Date of
Revision
Date of
Previous
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Programme Specification
A programme specification is required for any programme on which a student may be
registered.
All programmes of the University are subject to the University’s Quality Assurance
and Enhancement processes as set out in the DASA Policies and Procedures Manual.
Programme Title
Programme Code
Mathematics and Statistics &
Operational Research
MTH-MSCI,
MTH-UM-SOR
Criteria for Admissions
(Please see General Regulations)
UCAS Code
MSci Honours
(exit route if applicable for
Postgraduate Taught
Programmes)
JACS
Code
GGC3
Stage 1 Entry: 3 A-levels AAA (or equivalent) grade A
Mathematics
Mode of Study (Full-time, Part-time, other)
Type of Programme
Final Award
Full-time
MSci Honours – Mathematics &
Statistics & Operational Research
Length of
Programme
Total
Credits for
Programme
4 Years
Awarding Institution/Body
Queen's University Belfast
Teaching Institution
QUB, School of Mathematics and Physics
School/Department
School of Mathematics and Physics
Framework for Higher Education Qualification
Level
FHEQ Level 7
http://www.qaa.ac.uk/publications/informationand
guidance
QAA Benchmark Group
http://www.qaa.ac.uk/AssuringStandardsAndQu
ality/subject-guidance/Pages/Subjectbenchmark-statements.aspx
Mathematics, Statistics and Operational Research
Collaborative Organisation and form of
Collaboration (if applicable)
Accreditations (PSRB)
ATAS Clearance
Institute of
Mathematics
Date of next scheduled
accreditation visit
2018
480
External Examiner Name:
External Examiner Institution/Organisation
Professor D A Jordan (Pure Maths)
University of Sheffield
Professor J Fyodorov (Applied Maths)
Queen Mary, University of London
Dr G Taylor (Statistics & Operational Research)
University of Bath
Does the Programme have any
approved exemptions from the
University General Regulations
Yes
(Please see General Regulations)
Programme Specific Regulations
□
No
X
(If yes, please state here any exemptions to regulations which have been
approved for this programme)
Examinations
Candidates who have completed an MSci Pathway to the
satisfaction of the examiners shall be placed in one of two honours
classes, first and second, the second class being in two divisions.
When calculating the honours classification the following module
weightings are used Stage
Stage
Stage
Stage
1
2
3
4
5%
15%
30%
5%
Students at the end of Stage 4 who do not achieve a 2.2 overall
standard may be awarded a BSc degree. The final degree
classification is calculated as follows.
Stage 1 Stage 2 Stage 3
10%
30%
60%
Transfer to Other Pathways
At any time, normally up to the end of Stage 2, students may
transfer to the BSc Pathway in Mathematics and Statistics &
Operational Research Students may transfer to other Pathways
(BSc, or if they have achieved a weighted average of at least 55%,
MSci), provided they have passed all the compulsory modules on
the Pathway to which they are transferring up to that time of
transfer.
Students with protected characteristics
Progression
Stage 1
Students will normally take six modules (or their equivalent) at Level
1 or above. Students must have passed at least five Stage 1
modules in order to progress to Stage 2.
Stage 2
Students will normally take six modules (or their equivalent) at Level
2 or above. In order to progress to Stage 3, students must have
passed at least five Stage 2 modules, and all six Stage 1 modules,
and have achieved an overall average at Stage 2 of at least 55%.
Students with an overall average lower than 55% will be required to
transfer to the BSc degree.
Stage 3
Students will normally take six modules (or their equivalent) at Level
3 or above. Students whose overall average, based on 25% of
Stage 2 and 75% of Stage 3 marks, is less than 55% will be
required to transfer to the BSc Honours degree.
.
Are students subject to Fitness to
Practise Regulations
(Please see General Regulations)
Please indicate Yes/No
Length of Programme
4 YEARS
Fitness to Practise programmes are those which permit students to
enter a profession which is itself subject to Fitness to Practise rules
Educational Aims of Programme On completion of the programme the student will be able to:
To provide a high quality, research informed, mathematical education for students, which provides opportunities for them to realise their mathematical potential to the highest
possible extent within the resources available to them for study. To equip them with the necessary base from which to embark on a research degree in mathematical subjects
(including Theoretical and Computational Physics), which provides them with opportunities to test their aptitude for, and interest in, research. To provide opportunities for a
balanced and coherent education in Mathematics and Statistics & OR, while retaining students' right to choose their modules flexibly according to their aptitudes and interests.
