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.
Postgraduate, leading to a Postgraduate Diploma (PGDip) or Master of Science (MSc)
- Start date: 7 September 2020
- End date: 30 June 2021 (PGDip) or 30 September 2021 (MSc)
Information about all programmes from previous years of entry can be found in the archive.
Ten months full time (PGDip); one year full time (MSc)
- 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, see the international entry requirements.
- English language proficiency. See English language tests and qualifications.
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.
UK and EU: £9,450
Wednesday 12 August 2020. Applicants should apply as early as possible to be eligible for certain scholarships and for international visa purposes.
- 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.
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.
- 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
- Statistical Statistics
- Time Series Analysis
- 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.