PROGRAMME SPECIFICATION
PROGRAMME SPECIFICATION
Programme title:
Mathematics and Statistical Science
Final award (BSc, MA etc):
MSci
(where stopping off points exist they should be
detailed here and defined later in the document)
UCAS code:
GG1K
(where applicable)
Cohort(s) to which this programme
specification is applicable:
Intakes from 2000
(e.g. from 2015 intake onwards)
Awarding institution/body:
University College London
Teaching institution:
University College London
Faculty:
Mathematical and Physical Sciences
Parent Department:
Mathematics
(the department responsible for the administration of
the programme)
Departmental web page address:
http://www.ucl.ac.uk/maths/
(if applicable)
Method of study:
Full/part time
Full-time/Part-time/Other
Criteria for admission to the
programme:
See:
http://www.ucl.ac.uk/maths/prospective-students/undergraduates
Length of the programme:
4 years full time. 1 of the 3 years spent abroad is optional.
(please note any periods spent away from UCL, such
as study abroad or placements in industry)
Level on Framework for Higher
Education Qualifications (FHEQ)
(see Guidance notes)
Relevant subject benchmark statement
(SBS)
Masters Level (Level 7)
Mathematics
(see Guidance notes)
Brief outline of the structure of the
programme
and
its
assessment
methods:
See: http://www.ucl.ac.uk/maths/prospectivestudents/undergraduates/degree-programmes
(see guidance notes)
Board of Examiners:
Professional body accreditation
(if applicable):
Name of Board of Examiners:
Mathematics
Date of next scheduled
accreditation visit:
EDUCATIONAL AIMS OF THE PROGRAMME:
To provide a four year degree programme for undergraduate students which is intellectually challenging and
rigorous as well as providing a qualification that will enable graduates to be well-placed to continue study for a
postgraduate qualification or to enter employment.
PROGRAMME OUTCOMES:
The programme provides opportunities for students to develop and demonstrate knowledge and understanding,
qualities, skills and other attributes in the following areas:
A: Knowledge and understanding
Knowledge and understanding of:
Teaching/learning methods and strategies:
1. Core topics in various branches of
advanced pure and applied
mathematics, together with statistical
science.
Lectures, problem classes, tutorials and private study.
2. A range of optional advanced topics
in courses which are informed by the
scholarship and/or research interests
of the staff.
3. The application of critical and
analytical reasoning and the
presentation of logical and concise
arguments.
Assessment:
Written unseen examinations for all courses apart from
the totally computer-based courses that have project
based examinations. There is also an additional
coursework and/or project component to the
assessment for all core courses and most optional
courses.
B: Skills and other attributes
Intellectual (thinking) skills:
Teaching/learning methods and strategies:
1. Understanding sophisticated
mathematical arguments and rigorous
proofs.
2. Comprehension of high levels of
abstraction in pure mathematics.
Lectures, problem classes, tutorials and projects where
appropriate.
Assessment:
See above.
C: Skills and other attributes
Practical skills (able to):
Teaching/learning methods and strategies:
1. The assimilation and manipulation of
substantial bodies of knowledge
Written presentations of solutions to problems set as
coursework and/or projects.
2. Apply physical insight and
mathematical techniques to the
solution of problems in applying the
techniques of probability and
statistics.
The accumulation of material necessary to write a report
on a project.
Written work produced under examination conditions.
3. Develop investigative skills required
for problem solving.
Assessment:
See above.
D: Skills and other attributes
Transferable skills (able to):
Teaching/learning methods and strategies:
1. Structure and communicate ideas
effectively.
2. Manage time and work to deadlines.
3. Work independently or within a group.
4. Develop self-confidence and reliance.
5. Use information technology and
retrieval systems in acquiring
investigative skills.
6. Assess the relevance and importance
of ideas and develop the ability to
identify the significant aspects in a
problem that is necessary in
mathematical modelling.
Courses with coursework and/or project work introduce
information that needs to be assessed critically.
Training in the presentation of logical and precise
arguments.
Contribute to Peer Assisted Learning programmes.
Encouragement to participate effectively in discussion
groups such as tutorials.
Assessment:
See above.
The following reference points were used in designing the programme:
 the Framework for Higher Education Qualifications
(http://www.qaa.ac.uk/en/Publications/Documents/Framework-Higher-Education-Qualifications-08.pdf);
 the relevant Subject Benchmark Statements (http://www.qaa.ac.uk/assuring-standards-and-quality/the-qualitycode/subject-benchmark-statements);
 the programme specifications for UCL degree programmes in relevant subjects (where applicable);
 UCL teaching and learning policies;
 staff research.
Please note: This specification provides a concise summary of the main features of the programme and the
learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes
full advantage of the learning opportunities that are provided. More detailed information on the learning outcomes,
content and teaching, learning and assessment methods of each course unit/module can be found in the
departmental course handbook. The accuracy of the information contained in this document is reviewed annually
by UCL and may be checked by the Quality Assurance Agency.
Programme Organiser(s)
Name(s):
Dr M L Roberts
Date of Production:
August 2003
Date of Review:
November 2014
Date approved by Head of
Department:
November 2014
Date approved by Chair of
Departmental Teaching
Committee:
Date approved by Faculty
Teaching Committee
November 2014
February 2015
APPENDIX
The Mathematics and Statistical Science MSci degree programme structure is summarised in the following
Table.
Year
1
Core Mathematics MATH1101 Analysis 1*
MATH1102 Analysis 2*
MATH1201 Algebra 1*
MATH1401
Mathematical Methods
1*
MATH1402
Mathematical Methods
2*
2
3
MATH2101 Analysis
3: Complex Analysis*
MATH7102 Analysis
4: Real Analysis*
MATH1202 Algebra
2*
MATH3101 Real
Analysis*
MATH2201 Algebra
3: Further Linear
Algebra*
Mathematics
Options
Core Statistical
Science
Statistical Science
Options
At least 0.5 unit from
designated maths
options (possibly
plus further maths
options)
STAT1004 Introduction
to Probability and
Statistics *
STAT1005 Further
probability and
Statistics*
STAT1006 Introduction
to Practical Statistics*
At least 1 unit of 4th
level modules
(possibly plus other
maths options)
STAT2001
STAT3001 Statistical
Probability and
Inference *
Inference*
STAT2003
Introduction to
Applied Probability*
STAT2002 Linear
Models & Analysis of
Variance*
STAT7001
Computing for
Practical Statistics *
One from
STAT7002 Social
Statistics
STAT7003
Optimization
Algorithms in
Operations Research
Mathematics
and/or Statistical
Science
Outside Options
4
At least one unit from
designated statistics
options (possibly
plus further stats
options)
At least 1 unit of 4th
level modules
(possibly plus other
stats options)
Project (1 unit) *
At most ½ unit
At most ½ unit
Normally at most a half-unit of ‘outside’ options may be taken in Years 3 and 4, subject to the
approval of the appropriate departmental tutors and the constraints of the timetable. In Year 4, there
is a free choice of the 4th level courses offered by the two departments, apart from the restriction of
at least one unit to be studied in either department and the compulsory one unit of project
supervised in Mathematics and/or Statistical Science.