To convey the elegance and usefulness of Mathematics and Statistics & OR. To develop students' knowledge and skills base in ways which, inter alia, will enhance their
employment opportunities and enable them to make a valuable contribution to society. To develop students' power of critical analysis. To enhance students' skills in solving
problems from a variety of contexts, using both analytic and computational methods. To develop the skill of communicating mathematics and mathematical results to others. To
develop students' ability to function professionally as statisticians after graduation.
Learning Outcomes: Cognitive Skills
On the completion of this course successful students will have
Teaching/Learning Methods and Strategies
Methods of Assessment
developed their ability to:
think logically;
By its nature, mathematics has to be presented
The assessment of these skills is implicit
logically. The lectures provide exemplars of this
in all forms of assessment, but is not
process, as do the model answers for the
explicitly measured. The overall degree
analyse problems and situations;
assignments. Applications of theory are
of success achieved by each student
discussed in lectures and in problems classes or
reflects the extent to which these skills
choose the appropriate mathematics or statistics needed for the
tutorials, in a manner, which brings out the need
have been acquired.
solution of those problems;
to call upon a range of mathematical and
carry out structured organisation of their work;
statistical skills in order to solve a problem. The
use of targeted assignments requires students to
learn independently, under guidance;
organise their work, sometimes collaboratively
but mostly independently.
work with other students towards a common goal.
Learning Outcomes: Transferable Skills
On the completion of this course successful students will have
developed:
skills of analytic thinking and critical analysis;
organisational skills and time management;
presentational skills, in both written and oral form, of mathematical,
statistical, graphical and tabular material;
the ability to work independently;
the ability to meet deadlines.
Teaching/Learning Methods and Strategies
Methods of Assessment
Analytic thinking and critical analysis permeate
any study of the mathematical sciences and
therefore all forms of assessment.
Students will only be successful if they plan their
own timetables of work, outside formal classes,
to maintain a balance between their different
modules and between study and other pursuits.
Much of their work is done individually, though in
one project-based module, team working is
encouraged and assessed.
All students make a series of oral
presentations of their project work; the
final one, lasting for 30 minutes, is
assessed and contributes 20% of the
total project mark. Individual feedback on
the earlier presentations is provided to
give guidance on how to make
improvements. Most of the assessment,
in examinations as in dissertations, is
based on students’ written presentation.
Feedback on assignment submission is
designed partly to enhance the students’
skills in this area.
Learning Outcomes: Knowledge and Understanding
On the completion of this course successful students will have
developed knowledge and understanding of:
basic methods and techniques of calculus and analysis, algebra, vector
methods, numerical methods, basic probability, statistical and
operational research methods;
the use of these basic techniques in areas of application, such as
classical mechanics, fluid mechanics, numerical analysis, statistical
inference, operational research;
the importance and development of mathematical rigour, in providing
secure proofs of mathematical statements;
a selection of more specialist optional topics to include areas of
statistics and operational research, as well as pure mathematics and
applied mathematics
particular areas of pure mathematics, applied mathematics, statistics or
operational research which would bring the students to the point from
which they can embark on research.
Learning Outcomes: Subject Specific Skills
On the completion of this course successful students will have
developed
a broad range of subject-specific skills in statistics and operational
research and in at least one of pure mathematics or applied
mathematics;
a high level of numeracy;
their ability to construct rigorous mathematical proofs;
an ability to construct computer programs in languages such as
MATLAB, MATHEMATICA or FORTRAN to aid the solution of
mathematically based problems;
an ability to use statistical packages such as SAS;
their ability to formulate situations in mathematical or statistical terms,
and to express the solutions in mathematical or statistical problems in
the context in which problems were originally posed;
an awareness of ways in which mathematics, statistics and operational
research are of importance in the world of work;
their ability to undertake a small research project.
Teaching/Learning Methods and Strategies
Methods of Assessment
Lectures constitute the foundation for the
presentation of the knowledge and
understanding required of successful students.
