(Postgraduate Diploma) Postgraduate Diploma
Statistics
Current
University of Essex
University of Essex
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Postgraduate Diploma
Full-time
None
DIP G30009
10/05/2023
Details
Professional accreditation
None
Admission criteria
We will consider applicants with a 2:1 degree in one of the following subjects:
- Mathematics,
- Statistics
- Operational research
- Computer Science
- Applied Mathematics
- Pure Mathematics
- Biostatistics
- Economic Statistics
- Statistics
- Economics
OR
A 2.1 degree in any subject which includes:
One module in:
- Calculus
- Maths
- Engineering Maths
- Advanced Maths
And one module in
- Statistics or Probability
- Maths
- Engineering Maths
- Advanced Maths
And one additional relevant module, from
- Algebra
- Analysis
- Programming language (R, Matlab or Python)
- A second module in Probability or Statistics
- Numerical Methods
- Complex Numbers
- Differential Equations
- Optimisation (Linear Programming)
- Regression
- Stochastic Process
- Maths
- Engineering Maths
- Advanced Maths
Applicants with a degree below 2:1 or equivalent will be considered dependent on any relevant professional or voluntary experience and previous modules studied.
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.
Additional Notes
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.
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
Dr Yinghui Wei
Dr Murray Pollock
Director of Statistics / Senior Lecturer
Newcastle University
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.
Programme aims
Degree designed for statisticians who are oriented towards applying their skills into understanding and analysing data using modern statistical methods.
- 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
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).
Assessment methods
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.
Learning methods
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
B1 - B5 are all-important aspects of the projects that constitute a part of some modules
B5 is acquired through all taught modules and MA199
Assessment methods
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
Learning methods
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-C6 are acquired and enhanced through MA981.
C5 and C6 are acquired and enhanced through the MA199 and MA317, MA321 and MA322.
Assessment methods
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-C6 include: writing thesis reports
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