Applied Statistics and Datamining (PGDip/MSc) 2020 entry

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.

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Course type

Postgraduate, leading to a Postgraduate Diploma (PGDip) or Master of Science (MSc)

Course dates

  • Start date: 7 September 2020
  • End date: 30 June 2021 (PGDip) or 30 September 2021 (MSc)

If you started this programme in 2019, you can find information about 2019 entry on the 2019 Applied Statistics and Datamining page. Information about all programmes from previous years of entry can be found in the archive.

Course duration

Ten months full time (PGDip); one year full time (MSc)

Entry requirements

The qualifications listed are indicative minimum requirements for entry. Some academic Schools will ask applicants to achieve significantly higher marks than the minimum. Obtaining the listed entry requirements will not guarantee you a place, as the University considers all aspects of every application including, where applicable, the writing sample, personal statement, and supporting documents.

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Tuition fees

UK and EU: £9,450
Overseas: £19,400

Application deadline

Wednesday 12 August 2020. Applicants should apply as early as possible to be eligible for certain scholarships and for international visa purposes.

Application requirements

  • CV
  • two original signed academic references
  • academic transcripts and degree certificates 
  • evidence of English language proficiency (required if English is not your first language)
  • personal statement (optional).

For more guidance, see supporting documents and references for postgraduate taught programmes.

Course information

The PGDip/MSc in Applied Statistics and Datamining is a taught programme run by the School of Mathematics and Statistics. The course is aimed at those with a good degree containing quantitative elements who wish to gain statistical data analysis skills.

Highlights

  • Commercially relevant course.
  • Course content is aligned to the requirements of the commercial analysis sector.
  • Dissertation topics are generated in part by commercial partners.
  • Teaching involves widely used commercial software packages (SAS, SPSS).
  • The popular open-source tool R is also used. 

Teaching format

The programme consists of two semesters with taught components which include a mixture of short, intensive 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 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.

Modules

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 which is for the 2019-2020 academic year; some elements may be subject to change for 2020 entry.

  • Advanced Data Analysis: covers modern modelling methods for situations where the data fails to meet the assumptions of common statistical models and simple remedies do not suffice.
  • Applied Multivariate Analysis: introductory and advanced training in the applied analysis of multivariate data.
  • Applied Statistical Modelling using GLMs: covers the main aspects of linear models and generalized linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.
  • Computing in Statistics: teaches computer programming skills, including principles of good programming practice, with an emphasis on statistical computing.
  • Introductory Data Analysis: covers essential statistical concepts and analysis methods relevant for commercial analysis. 
  • Knowledge Discovery and Datamining: covers many of the methods found under the banner of "datamining", building from a theoretical perspective but ultimately teaching practical application.
  • Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation. 

Students choose one optional module, which can be chosen from the School's modules at level 3000 or above.

Undergraduate-level modules

  • Bayesian Inference
  • Classical Statistical Inference
  • Computing in Mathematics
  • Design of Experiments
  • Financial Mathematics
  • Markov Chains and Processes
  • Mathematical Biology 1
  • Population Genetics
  • Quantitative Risk Management 
  • Sampling Theory
  • Spatial Statistics
  • Statistical Statistics
  • Time Series Analysis

Postgraduate-level modules

  • Advanced Combinatorics
  • Estimating Animal Abundance and Biodiversity
  • Independent Study Module
  • Mathematical Biology 2
  • Mathematical Oncology
  • Medical Statistics

Computer Science 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:

  • Data-Intensive Systems
  • Database Management Systems
  • Information Visualisation and Visual Analytics

Optional modules are subject to change each year, and some may only allow limited numbers of students (see the University's position on curriculum development).

MSc students complete a dissertation during the final three months of the course to be submitted near 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. Many topics are in collaboration with companies and other external bodies.

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.


The modules listed here are indicative, and there is no guarantee they will run for 2020 entry. Take a look at the most up-to-date modules in the module catalogue.

Visit St Andrews

If you are interested in studying at St Andrews, join us at a visiting day to explore the town, find out about our courses and meet current students.

Upcoming postgraduate visiting days:

  • Wednesday 11 March 2020

Sign up

Virtual events

Find out more about studying at St Andrews, why it's unique and what it will do for your future.

Upcoming virtual events:

  • Friday 22 November at 9.15am (UK time)
  • Friday 29 November at 3.15pm (UK time)
  • Thursday 5 December at 9.15am (UK time)
  • Thursday 12 December at 3.15pm (UK time)

Register

Conferences and events

There are a number of different seminars held each week in the School of Mathematics and Statistics. These include:

Pure Mathematics

Statistics

Funding

There are many potential scholarships or support schemes available to postgraduates.

Recent Graduate Discount
The University of St Andrews offers a 10% discount in postgraduate tuition fees to students who are eligible to graduate or who have graduated from St Andrews within the last three academic years and are starting a postgraduate programme with the University of St Andrews. 

Find out more about postgraduate scholarships.

After the MSc

Research degrees 

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:

  • Pure Mathematics (Analysis Group, Algebra and Combinatorics Group)
  • Applied Mathematics (Vortex Dynamics Group, Solar and Magnetospheric Theory Group, Mathematical Biology Group)
  • Statistics (Statistical Ecology Group, Statistical Inference Group).

PhD in Mathematics and Statistics

Careers

Graduates from this programme typically seek employment as analysts within a company, research body, government, or as statistical consultants.

Recent graduates have found employment in:

  • large consulting firms and major financial institutions including Scottish and Southern Energy, RBS, Aviva, Lloyds, TSB, PwC, Capital One, Vodafone, American Express, Goldman Sachs, Tesco Bank and many others
  • biomedical research, clinical trials and pharmaceuticals
  • wildlife and conservation managers including the Wildlife Conservation Society (WCS).

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 in building their employability skills.

Contact

School of Mathematics and Statistics 
University of St Andrews
Mathematical Institute 
North Haugh 
St Andrews 
KY16 9SS

Phone: +44 (0)1334 46 2344
Email: maths-pgstats@st-andrews.ac.uk

Mathematics and Statistics website

Policies

Admission to the University of St Andrews is governed by our admissions policy.

Curriculum development

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).

Tuition fees

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).