(Postgraduate Diploma) Postgraduate Diploma
University of Essex
University of Essex
A degree with an overall mid 2.2 in one of the following subjects: Mathematics, Statistics, Operational research, Finance, Economics, Business Engineering, Computing, Biology, Physics or Chemistry.
Will consider applicants with a unrelated degree but which contained at least three modules in calculus, algebra, differential equations, probability & statistics, optimisation or other mathematical modules.
Applications from students with a 2:2 or equivalent will be considered dependent on any relevant professional or voluntary experience, previous modules studied and/or personal statement.
IELTS (International English Language Testing System) code
IELTS 6.0 overall with a minimum component score of 5.5
If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.
The University uses academic selection criteria to determine an applicant’s ability to successfully complete a course at the University of Essex. Where appropriate, we may ask for specific information relating to previous modules studied or work experience.
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Prof Fionn Murtagh
Professor of Data Science
External Examiners provide an independent overview of our courses, offering their expertise and help towards our continual improvement of course content, teaching, learning, and assessment.
External Examiners are normally academics from other higher education institutions, but may be from the industry, business or the profession as appropriate for the course.
They comment on how well courses align with national standards, and on how well the teaching, learning and assessment methods allow students to develop and demonstrate the relevant knowledge and skills needed to achieve their awards.
External Examiners who are responsible for awards are key members of Boards of Examiners. These boards make decisions about student progression within their course and about whether students can receive their final award.
Statistics is a degree designed for statisticians who are oriented towards applying their skills into understanding and analysing data using modern statistical methods.
Its teaching aims are to equip students with an equivalent level of statistical knowledge and skills that are currently in demand in statistically oriented employment in business, commerce, industry, government service, health, the field of education and in the wider economy.
To provide students with a foundation for further study, research and professional development.
To produce graduates who are statistically literate and capable of appreciating a logical argument.
To produce graduates who can perform data analysis, understand randomness and sampling.
To provide teaching which is informed and enhanced by the research activities of the staff.
Learning outcomes and learning, teaching and assessment methods
On successful completion of the programme a graduate should demonstrate knowledge and skills as follows:
A: Knowledge and understanding
A1: Knowledge and understanding of some statistical modelling methods and techniques, such as regression modelling and stochastic modelling.
A2: Knowledge and understanding of some statistical methods for analysing statistical models, such as statistical estimation and hypothesis test.
A3: Knowledge and understanding of different statistical inference methods, such as likelihood inference and Bayesian inference.
A4: Knowledge and understanding of how to apply statistical techniques into different areas, such as finance, epidemiology and medical research.
A5: Knowledge and understanding of current research development in statistical methodologies.
Lectures are the principal method of delivery for the concepts and principles involved in A1 - A5.
Students are also directed to reading from textbooks and material available online.
In some modules, understanding is enhanced through the production of a written report.
Understanding is reinforced by means of classes, laboratories and assignments (A1 - A5).
Achievement of knowledge outcomes is assessed primarily through unseen closed-book examinations and also, in some modules, through marked coursework, laboratory reports, statistical assignments, project reports and oral examinations (A1-A5).
Regular problem sheets provide formative assessment in all modules.
Methods employed to assess knowledge and understanding statistics include class presentations, written coursework, project work and class tests.
B: Intellectual and cognitive skills
B1: Identify an appropriate statistical model for a specific statistical problem.
B2: Analyse a given statistical problem and select the most appropriate tools for its solution.
B3: Use critical probability and statistics to evaluate of statistical methods and outputs.
B4: Perform statistical data analysis.
B5: Use probability and statistics for research.
The basis for intellectual skills in statistics modules is provided in lectures, and the skills are developed by means of recommended reading, guided and independent study, assignments and project work.
B1 - B4 are developed through exercises supported by classes.
B1 - B4 are all-important aspects of the projects that constitute a part of some modules
B5 is acquired through the MA902 project.
Achievement of intellectual skills in statistics modules is assessed primarily through unseen closed-book examinations, and also through marked assignments and project work.
Methods employed to assess knowledge and understanding statistics include presentations, written coursework, project work and class tests.
C: Practical skills
C1: Use computational tools and packages.
C2: The ability to apply a rigorous, analytic, highly numerate approach to a problem.
C3: Organising and presenting data.
C4: Gathering and processing information from different sources.
C5: Make an effective literature search
C6: Prepare a technical report
C7: Give a presentation and defend their ideas in an interview.
The practical skills of statistics are developed in exercise classes, laboratory classes, assignments and project work.
C1 is acquired through the learning of at least one programming language and the use of a number of computer packages, as a part of the teaching of modules for which they are relevant.
C2 is acquired and enhanced throughout the programme.
C3 is acquired through such methods as group discussion of topical themes and analysis of authentic materials in class; laboratory work involving use of dedicated software and Web materials; and staff advice, feedback and interaction with students.
C4 is acquired and enhanced throughout the programme.
C5-C7 are acquired and enhanced through the MA902 project.
Achievement of practical skills C1 and C2 is assessed through marked coursework, project reports and oral examinations.
Methods employed to assess practical skills C3 and C4 typically include class presentations, written coursework, written exams, class tests, web-based assignments.
Methods employed to assess practical skills C5-C7 include: writing thesis reports and thesis viva.
D: Key skills
D1: Writing statistical arguments, ideas, outputs and other information clearly into a report.
D2: Use appropriate IT facilities as a tool in the analysis of mathematical problems, word processing, finding modern language materials etc.
D3: Use statistical techniques correctly.
D4: Analyse complex problems and find effective solutions.
D5: Improve own learning and performance from feedback.
D6: Working autonomously showing organisation, self-discipline and time management