• Goldsmiths, University of London Featured Masters Courses
  • Xi’an Jiaotong-Liverpool University Featured Masters Courses
  • University of Leeds Featured Masters Courses
  • University of Edinburgh Featured Masters Courses
  • University of Cambridge Featured Masters Courses
  • Ulster University Featured Masters Courses
  • Jacobs University Featured Masters Courses
  • Swansea University Featured Masters Courses
Ross University School of Veterinary Medicine Featured Masters Courses
Queen Mary University of London Featured Masters Courses
University of Leicester Featured Masters Courses
Imperial College London Featured Masters Courses
Newcastle University Featured Masters Courses

Applied Statistics and Datamining (MSc)

Course Description

MSc in Applied Statistics and Datamining

• Aimed at individuals with a good degree containing quantitative elements, who wish to gain statistical data analysis skills relevant to business, commerce and other applications.

• Preparation for commercial data analysis.
• A commercially relevant programme of study that has content aligned with the requirements of partners in the commercial analysis sector.

• Strongly applied bias, with an emphasis on application in the commercial sector.

• Dissertation topics are generated in part by our commercial partners.

• Teaching includes widespread commercial software packages e.g. SAS, SPSS, along with popular open-source tools e.g. R.

• Teaching consists of a mixture of short, intense courses with a large proportion of continuous assessment and more traditional lecture courses with end of semester exams.

• A graduate from this programme would be seeking employment as an analyst within a company, research body, government, or as a statistical consultant.


* Opportunities to work closely, and undertake project work, within a research group.

* Access to a wide range of advanced MMath courses across the entire spectrum of Mathematics and Statistics.

* The School is well equipped with personal computers and laptops, a parallel computer and an on-site library, and has attracted substantial amounts of external funding.


Our graduates hold positions at leading universities or companies in areas as diverse as business administration, computer science and modelling, fisheries laboratories and pure mathematics. In short, a postgraduate degree in mathematics or statistics from St Andrews opens the way for a variety of careers.
Our recent graduates at Masters and Doctoral level have, amongst other things:
• Moved on to postdoctoral studies.
• Joined the academic staff of leading UK and international universities.
• Found highly-paid positions in analysing futures/finance for large consulting firms and major financial institutions, for example: Scottish and Southern Energy, RBS, Capital One, Aquila Insight, Aviva, PwC, American Express, Goldman Sachs, Tesco Bank.
• Found rewarding and challenging positions in the computer industry.
• Found academically rewarding positions and careers in government agencies, including, for example, GCHQ.
• Joined government and non-governmental organisations to advise wildlife and conservation managers, including, for example, the Wildlife Conservation Society (WCS).
• Improved their mathematics qualifications, hence enhancing their positions and prospects in the secondary and tertiary education sectors.

Visit the Applied Statistics and Datamining (MSc) page on the University of St Andrews website for more details!

Entry Requirements

Our minimum academic requirement for entry to most programmes is a 2:1 degree classification from a recognised UK university or the equivalent in a subject-related area

Course Fees

2017/18: Home/EU £7,500; Overseas £17,090

Email Enquiry

Recipient: University of St Andrews
Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.
Email Sent

Share this page:

Cookie Policy    X