The PGDip/MSc in Applied Statistics and Datamining is a commercially relevant programme of study providing students with the statistical data analysis skills needed for business, commerce and other applications.
Postgraduate; leading to a Postgraduate Diploma (PGDip) or Master of Science (MSc)
One year full time
A good 2.1 undergraduate Honours degree in Mathematics, Statistics or in an area with substantive mathematical/statistical content. If you studied your first degree outside the UK, see the international entry requirements.
English language proficiency. See English language tests and qualifications.
UK and EU: £6,800
Overseas: £16,250
Applications for 2016 entry are now closed. Please check back for applications information for 2017 entry.
For more guidance, see supporting documents and references for postgraduate taught programmes.
If you are looking to start this programme in 2017, you can find information about 2017 entry on the 2017 Applied Statistics and Datamining (PGDip/MSc) page. Information about all programmes from previous years of entry can be found in our archive.
Watch current students and staff discuss the teaching facilities, research opportunities and student life at Scotland's first university.
The PGDip/MSc in Applied Statistics and Datamining is a one-year taught programme run by the School of Mathematics and Statistics. The programme is aimed at those with a good degree containing quantitative elements who wish to gain statistical data analysis skills.
The programme consists of two semesters with taught components which include a mixture of short, intense courses with a large proportion of continuous assessment and more traditional lecture courses with end of semester exams.
For those on the MSc, the taught component will be followed by a 15,000-word dissertation project taking place during the last three months of the course.
The School of Mathematics and Statistics is well equipped with personal computers and laptops, a parallel computer and an on-site library.
Further particulars regarding curriculum development.
The modules in this programme have varying methods of delivery and assessment. For more details of each module, including weekly contact hours, teaching methods and assessment, please see the latest module catalogue.
Students choose two optional modules, which can be chosen from across the School's undergraduate and postgraduate-level modules.
Computing in Statistics is strongly recommended unless you have extensive R experience.
Undergraduate-level modules
Postgraduate-level modules
In addition, students may take modules from the School of Computer Science that are consistent with the degree. Representative examples of these modules are:
The modules listed ran in the academic year 2015-2016 and are indicative of this course. There is no guarantee that these modules will run for 2016 entry.
Take a look at the most up to date modules in the module catalogue.
MSc students complete a 15,000-word dissertation during the final three months of the course to be submitted by the end of August. Dissertations are supervised by members of teaching staff who will advise on the choice of subject and provide guidance throughout the progress of the dissertation.
If students choose not to complete the dissertation requirement for the MSc, there is an exit award available that allows suitably qualified candidates to receive a Postgraduate Diploma. By choosing an exit award, you will finish your degree at the end of the second semester of study and receive a PGDip instead of an MSc.
There are a number of different seminars held each week in the School of Mathematics and Statistics. These include:
Pure Mathematics
Pure Mathematics Colloquia
Algebra and Combinatorial Seminars
Analysis Group Seminars
Applied Mathematics
Applied Mathematics Seminars
Solar and Magnetospheric Theory Group Seminars
Vortex Dynamics Group Seminars
Statistics
Statistics Seminars
CREEM/NCSE Seminars
There are many potential scholarships or support schemes available to postgraduates.
Recent Graduate Discount
The University of St Andrews offers a 10% reduction in tuition fees for students who have graduated during the last three years and are now starting a postgraduate programme.
Thomas and Margaret Roddan Trust (Postgraduate)
Competitive awards ranging from £500 to £3,000 are usually available for postgraduates undertaking either taught or research courses in Scotland.
The MSc in Applied Statistics and Datamining prepares students for further postgraduate studies in statistical data research, and many graduates of the programme continue their education by enrolling in PhD programmes at St Andrews or elsewhere.
The School of Mathematics and Statistics has active research groups in:
Graduates from this programme typically seek employment as analysts within a company, research body, government, or as statistical consultants.
Our recent graduates at Masters level have found employment in:
The Careers Centre offers one-to-one advice to all students on a taught postgraduate course and offers a programme of events to assist students to build their employability skills.
School of Mathematics and Statistics
Mathematical Institute
North Haugh
St Andrews
Fife KY16 9AL
Scotland
Phone: +44 (0)1334 46 2344
Email: maths-pgstats@st-andrews.ac.uk
Admission to the University of St Andrews is governed by our Admissions policy.
As a research intensive institution, the University ensures that its teaching references the research interests of its staff, which may change from time to time. As a result, programmes are regularly reviewed with the aim of enhancing students' learning experience. Our approach to course revision is described online. (PDF, 72 KB).
The University will clarify compulsory fees and charges it requires any student to pay at the time of offer. The offer will also clarify conditions for any variation of fees. The University’s approach to fee setting is described online. (PDF, 84 KB).
St Andrews has two postgraduate prospectuses - one for taught courses and one for research programmes. Both prospectuses are available for you to view and download.
