Applied Statistics and Data Mining (Taught)

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.

Course 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.

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

• Bayesian Inference
• Classical Statistical Inference
• Computing in Mathematics
• Computing in Statistics
• Design of Experiments
• Financial Mathematics
• Markov Chains and Processes

Optional Postgraduate modules
Examples include:

• Advanced Bayesian Inference
• Advanced Combinatorics
• Estimating Animal Abundance and Biodiversity
• Independent Study Module
• Mathematical Oncology

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

Dissertation
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

Further information is available via the "Course contact details" section at the bottom of this webpage.