Knowledge Discovery and Datamining

Contemporary data collection can be automated and on a massive scale (e.g. credit card transaction databases). Large databases potentially carry a wealth of important information that could inform business strategy, identify criminal activities, characterise network faults, and much more besides. These large scale problems may preclude the standard carefully constructed statistical models, necessitating highly automated approaches.

Applications for Summer 2022 entry have now closed. Future dates and application deadlines will be released in due course.

Course type

Short course at postgraduate taught (Masters) level, leading to a St Andrews Certificate of Completion (module with credit)

Course length

14 weeks

Start date

Monday 11 July 2022

Entry requirements

  • Educated to degree level
  • B in Mathematics at Higher, A-level or equivalent
    • or relevant professional experience.
  • Have prior experience of coding in any language
  • Have access to a Windows or Mac-platform computer of good quality
  • Have a stable internet connection and a Google account.

Tuition fees


Funding support for Scottish applicants is available. 

Application deadlines

12 noon, Friday 10 June 2022. Applicants should apply as early as possible, applications and scholarships will close when the programme is full.


This module will be delivered entirely online through guided independent study and four 1-hour practical sessions.

Time commitment

  • Scheduled online contact hours 6 hours
  • Guided Independent study hours 144 hours


The module will be assessed on the basis of Coursework (40% module grade) and a 2-hour written exam (60% module grade).

Funding for Scottish applicants

The cost of the module is £1,200.

Scottish Funding Council backed University of St Andrews upskilling scholarships are available to candidates who meet the following eligibility criteria;

Candidates must be:

  • Resident in Scotland and (one of the following must apply)
    • be a UK citizen
    • EU citizens: have a settled or pre-settled status
    • Other citizens: have a residency permit
  • Over 25 years of age
  • Not a University of St Andrews student or staff member.

There is a small number of scholarships available, they will be allocated by application date. Early application is advised. 

Find out more about funding support

Course information

This course covers many of the methods found under the banner of Datamining, building from a theoretical perspective but ultimately teaching practical application.

Topics covered include:

  • historical and philosophical perspectives
  • model selection algorithms and optimality measures
  • tree methods, bagging and boosting
  • neural nets, and classification in general.

Practical applications build sought-after skills in programming (typically R, SAS or python). Course projects and practicals are designed to demonstrate value to organisations and enterprises, with recent examples including house price prediction using data, insurance claim value and frequency prediction using corporate data, diagnosis and staging of Alzheimer’s disease using fMRI scan data, stock market price prediction using deep learning applied to Bloomberg and Reuters data, and credit risk assessment based on sentiment analysis of Twitter and Reuters data.

Learning outcomes

  • Understand the mathematics underpinning common machine-learning/data-mining methods, including parameter estimation
  • Determine what models are applicable for different data and objectives
  • Understand complex regressions from the perspective of basis functions, tree methods, boosting/bagging/ensemble model variants, neural networks, deep-learning, and other selected method
  • Conduct hyperparameter-tuning/model-selection as appropriate to the model
  • Manipulate data, fit models, and summarise/display their results/performance and objectively compare models in R, Python or other suitable language
  • Conduct comprehensive analysis of large real-world data, within a group, covering: data preparation; model fitting, critique & refinement; and presentation of results to a range of audiences

Application and award


Applications are made online. You will need to include details of your formal educational qualifications and a short personal statement explaining how the module will support your future career intentions or employment opportunities.


Subject to the completion of all work and the passing of the module at the appropriate level, candidates will be awarded a University of St Andrews Certificate of Completion (module with credit).


If you have questions about applying for this programme or would like further information, please contact Gerald Prescott, Associate Dean, Education (Science) on