Skip to main content

Study information

Statistics (2024)

1. Programme Title:

Statistics

NQF Level:

7

2. Description of the Programme (as in the Business Approval Form)

This degree provides the skills needed to collect, analyse, model and extract meaningful information from data in a way that is relevant to a broad range of careers. It provides both the methodological and computational foundations that you will need together with practical examples of their application using real-world data. You will be equipped with the skills that you will need if you wish to pursue a career in statistics and related areas, or if you are looking to engage in postgraduate research.

Building on your existing mathematical and quantitative skills, you will encounter a wide variety of methods for performing statistical modelling and inference, from traditional approaches to those at the cutting-edge of current research. You will learn how to perform complex statistical analyses through working on real data from a wide range of settings and how to communicate your findings to a variety of audiences.

Taught by experts in both the theoretical and applied aspects of statistical analyses, you will cover both the theory and application of traditional and modern statistical methods from the foundations of statistical theory to the application of cutting-edge regression models.Your summer project will provide the opportunity to apply in detail or potentially develop some methods you have been taught.

Statistics is a growth area with excellent career development potential. University of Exeter is a world-class research active institution which regularly features in UK Top-10 and Global Top-100 rankings. The University is making significant new investment in Statistics and Data Science.

 

3. Educational Aims of the Programme

The MSc in Statistics will provide comprehensive training in all aspects of modern statistics: methods, application and computation. There is a strong focus on applying statistical inference and statistical modelling to practical problems in a variety of settings. A wide variety of approaches to statistical inference and methods for modelling will be covered and throughout the programme with a strong emphasis on learning methods and techniques through their application using real-world examples. Specific focus will be on the communication of results and understanding the implications that different data generating mechanisms can have on interpretation. Course content will range from introductory material, building upon previous mathematical experience to introduce the concepts of statistical inference and uncertainty and covering the coding techniques that will be required throughout the programme, to the application of cutting-edge methods for analysing complex patterns in data, and exploring the links between statistical modelling and machine learning (known as statistical learning).

Content will be delivered through a combination of lectures, hands-on practical sessions, individual self-study, and group work on Exeter’s Streatham campus.

4. Programme Structure

The MSc Statistics is a one-year full-time programme of study at Regulated Qualifications Framework (RQF) level 7 (as confirmed against the FHEQ). You will be located at the Streatham campus for the duration of your studies.

Your programme is also divided into units of study called ‘modules’ which are assigned a number of ‘credits’. The credit rating of a module is proportional to the total workload, with 1 credit being nominally equivalent to 10 hours of work.

Exit Awards

You may exit this award with a Postgraduate Certificate in Statistics where you have achieved 60 credits or a Postgraduate Diploma in Statistics where you have achieved 120 credits. 

5. Programme Modules

The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual review of this programme. Details of the modules currently offered may be obtained from the Faculty website:

http://intranet.exeter.ac.uk/emps/studentinfo/subjects/mathematics/)

The programme comprises:

  • 90 credits of compulsory modules plus a 60 credit Research Project in Statistics.
  • 30 credits of free choice among the options listed below or other elective modules. You may take elective modules as long as any necessary prerequisites have been satisfied, where the timetable allows and if you have not already taken the module in question or an equivalent module.

 

Stage 1

Code Title Credits Compulsory NonCondonable
Compulsory Modules:
MTH3028Statistical Inference: Theory and Practice15YesNo
MTHM033Statistical Modelling in Space and Time15YesNo
MTHM047Bayesian Statistics, Philosophy and Practice 15YesNo
MTHM050Research Project in Statistics60YesYes
MTHM503Applications of Data Science and Statistics15YesNo
MTHM507Communicating Data Science15YesNo
45 credits of free choice among options listed below or other elective modules:
ECMM422Machine Learning15NoNo
ECMM450Stochastic Processes15NoNo
MTH3045Statistical Computing15NoNo
MTHM002Methods for Stochastics and Finance15NoNo
MTHM006Mathematical Theory of Option Pricing 15NoNo
MTHM063Uncertainty Quantification15NoNo
MTHM611Topics in Environmental Intelligence15NoNo
SOCM033Data Governance and Ethics15NoNo

135 credits of compulsory modules (including 60 credit Research Project in Statistics), 45 credits of optional modules.

6. Programme Outcomes Linked to Teaching, Learning & Assessment Methods

On successfully completing the programme you will be able to: Intended Learning Outcomes (ILOs) will be accommodated & facilitated by the following learning & teaching and evidenced by the following assessment methods:

A Specialised Subject Skills & Knowledge

1 Employ a range of statistical methods and techniques for detecting and modelling patterns in data

2 Apply a range of statistical modelling and computation techniques to real-world problems

3 Communicate the results of complex analyses with an understanding of how the source of data, and how it was collected, can have an effect on subsequent analyses.

 

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

 

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include:

Closed book examinations (1), written reports (1, 2,3),practical exercises in coding and data analysis (1, 2), presentations (2, 3)

B Academic Discipline Core Skills & Knowledge

4) Understand the methodology, and practical use, of statistical modelling and different approaches to statistical inference

5) Select appropriate methods based on the problem being addressed

6) Critically analyse and interpret relevant academic and technical literature.

