(Postgraduate Diploma) Postgraduate Diploma
Optimisation and Data Analytics
Current
University of Essex
University of Essex
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Postgraduate Diploma
Full-time
None
DIP G20109
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
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
1. To enhance the general skills of students (including IT skills, presentation skills, problem solving abilities, numeracy and their ability to retrieve information in an efficient manner).
2. To offer students the opportunity to study data analytics and optimisation to an advanced level within an environment informed by current research.
3. To provide students with advanced training that will be of use in a career as a data analyst or operational researcher.
4. To provide students with training in the preparation of reports involving mathematical material, including correct referencing, appropriate layout and style.
5. To provide students with information that will help them to make an informed judgement as to the appropriate methods to employ when analysing a problem of a data analytics or operations research nature.
6. To provide students with a research-type experience that will aid them in their approach to further research activity.
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
A101: A range of ideas concerning Data analytics and optimisation including methods appropriate in specialized applications.
A102: Ways in which data analytical methods can aid understanding in the social sciences.
A103: One or more current areas of research in Data Analytics or Operational Research, including an awareness of the development of these areas of research.
Learning methods
A1-A3 are principally acquired through the coherent programmes of lectures, exercises and problem classes.
These are supplemented, where appropriate, by the use of computers, computer packages, textbooks, handouts and on-ine material.
In most modules there is regular set work.
This work is marked and this process informs the course teacher of common difficulties that require extra attention during the subsequent problem classes.
Assessment methods
Knowledge and understanding are assessed through examinations and essays.
B: Intellectual and cognitive skills
B1: Analyse a mass of information and carry out an appropriate analysis.
B2: Express a problem in mathematical terms and carry out an appropriate analysis.
B3: Reason critically and interpret information in a manner that can be communicated effectively to non-specialists.
Learning methods
B1-3 These skills are developed through the regular coursework exercises.
In seeking to answer these exercises students become accustomed to identifying key facts in a body of information.
The problems classes provide back-up as required.
Assessment methods
The level of attainment of these skills is assessed through the summer examinations.
C: Practical skills
C1: Carry out analyses of complex data sets, design experiments & analyse practical OR problems.
C2: Use simple algorithms.
C3: Use computer programmes and/or packages
Learning methods
C1-C3 are developed through the programme of lectures, regular exercises and computer work.
Assessment methods
C1-C3 are assessed by regular coursework and examinations.
D: Key skills
D1: Write clearly and effectively
D2: Use computer packages and/or programming languages for data analysis and computation and use computer packages for presentation of material to others.
D3: Enhance existing numerical ability
D4: Choose the appropriate method of inquiry in order to address a range of practical and theoretical problems.
D5: Learn from feedback and respond appropriately and effectively to supervision and guidance
D6: Work pragmatically to meet deadlines.
Learning methods
D1 is promoted by class teachers’ feedback on written solutions to problems.
D2 results from the coursework associated with various modules.
D3 is a natural consequence of modules with high numeric content.
D4 is a consequence of the coursework, problems classes, lectures and laboratory work.
D5-6 result from a tightly timetabled course of lectures and submission dates that require the student to effectively organise time to meet deadlines.
Assessment methods
Key skills are assessed throughout the degree via coursework and examinations.