Statistics
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(Postgraduate Diploma) Postgraduate Diploma
Statistics
Current
University of Essex
University of Essex
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Postgraduate Diploma
DIP G30009
15/01/2015
Details
Professional accreditation
None
Admission criteria
A degree with an overall 2:1.
IELTS (International English Language Testing System) code
IELTS 6.0 overall with a minimum component score of 5.5
Course qualifiers
A course qualifier is a bracketed addition to your course title to denote a specialisation or pathway that you have achieved via the completion of specific modules during your course. The
specific module requirements for each qualifier title are noted below. Eligibility for any selected qualifier will be determined by the department and confirmed by the final year Board of
Examiners. If the required modules are not successfully completed, your course title will remain as described above without any bracketed addition. Selection of a course qualifier is
optional and student can register preferences or opt-out via Online Module Enrolment (eNROL).
None
Rules of assessment
Rules of assessment are the rules, principles and frameworks which the University uses to calculate your course progression and final results.
Additional notes
None
External examiners
Prof Lazoros Papageorgiou
Professor
University College London
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.
Key
|
|
|
| Core |
You must take this module.
You must pass this module. No failure can be permitted. |
| Core with Options |
You can choose which module to study.
You must pass this module. No failure can be permitted. |
| Compulsory |
You must take this module.
There may be limited opportunities to continue on the course/be eligible for the degree if you fail. |
| Compulsory with Options |
You can choose which module to study.
There may be limited opportunities to continue on the course/be eligible for the degree if you fail. |
| Optional |
You can choose which module to study.
There may be limited opportunities to continue on the course/be eligible for the degree if you fail. |
Exit awards
A module is given one of the following statuses: 'core' – meaning it must be taken and passed; 'compulsory' – meaning it must be taken;
or 'optional' – meaning that students can choose the module from a designated list. The rules of assessment may allow for limited condonement
of fails in 'compulsory' or 'optional' modules, but 'core' modules cannot be failed. The status of the module may be different in any exit
awards which are available for the course. Exam Boards will consider students' eligibility for an exit award if they fail the main award or do not complete their studies.
Programme aims
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.
Learning methods
Assessment methods
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.
Learning methods
Assessment methods
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.
Learning methods
Assessment methods
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
Learning methods
Assessment methods
Note
The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes,
for example to courses, facilities or fees. Examples of such reasons might include a change of law or regulatory requirements, industrial action, lack of demand, departure of
key personnel, change in government policy, or withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery
of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour
to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications.
The full Procedures, Rules and Regulations of the University governing how it operates are set out in the
Charter, Statutes and Ordinances and in the
University Regulations, Policy and Procedures.
Contact
If you are thinking of studying at Essex and have questions about the course, please contact Undergraduate Admissions by emailing admit@essex.ac.uk, or Postgraduate Admissions by emailing pgadmit@essex.ac.uk.
If you're a current student and have questions about your course or specific modules, please contact your department.
If you think there might be an error on this page, please contact the Course Records Team by emailing crt@essex.ac.uk.