Programme Specification
A statement of the knowledge, understanding and skills that underpin a
taught programme of study awarded by
The University of Sheffield
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Programme Title
Statistics (MSc)
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Programme Code
MAST01, MAST02
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JACS Code
G300
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Level of Study
Postgraduate
5a
Final Qualification
MSc in Statistics (MSc)
5b
QAA FHEQ Level
Masters
6a
Intermediate Qualification(s)
Postgraduate Diploma, Postgraduate Certificate
6b
QAA FHEQ Level
Masters
7
Teaching Institution (if not Sheffield)
Not applicable
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Faculty
Science
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Home Department
School of Mathematics and Statistics
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Other Department(s) involved in
teaching the programme
Applicable
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Mode(s) of Attendance
Full-time (MAST01); Part-time (MAST02)
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Duration of the Programme
1 year (MAST01), 2 to 4 years (MAST02)
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Accrediting Professional or
Statutory Body
Royal Statistical Society
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Date of production/revision
July 2003, revised January 2009, March 2012, March 2016
15. Background to the programme and subject area
Great Britain has long been recognized as having an especially admirable statistical tradition, in which empirical and
theoretical work continually meet and strengthen each other. The Probability and Statistics group in SoMaS is firmly
in this tradition, both in its teaching and in its research.
The MSc in Statistics provides both a practically-based professional statistical training and a foundation for those
wishing to pursue further research. It is available via distance learning (2-4 years, part time) as well as residential
study (1 year full-time). The course has been running successfully for many years. During that time, it has evolved
into its present form, which concentrates on giving a firm grounding in practical statistical methodology and
computation, as well as developing many of the personal skills in demand from employers. It is a general purpose
course, which provides an excellent foundation for a career as a Statistician or for a PhD in Statistics.
The course has been supported by the Government Research Councils for over 35 years. In recent years it has been
one of only around 6 Statistics MSc courses receiving EPSRC funding.
The MSc is accredited by the Royal Statistical Society. The Society accords GradStat status with one year's relevant
experience towards CStat status to all students who pass the course.
We have contacts with employers through the course's Advisory Board, employer open days, sponsored student
attendance at meetings of Statisticians in the Pharmaceutical Industry, career presentations and dissertation
projects.The School has an international reputation in research, with 89% of research activities being rated as world
leading or internationally excellent in the 2014 Research Excellence Framework exercise. Students can be sure that
the training in this programme is informed by the latest thinking in the subject.
Further information is available from the School web site:
http://www.sheffield.ac.uk/maths/prospectivepg/taughtpg/statistics
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16. Programme aims
In the context of this programme the School aims:
(a) to provide a high quality thorough initial training for professional statisticians, offering good general coverage of
the subject in an up-to-date way;
(b) to provide an intellectual environment conducive to learning;
(c) to prepare students for careers which use their mathematical and statistical training;
(d) to provide teaching which is informed and inspired by the research and scholarship of the staff;
(e) to provide students with assessments of their achievements and to identify and support academic excellence.
17. Programme learning outcomes
Knowledge and understanding. Students will:
K1
demonstrate a reasonable understanding of the relevant body of knowledge.
Knowledge-based skills. Students will:
SK1
(for Certificate:) be able to formulate straightforward problems in statistical terms and analyse data using a
range of standard techniques.
SK2
(for Diploma:) be able to formulate problems in statistical terms, plan studies, and analyse data using a
variety of techniques.
SK3
(for MSc:) be able to formulate problems in statistical terms, plan studies, and analyse data using a wide
variety of techniques.
Skills and other attributes. Students will:
S1
have ability in using at least one major statistical computer package, and general computing skills.
S2
(for Certificate:) be able to prepare short statistical studies and have some experience of preparing longer
reports.
S3
(for Diploma and MSc:) have developed skill in preparing and writing reports (both technical and nontechnical), in other methods of presentation of results, and in working in groups.
S4
(for MSc:) have shown the ability to complete an extended individual study of a statistical problem and to
present the results in a dissertation.
S5
(for MSc:) have developed attitudes and confidence which will allow them to acquire new statistical
knowledge and expertise throughout a subsequent career.
18. Teaching, learning and assessment
Development of the programme learning outcomes is promoted through the following teaching and learning
methods:
MAST01 is a full-time residential programme, with lectures, MAST02 is a part-time distance learning programme.
They are as closely integrated as possible within the constraints this difference imposes. The distance learning
version is designed so that students study the same subjects as their residential counterparts essentially
concurrently.
