Course summary
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. 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.
Modules
Compulsory
- 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 Statistical Modelling using GLMs: covers the main aspects of linear models and generalised linear models, including model specification, various options for model selection, model assessment and tools for diagnosing model faults.
- 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.
- Multivariate Analysis: introductory and advanced training in the applied analysis of multivariate data.
- Software for Data Analysis: covers the practical computing aspects of statistical data analysis focusing on widely used packages, including data-wrangling and visualisation.
- Bayesian Inference
- Classical Statistical Inference
- Computing in Mathematics
- Computing in Statistics
- Design of Experiments
- Financial Mathematics
- Markov Chains and Processes
- Advanced Bayesian Inference
- Advanced Combinatorics
- Estimating Animal Abundance and Biodiversity
- Independent Study Module
- Mathematical Oncology
- Data-Intensive Systems
- Database Management Systems
- Information Visualisation
Assessment method
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.
Entry requirements
A good 2.1 undergraduate Honours degree in Mathematics, Statistics or in an area with substantive mathematical or statistical content. If you studied your first degree outside the UK, please see the university’s international entry requirements.
English language requirements
For the current English Language requirements please visit the English language requirements for postgraduate students on the University of St Andrews website.
English language requirements for postgraduate students
https://www.st-andrews.ac.uk/subjects/entry/language-requirements/postgraduate/
Fees and funding
Tuition fees
No fee information has been provided for this course
Tuition fee status depends on a number of criteria and varies according to where in the UK you will study. For further guidance on the criteria for home or overseas tuition fees, please refer to the UKCISA website .
Additional fee information
For the most current information on course fees please visit https://www.st-andrews.ac.uk/study/fees-and-funding/postgraduate/taught/.
Sponsorship information
Carnegie-Cameron bursaries; entrant accommodation bursary; Formula Santander postgraduate scholarship; recent graduate discount; Thomas and Margaret Roddan Trust bursary; SAAS postgraduate funding.
Provider information
University of St Andrews
College Gate
St Andrews
KY16 9AJ