These are augmented by a range of measures –
tutorials, problems classes, practical classes –
as appropriate.
Assignments, comprising sets of questions
relevant to the material recently covered in
lectures, and normally set at weekly intervals,
form the major vehicle for a student’s learning of
the various areas of mathematics. Assignments
submitted are marked within one week and
returned to the students to provide individual
feedback on progress.
For the most part, the assignments do
not contribute to the assessment: they
are part of the learning process rather
than the assessment process.
Assessment is mainly through formal
examinations, either at the end of each
module or in class tests held during the
module. In some modules, practical work
is assessed. In the context of project
work, knowledge and understanding are
assessed through the write-up or
dissertation.
Teaching/Learning Methods and Strategies
Methods of Assessment
Mathematical skills are acquired through doing
and applying mathematics. While lectures
provide a basis for this process, it is the
undertaking of the weekly assignments, which is
the key vehicle for developing a breadth and
depth of mathematical ability. Confidence is
thereby engendered, and this is enhanced
through discussion in tutorials and problems
classes. Practical classes develop skills in the
use of mathematical and statistical software and
the solution of problems for which an analytic
approach does not lead to a full solution.
One third of the final year’s work
comprises a research project in statistics
and/or operational research, individually
supervised by a researcher from the
academic staff.
We link closely with the University
Careers Service who provide lectures
and workshops involving employers of
mathematicians and statisticians.
Assessment is through formal
examinations, practical assignments and
project dissertations..
Programme Requirements
Module Title
Module
Code
Level/
stage
Credits
Availability
S1
Duration
Pre-requisite
S2
Assessment
Core
Option
Coursework %
Examination %
10
90
At Stage 1 Students are required to take six compulsory modules.
Vector Algebra & Dynamics
AMA1001
I
20
12 Weeks
A-level Maths B
Numbers, Sets and
Sequences
PMA1012
I
20
12 Weeks
A-level Maths B
Introduction to Probability
and Operational Research
SOR1001
I
20
12 Weeks
A level Maths B
Waves and Vector Fields
AMA1002
I
20
12 Weeks
AMA1001 (corequisite)
Analysis and Linear Algebra
PMA1014
I
20
12 Weeks
Statistical Methods
SOR1002
I
20
12 Weeks
A-level Maths B
PMA1012 (corequisite)
SOR1001 (corequisite)
100
10
90
20
80
100
10
90
Module Title
Module
Code
Level/
stage
Availability
S1
Duration
Pre-requisite
S2
Assessment
Core
Option
Coursework %
Examination %
At Stage 2 Students are required to take an approved combination of six modules from those available in Applied mathematics, Pure Mathematics, Statistics & OR, to include
SOR2002 and at least one of SOR2003 and SOR2004.
A student intending to take more than one module of Applied Mathematics at Stage 3 must pass the examination in at least two of the AMA modules.
A student intending to take more than one module of Pure Mathematics at Stage 3 must pass the examination in at least two of PMA2002, PMA2008, PMA2007.
Normally no student shall be permitted to take more than two of AMA2003, PMA2003 and PMA2007
AMA2001
AMA1001 and
Classical Mechanics
20
12 Weeks
II
AMA1002
Methods of Applied
Mathematics
AMA2003
II
20
12 Weeks
None
Complex Variables
PMA2003
II
20
12 Weeks
PMA1014
Linear Algebra
PMA2007
II
20
12 Weeks
PMA1012 and
PMA1014
Elementary Number Theory
PMA2010
II
20
12 Weeks
None
Statistical Inference
SOR2002
II
20
12 Weeks
SOR1002
Numerical Analysis
AMA2004
II
20
12 Weeks
None
Fluid Mechanics
AMA2005
II
20
12 Weeks
AMA1002
Analysis
PMA2002
II
20
12 Weeks
PMA1014
Group Theory
PMA2008
II
20
12 Weeks
PMA1012 and
PMA1014
Geometry
PMA2009
II
20
12 Weeks
None
Methods of Operational
Research
SOR2003
II
20
12 Weeks
SOR1001
Linear Models
SOR2004
II
20
12 Weeks
SOR2002 (corequisite)
100
100
100
100
100
30
70
40
60
100
100
100
10
90
100
20
80
Module Title
Module
Code
Level/
stage
Availability
S1
Duration
Pre-requisite
S2
Assessment
Core
Option
Coursework %
Examination %
At Stage 3 Students must take an approved combination of six modules from those available at Level 3 in Applied Mathematics, Pure Mathematics and Statistics & OR, to
include EITHER AMA4020 OR PMA4013; at least two modules in Pure Mathematics OR the equivalent of at least two full modules in Applied Mathematics; and the equivalent
of at least two full modules from those with SOR codes given in the list below. No Project module may be chosen.