MOLE
The course materials are made available through My Online Learning Environment (MOLE) via the world wide web
and support is available from a designated personal tutor from the individual module lecturers and from the
course's Course Director via email or telephone. Most communication within the course, particularly between
residential and distance-learning students, takes place via MOLE and so training in its use is given early in the
MSc.
For all modules except the project-based Data Analysis, students are provided with module notes, structured
problems and a schedule of work. The MOLE discussion board is the main vehicle for academic interaction. It also
serves to keep distance-learning students exactly in step with the delivery of material in Sheffield. (K1, SK1-3, S12
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3).
Independent Learning
This is the cornerstone of success in the programme. It is vital for the assimilation of the material provided, for the
preparation of written reports, and other presentations, and for the proper use of sophisticated software.
Residential Weeks
Distance learning students spend three residential weeks in Sheffield. The first of these is the Induction Week.
During that week all students (distance and residential) receive instruction in and gain initial experience of the main
computer package used. They are also introduced to MOLE and its central role explained. Basic, underpinning,
theoretical material is reviewed. (S1, SK1).
Other residential weeks are held at the time of the examinations. Examinations are towards the start of the week and
the second half is used for group work and presentations. (S2-3). Distance learning students also have face-to-face
meetings with their dissertation supervisors. (S4).
Lectures
A 20-credit lecture-module generally comprises about 40 lectures. In lectures the important points in the lecture
notes are explained and illustrated, with computer demonstrations when appropriate. The MOLE discussion board is
used to keep distance-learning students up-to-date with what has been covered and highlight any special points
made during lectures. (K1, SK1-3).
Problems
Students are required to submit work on specified problems for marking at regular intervals. (K1, SK1-3, S1).
Project and Assignment work
All modules require some practical work, requiring the integration of theory with practical skills. (S1-2).
However two modules have this aspect as their main focus. One requires the preparation of a number of
assignments designed to develop skills in statistical computing and the associated interpretation. (S1-2)
The other requires the preparation of a number of project reports based on actual 'open-ended' problems and data,
often originating from consulting activities, and with no obviously appropriate method. In addition to written reports
other presentation methods are also demanded and several of the tasks are tackled in groups; these groups involve
both distance learning and residential students, using email to discuss projects, share documents and prepare joint
presentations. (K1, SK1-2, S1-3).
Dissertation
Teaching for the dissertation is through supervision by one or more members of School staff. Students will
experience the key phases of a relatively large piece of work: planning to a deadline; researching background
information; acquisition and validation of data; problem specification; carrying out of relevant analyses; and reporting,
both at length through the dissertation and in summary through a poster display. Dissertation topics are often
provided by non-statisticians, and learning to communicate with, and relate to, these external clients is an extra
benefit of the dissertation; for distance learning students, projects based in the workplace in co-operation with an
employer are encouraged. (K1, SK1-3, S2-4).
Personal Tutorials
The Department runs a personal tutorial system conforming to the guidelines in the University’s Students’ Charter.
The system is essentially pastoral; tutors are available to provide personal support and general academic guidance.
Physical proximity
Residential students have a room with individual desks and an attached computer room. Distance learning students
share this space during residential weeks.
Opportunities to demonstrate achievement of the programme learning outcomes are provided through the
following assessment methods:
Assignments on statistical computing and the associated interpretation. K1, SK1, S1-2.
Project work associated with modules that also have an examination. K1, S1-2.
Other project work, singly and in groups, K1, SK2, S3.
Examinations, which are held in May/June, are 'open book', a format that encourages understanding rather than
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learning by rote and provides an assessment of skills that are relevant to a working environment. K1, SK1-3, S1.
Dissertation. S4 (and, as part of this, K1, SK2 and S3).
The outcome S5 is not assessed directly; it is an outcome arising from achieving all the other learning outcomes.
19. Reference points
The learning outcomes have been developed to reflect the following points of reference:
Subject Benchmark Statements
http://www.qaa.ac.uk/AssuringStandardsAndQuality/subject-guidance/Pages/Subject-benchmark-statements.aspx
University Strategic Plan
http://www.sheffield.ac.uk/strategicplan
Learning and Teaching Strategy (2011-16)
http://www.shef.ac.uk/lets/staff/lts
The research interests and scholarship of the staff.
The European Mathematical Society Mathematics Tuning Group report “Towards a common framework for
Mathematics degrees in Europe” at www.maths.soton.ac.uk/EMIS/newsletter/newsletter45.pdf pages 26-28.
The Royal Statistical Society’s accreditation framework.
