Programme Specification
A statement of the knowledge, understanding and skills that underpin a
taught programme of study leading to an award from
The University of Sheffield
1
Programme Title
Statistics with Financial Mathematics
2
Programme Code
MAST11, MAST12
3
JACS Code
G300
4
Level of Study
Postgraduate
5a
Final Qualification
MSc in Statistics with Financial Mathematics (MSc)
5b
Position in the QAA Framework for
Higher Education Qualifications
Masters
6a
Intermediate Qualification(s)
Postgraduate Diploma (PG Dip), Postgraduate Certificate (PG
Cert)
6b
Position in the QAA Framework for
Higher Education Qualifications
Masters
7
Teaching Institution (if not Sheffield)
Not applicable
8
Faculty
Science
9
Department
School of Mathematics & Statistics
10
Other Department(s) involved in
teaching the programme
None
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Mode(s) of Attendance
Full-time (MAST11), Part-time (MAST12)
12
Duration of the Programme
1 year (MAST11), 2-4 years (MAST12)
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Accrediting Professional or Statutory
Body
Royal Statistical Society
14
Date of production/revision
January 2008, revised March 2012, March 2016
15. Background to the programme and subject area
The UK’s statistical tradition, in which empirical and theoretical work continually meet and strengthen each
other, has long been recognised as among the best in the world. The Probability & Statistics group stands firmly
within this tradition, both in its teaching and its research. In recent years a new area of application of
probabilistic, statistical and mathematical techniques has emerged in finance, leading to rapid advances in
optimal investment, risk management and the pricing of options and derivatives. The new area has seen major
development, much of it in the UK, stimulated by the needs of the country’s financial services industry, which is
of global as well as national importance. There is a substantial demand for high-quality postgraduate training in
this area, including demand for such training in part-time distance-learning form.
The MSc in Statistics with Financial Mathematics provides both a practically-based professional training
combining statistics and financial mathematics, and a foundation for those wishing to pursue further research. It
is available via distance-learning (2-4 years, part-time) as well as by residential study (1 year full-time). The
programme is a development of that leading to the MSc in Statistics, which has been running successfully for
many years. It builds on the provision of a firm grounding in practical statistical methodology and computation,
including the development of the personal skills in demand by employers, from the established Statistics MSc
programme, and adds to them development of an understanding of, and ability to apply, the concepts, models
and tools of modern mathematical finance. It provides an excellent foundation for a career in financial areas, or
for further study for a research degree.
The established Statistics MSc programme has been supported by national 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.The programme is kept in close
touch with the needs of employers through the programme's Advisory Board, whose members are drawn from
industry, commerce and government. Students benefit from contacts with members of the Board, from meetings
with employers through open days, from career presentations and through work on dissertation projects arising
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from commerce and industry. 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 confident that the training offered by the 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
16. Programme aims
In the context of this programme the School aims:
(a) to provide a high-quality thorough initial training for professional statisticians with a strong interest in
quantitative finance;
(b) to provide an intellectual environment conducive to learning;
(c) to prepare students for careers which use their statistical and financial-mathematical 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:
Candidates for MSc, PG Dip and PG Cert will:
K1
be able to demonstrate a reasonable understanding of the relevant body of knowledge.
K2
be able to formulate straightforward problems in statistical and financial-mathematical terms and analyse
data using a range of standard techniques.
In addition, candidates for MSc and PG Dip will:
K3
be able to formulate problems in statistical and financial-mathematical terms, plan studies and analyse
data using a range of standard techniques.
In addition, candidates for MSc will:
K4
be able to formulate problems in statistical and financial-mathematical terms; to plan studies; and to
select, adapt and apply techniques to suit the needs of data analysis and modelling.
Skills and other attributes:
Candidates for MSc, PG Dip and PG Cert will:
S1
have ability in using at least one major statistical computer package, and general computer skills.
S2
be able to conduct short statistical and financial-mathematical studies and have some experience of
preparing longer reports.
In addition, candidates for MSc and PG Dip will:
S3
have skill in the preparation and writing of longer reports (both technical and non-technical), in other
methods of communication of results (for example, oral presentation), and in working in groups.
In addition, candidates for MSc will:
S4
have shown the ability to complete an extended individual study of a statistical or financial-mathematical
problem and to present the results in a dissertation.
S5
have skills in mathematical and financial literacy and attitudes and confidence which will allow them to
acquire new statistical and financial-mathematical knowledge throughout a subsequent career.
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18. Teaching, learning and assessment
Development of the learning outcomes is promoted through the following teaching and learning
methods:
MAST11 is a full-time residential programme, with lectures, discussion sessions and computer laboratory work.
MAST12 is a part-time distance learning programme. The two programmes are as closely integrated as possible
within the constraints of their distinct identities. The distance learning version is designed so that students study
the same subjects as their residential counterparts at essentially the same times.
MOLE
The programme materials are made available through My Online Learning Environment (MOLE) via the world
wide web, and support for distant students is available from lecturers of individual modules, from a dedicated
personal tutor and from the programme's Course Director via email or telephone. Most communication within the
programme, particularly between residential and distance-learning students, takes place via MOLE and so
training in its use is given early in the programme.