7) The ability to understand the technical details behind new methods and appraise their suitability before applying them

8) Effectively handle large and complex datasets and prepare them for analysis.

9) Understand the importance and practical use of the graphical representation of summaries of, and patterns in, data and the ability to use appropriate methods for data visualisation and presentation of data.

 

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include:

Closed book examinations (4, 5), written reports (4 - 9), practical exercises in coding and data analysis (6 - 9), presentations (4, 5, 6, 7, 9)

C Personal / Transferable / Employment Skills & Knowledge

10) Effectively communicate methods and results based on analysis of complex datasets in both written reports and oral presentations.

12) Demonstrate awareness of tools and technologies relevant to statistical modelling and inference.

13) Design and manage a data analysis project from initiation to final report.

14) Work effectively independently or in a team.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include:

Closed book examinations (1), written reports (10 - 14),practical exercises in coding and data analysis (12), presentations (11, 12, 13, 14).

7. Programme Regulations

Full details of assessment regulations for all taught programmes can be found in the TQA Manual, specifically in the Credit and Qualifications Framework, and the Assessment, Progression and Awarding: Taught Programmes Handbook.

Additional information, including Generic Marking Criteria, can be found in the Learning and Teaching Support Handbook

8. College Support for Students and Students' Learning

In accordance with University policy, a system of Personal Tutors is in place for all students on this programme. A University-wide statement on such provision is included in the University's TQA Manual. As a student enrolled on this programme, you will receive the personal and academic support of the Programme Coordinator and will have regular scheduled meetings with your Personal Tutor; you may request additional meetings as and when required. The role of personal tutors is to provide you with advice and support for the duration of the programme and extends to providing you with details of how to obtain support and guidance on personal difficulties, such as accommodation, financial difficulties and sickness. You can also make an appointment to see individual teaching staff.

Information Technology (IT) Services provide a wide range of services throughout the Exeter campuses including open access computer rooms, some of which are available 24 hours, 7 days a week. Help may be obtained through the Helpdesk, and most study bedrooms in halls and flats are linked to the University's campus network.

Additionally, the College has its own dedicated IT support staff, helpdesk and computer facilities which are linked to the wider network, but which also provide access to some specialised software packages. Email is an important channel of communication between staff and students in the College and an extensive range of web-based information (see https://intranet.exeter.ac.uk/emps/) is maintained for the use of students, including a comprehensive and annually revised student handbook.

The Harrison Learning Resource Centre is generally open during building open hours. The Centre is available for quiet study, with four separate rooms that can be booked for meetings and group work. Amongst its facilities, the Learning Resource Centre has a number of desks, four meeting rooms with large LCD screens, and free use of a photocopier. Also available are core set texts from your module reading lists, and undergraduate and MSc projects from the past two years.

Online Module study resources provide materials for modules that you are registered for, in addition to some useful subject and IT resources. Generic study support resources, library and research skills, past exam papers, and the 'Academic Honesty and Plagiarism' module are also available through the student portal (http://vle.exeter.ac.uk).

Student/Staff Liaison Committee enables students & staff to jointly participate in the management and review of the teaching and learning provision.

10. Admission Criteria

Postgraduate applicants must satisfy the Postgraduate Admissions Policy of the University of Exeter.

Specific requirements required to enrol on this programme are available at the respective Undergraduate or Postgraduate Study Site webpages.

Candidates will normally have a 2:1 honours degree in a quantitative subject, for example: statistics, mathematics, engineering or physics.

Requirements for international students

If you are an international student, please visit our international equivalency pages to enable you to see if your existing academic qualifications meet our entry requirements.

English language requirements

IELTS (Academic): Overall score 6.5. No less than 6.0 in any section.

TOEFL IBT*: Overall score 90 with minimum scores of 21 for writing, 21 for listening, 22 for reading and 23 for speaking.

11. Regulation of Assessment and Academic Standards

Each academic programme in the University is subject to an agreed Faculty assessment and marking strategy, underpinned by institution-wide assessment procedures.

The security of assessment and academic standards is further supported through the appointment of External Examiners for each programme. External Examiners have access to draft papers, course work and examination scripts. They are required to attend the Board of Examiners and to provide an annual report. Annual External Examiner reports are monitored at both Faculty and University level. Their responsibilities are described in the University's code of practice. See the University's TQA Manual for details.

12. Indicators of Quality and Standards

Certain programmes are subject to accreditation and/or review by professional and statutory regulatory bodies (PSRBs). This programme is not subject to any such requirements.

 

14 Awarding Institution University of Exeter
15 Lead College / Teaching Institution College of Engineering, Mathematics and Physical Sciences
16 Partner College / Institution N/A
17 Programme accredited/validated by N/A
18 Final Award(s) MSc
19 UCAS Code (UG programmes) mscstats
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 180 credits (90 ECTS)
22 QAA Subject Benchmarking Group (UG and PGT programmes) Mathematics, Statistics and Operational Research
23 Origin Date March 15th 2024 Last Date of Revision: April 29th 2024