fundamental principles of statistical inference. Training is delivered through mathematically demanding lectures and problems classes, hands-on practical sessions in the computer laboratory, report writing and dissertation supervision. You will have around three months to work on your dissertation with guidance from your supervisor.

You will be assessed on your performance in written examinations around May, through your work in the assessed practical problems set during the year, and by the quality and depth of your dissertation.

The Department of Statistics has made some changes to the content and delivery of the course and the revised MSc programme is running for the first time in 2016-17. There is now more emphasis on computational statistics and statistical machine learning, more opportunity for students to take courses from the MMath Mathematics and Statistics degree, and enhanced class support. The assessment structure remains the same as in previous years. From 2017-18 the course is known as the MSc in Statistical Science (previously the MSc in Applied Statistics) to better reflect its content.

Students take four, or exceptionally five, courses each term. Three courses each term are core courses and students must complete the practical sessions in these courses.

The options available will vary from year to year. The core courses available each year may also vary. In 2016-17 the core courses are:

• Applied Statistics
• Statistical Inference
• Statistical Programming
• Computational Statistics
• Data Mining and Machine Learning
• Bayes Methods.

In 2016-17 the options are:

• Stochastic Models in Mathematical Genetics
• Probability and Statistics for Network Analysis
• Graphical Models
• Statistical Machine Learning
• Advanced Simulation Methods
• Actuarial Science.

Applicants are normally expected to be predicted or have achieved a first-class or strong upper second-class undergraduate degree with honours (or equivalent international qualifications), as a minimum, in a degree course with substantial advanced mathematical and statistical content; some very quantitative courses in science, social science (notably economics) or medicine may be appropriate if they meet this criterion.

However, entrance to the course is very competitive and most successful applicants have a first-class degree or the equivalent.

For applicants with a degree from the USA, the minimum GPA sought is 3.6 out of 4.0.

If you hold non-UK qualifications and wish to check how your qualifications match these requirements, you can contact the National Recognition Information Centre for the United Kingdom (UK NARIC).

No Graduate Record Examination (GRE) or GMAT scores are sought.

• Official transcript(s)
• CV/résumé
• Personal statement:Up to two pages
• Written work:Either one essay of 4,000 words or two essays of 2,000 words each
• References/letters of recommendation:Three overall, generally academic

ENGLISH LANGUAGE REQUIREMENTS

Higher level

• Global Education
• Hill Foundation Scholarships