For all modules except the project-based Data Analysis module, 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. (K1-4, S1-3).
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 packages used in the programme of study. They are also introduced to MOLE and its central
role is explained. Basic, underpinning, theoretical material is reviewed. (K2, S1).
Other residential weeks are held at the time of the examinations. Examinations take place towards the start of
the week and the later part of the week is used for group work and presentations. (S2-S3).
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. Full printed lecture notes are made available.
The lectures themselves are used to explain and illustrate the most important points in the notes, with computer
demonstrations when appropriate. The MOLE discussion board is used to keep distance-learning students upto-date with what has been covered, and to highlight special points made during lectures. (K1-K4).
Problems
Students are required to submit work on specified problems for marking at regular intervals. (K1-K4,S1).
Project and Assignment work
All modules require some practical work, designed to foster 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 real problems and data, often
originating from consulting activities, and for which a variety of approaches are likely to be illuminating. In
addition to gaining experience in the writing of reports, students gain experience in the use of other methods of
communication (seminar-style presentations, round table discussions, rôle-play) and in working with others on
larger projects in small groups. Groups involve both distance learning and residential students. Group members
use email to collaborate, share documents, reach decisions and prepare joint presentations. (K1-K3, S1-3).
Dissertation
Teaching for the dissertation is through individual supervision for each student 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 through
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 or non-mathematicians, and learning to
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communicate with, and relate to, external clients expert in other disciplines 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-K4, 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 learning outcomes are provided through the following
assessment methods:
Assignments on statistical computing and the associated interpretation. K1-K2, S1-2.
Project work associated with modules that also have an examination. K1, S1-2.
Other project work, singly and in groups, K1, K3, S3.
Examinations, which are held in May/June, are in a format that encourages understanding rather than learning
by rote and provides an assessment of skills that are relevant to a working environment. K1-K4, S1.
Dissertation. S4 (and, as part of this, K1, K3 and S3), S5.
The outcome S5 is assessed through the dissertation and, indirectly, through 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-benchmarkstatements.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.
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All students must take:
MAS6051 Introductory Mathematical Finance & Time Series (20 credits)
MAS6052 Stochastic Processes and Finance (20 credits)
MAS6001 Data Analysis (20 credits)
MAS6002 Statistical Laboratory (20 credits)
All students take further modules:
MAS6003 Linear Modelling (20 credits)
MAS6004 Inference (20 credits)
except when there is compelling evidence of existing competence based on previous qualifications. In this case
two 10-credit modules on Special Topics may replace one of these.
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’, ‘Introductory Mathematical Finance & Time Series’ and ‘Linear Modelling’ in year 1 and ‘Data
Analysis’, ‘Stochastic Processes and Finance’ and ‘Inference’ in year 2. Those who take the modules (other
than the dissertation) over three years take ‘Statistical Laboratory’ and ‘Linear Modelling’ in year 1, ‘Data
Analysis’ and ‘Introductory Mathematical Finance & Time Series’ in year 2 and ‘Stochastic Processes and
Finance’ and ‘Inference’ in year 3.
Residential students begin work on the dissertation in early Spring, but work on it most intensively during the
Summer. The arrangement for part-time students is more flexible, but they too are expected to do most of the
work during the summers or in the year after they have completed all the other modules.
Successful completion of the programme leads to the award of the MSc with either ‘pass’, ‘pass with merit’ or
‘pass with distinction’ grade.
The PG Diploma is available for candidates who take all of the taught part of the MSc but not the dissertation.
The PG Certificate is available for candidates who take only a sub-set of the modules and do not undertake the
dissertation.
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 basic ideas of modern financial mathematics and time
series modelling (Introductory Mathematical Finance & Time Series), in advanced ideas from probability theory
and their application to financial markets and the pricing of derivatives (Stochastic Processes and Finance), in
the professional skills required to tackle substantial statistical projects and communicate results (Data Analysis),
and in practical data handling and statistical methods (Statistical Laboratory). In particular, Statistical Laboratory
introduces and develops practical skills that are drawn on and used in all the other modules and the dissertation.
The focus of Data Analysis is the preparation and communication of 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 skills develop. Data Analysis is also the vehicle for general professional development,
including the opportunity to extend communication skills, to experience group working, to develop the
interpersonal skills needed in statistical consultancy, and to appreciate the ethical framework of professional
activities. Work on financial mathematics progresses from basic ideas founded on partial differential equations in
Introductory Mathematical Finance to the development of more advanced stochastic treatments using
martingales and diffusions and their applications in pricing and portfolio optimization in Stochastic Processes
and Finance.
The dissertation draws on and extends the knowledge and skills acquired in other parts of the programme, and
promotes the development of independent and reflective modes of study.
22. Criteria for admission to the programme
Detailed information regarding admission to the programme is available at http://www.shef.ac.uk/study/
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23. Additional information
There is an active local group of the Royal Statistical Society in Sheffield which organises a series of meetings
through the year featuring visiting national speakers. The 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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