AMA3001
None
Electromagnetic Theory
20
12 Weeks
III
100
Quantum Theory
AMA3002
III
20
12 Weeks
None
Advanced Numerical
Analysis
Partial Differential Equations
AMA3004
III
20
12 Weeks
AMA2004
AMA3006
III
20
12 Weeks
None
Computer Algebra
PMA3008
III
20
12 Weeks
None
Ring Theory
PMA3012
III
20
12 Weeks
PMA2007
Set Theory
PMA3014
III
20
12 Weeks
PMA2007
Convergence
PMA3016
III
20
12 Weeks
PMA2002 (or
PMA2003
100
Linear & Dynamic
Programming
Stochastic Processes
SOR3001
III
20
12 Weeks
AMA1001 or
PMA1012
100
SOR3012
III
20
12 Weeks
SOR1001
Statistical Data Mining
SOR3008
III
20
12 Weeks
SOR2004
Tensor Field Theory
AMA3003
III
20
12 Weeks
None
Financial Mathematics
AMA3007
III
20
12 Weeks
None
Calculus of Variations &
Hamiltonian Mechanics
Mathematical modelling in
biology and medicine
Investigations
AMA3013
III
20
12 Weeks
None
AMA3014
III
20
12 Weeks
None
AMA4020
III
20
12 Weeks
None
Mathematical Investigations
PMA4013
III
20
12 Weeks
None
Metric and Normed Spaces
PMA3017
III
20
12 Weeks
PMA2002
Algebraic Equations
PMA3018
III
20
12 Weeks
PMA2008 & either
PMA2007 or
AMA2003
100
30
70
100
100
100
100
100
15
85
100
100
100
100
100
100
100
Module Title
Module
Code
Level/
stage
Availability
S1
Duration
Pre-requisite
S2
Assessment
Core
Option
Coursework %
Examination %
Stage 4 Students must take SOR4001 and four other modules from Applied Mathematics, Pure Mathematics, Statistics & OR, comprising EITHER four modules form Level 4
OR three modules from Level 4 plus one not previously taken from Level 3 (other than a Level 3 Project).
Advanced Quantum Theory
AMA4001
IV
20
12 Weeks
AMA3002 or
PHY3011
Practical Methods for Partial
Differential Equations
AMA4006
IV
20
12 Weeks
None
Functional Analysis
PMA4002
IV
20
12 Weeks
PMA3017 and
PMA3014
Topology
PMA4003
IV
20
12 Weeks
PMA3017 and
PMA3014
Information Theory
AMA4009
IV
20
12 Weeks
None
Mathematical Methods for
Quantum Information
Processing
Integration Theory
AMA4021
IV
20
12 Weeks
AMA2003 or
PMA2007
PMA4004
IV
20
12 Weeks
PMA2002 and
PMA3014
Statistical Mechanics
AMA4004
IV
20
12 Weeks
AMA3002 or
PHY3011
Advanced Mathematical
Methods
AMA4003
IV
20
12 Weeks
None
Rings and Modules
PMA4008
IV
20
12 Weeks
PMA3012
Algebraic Topology
PMA4010
IV
20
12 Weeks
PMA4003 (corequisite)
Survival Analysis
SOR4007
IV
20
12 Weeks
SOR2004
Statistics Project
SOR4001
IV
40
24 Weeks
None
100
25
75
100
100
100
100
100
100
100
100
100
15
85
20
80
Approved by Director of Education:
Print Name: ……………………………………………………..
Signature: …………………………………………
Date: ……………………………..