Contacts with employers, mainly through the programme's Advisory Board
The University of Sheffield Students’ Charter at
http://www.shef.ac.uk/ssid/ourcommitment/charter/
The University’s coat of arms, containing the inscriptions Disce Doce (Learn and Teach) and Rerum Cognoscere
Causas (To Discover the Causes of Things; from Virgil's Georgics II, 490), at http://www.sheffield.ac.uk/about/arms
20. Programme structure and regulations
The full-time (residential) and part-time (distant learning) programmes start together with an induction week in
Sheffield in September. The full-time course is offered over 12 months, finishing in the following September. The part
time course takes 2, 3 or 4 years to complete. The components other than the dissertation must be completed within
three years.
The teaching year is divided into two semesters each of fifteen weeks. Modules giving 120 credits must be taken
during this period. The six main modules are each of 20 credits and run through both semesters. Some flexibility is
allowed in the programme by the provision of some one-semester 10-credit modules.
All students must take:
Statistical Laboratory (20 credits)
Data Analysis (20 credits)
All students must take:
Linear modelling (20 credits)
Inference (20 credits)
except when there is compelling evidence of existing competence based on previous qualifications when two 10credit modules on Special Topics may replace one of these.
All students take modules, drawn from a prescribed list, to give 40 further credits. Usually, these 40 credits are
Dependent Data (20 credits) and Sampling, Design and Medical Statistics (20 credits),
All students complete a Dissertation (60 credits).
Part-time students who take the modules (other than the dissertation) over two years normally take ‘Statistical
Laboratory’, ‘Linear Modelling’ and ‘Dependent Data’ in year 1 and ‘Data Analysis’, ‘Inference’ and ‘Sampling,
Design, Medical Statistics’ in year 2. Those who take the modules (other than the dissertation) over three years
normally take ‘Statistical Laboratory’ and ‘Linear Modelling’ in year 1, ‘Data Analysis’ and ‘Inference’ in year 2 and
‘Dependent Data’ and ‘Sampling, Design, Medical Statistics’ in year 3.
For residential students the dissertation is mainly prepared during the summer. The arrangement for part-time
students is more flexible but it is expected that they too will do most of the work during the summers or in the year
after they have completed all the other modules.
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Successful completion of the programme leads to the award of the MSc with either ‘pass’, ‘pass with merit’ or ‘pass
with distinction’ grade.
Detailed information about the structure of programmes, regulations concerning assessment and progression and
descriptions of individual modules are published in the University Calendar available on-line at.
http://www.sheffield.ac.uk/calendar
21. Student development over the course of study
The compulsory modules provide thorough training in the professional skills of tackling substantial projects and
presentation of results (Data Analysis), in practical data handling and statistical methods (Statistical Laboratory), in
the most important and pervasive classes of statistical models (Linear Modelling) and in the underlying theory and
computational tools (Inference). In particular, Statistical Laboratory introduces and develops practical skills that are
drawn on and used in all the other modules and the dissertation. Data Analysis has as its main focus the preparation
and writing of intelligible reports on practical statistical problems. In both modules the tasks, on which feedback is
given as the module develops, become more challenging through the year, as student sophistication increases. Data
Analysis is also the vehicle for general professional development, including: the use of other forms of presentation;
group working; the illustration of the interpersonal skills involved in being a statistical consultant; the consideration of
professional ethics.
The dissertation draws on the knowledge and skills aquired in the remainder of the programme.
The Postgraduate Certificate is available as a final qualification on completion of 60 credits including Statistical
Laboratory.
The Postgraduate Diploma is available as a final qualification on completion of the components other than the
dissertation.
22. Criteria for admission to the programme
The minimum entrance requirement for the course is:
either a Second Class Honours Degree, from a three or four year course at a UK university, with substantial
mathematical and statistical components; or any comparable qualification of equivalent standard. The School also
offers a Graduate Certificate which can be used as a entry qualification for the programme.
In addition, students whose first language is not English will need to demonstrate English language proficiency (even
if their education has been chiefly in English). Our usual minimum requirements are: TOEFL 232 (computer-based)
or 575 (paper-based), IELTS 6.5, or equivalent.
Detailed information regarding admission to the programme is available at http://www.shef.ac.uk/study/
23. Additional information
There is an active RSS local group that organises regular talks. These talks are accessible to and interesting for
students on this programme.
This specification represents a concise statement about the main features of the programme and should be
considered alongside other sources of information provided by the teaching department(s) and the University. In
addition to programme specific information, further information about studying at The University of Sheffield can be
accessed via our Student Services web site at www.shef.ac.uk/ssid.
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