Statistical Science

The Postgraduate Diploma in Statistical Science is a nine-month taught course, running from October each academic year. It is similar to the MSc in Statistical Science but there is no dissertation. The course has a particular focus on modern computationally-intensive theory and methods.

The PGDip aims 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. 

The course 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 and report writing.

You will be assessed on your performance in two written examinations around May, and through your submitted reports in assessed practical problems set during the year. 

The Department of Statistics has made some changes to the content and delivery of the course and the revised 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 in 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 PGDip in Statistical Science (previously the PGDip 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 highly 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
• References/letters of recommendation:Three overall, generally academic

ENGLISH LANGUAGE REQUIREMENTS

Higher level

Want to improve your English level for admission?

Prepare for the program requirements with English Online by the British Council.

• ✔️ Flexible study schedule
• ✔️ Experienced teachers
• ✔️ Certificate upon completion

📘 Recommended for students with an IELTS level of 6.0 or below.

Enroll in the course