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: A. NATURE OF THE AWARD
Awarding Institution: Kingston University
Programme Accredited by: Institute of Actuaries and Faculty of Actuaries
Final Award(s): BSc (Hons)
Intermediate Award(s): Dip Cert E; Dip H E
Field Title: Actuarial Mathematics & Statistics
FHEQ Level for the final award: Honours
Credit rating by level: 120 per level
JACs code: G100
QAA Benchmark Statement(s):
http://www.qaa.ac.uk/academicinfrastructure/benchmark/honours/default.asp
Minimum/Maximum Period of Registration: 3 years/9 years
Faculty: Computing, Information Systems and Mathematics
School: N/A
Location: Penrhyn Road
Date Specification Produced: March 2006
B. FEATURES OF THE FIELD
1. Title:
The field is available in the following forms:
BSc (Hons) Actuarial Mathematics and Statistics
2. Modes of Delivery
The field is offered in the following alternative patterns:
- Full time
- Part time
Actuarial Mathematics and Statistics is offered as a three year full time course, although it is possible for students to switch between full time and part time mode attendance. A sandwich option is available (and strongly recommended).
3. Features of the Field
The Faculty of CISM offers a full course within the Undergraduate Modular Scheme (UMS).
Actuaries provide commercial, financial and prudential advice on the management of assets and liabilities - especially where long term management and planning are critical factors. Actuaries work in HYPERLINK "http://www.actuaries.org.uk/Display_Page.cgi?url=/board_area/finance.html" Finance, investment & risk management - HYPERLINK "http://www.actuaries.org.uk/Display_Page.cgi?url=/board_area/general.html" General insurance - HYPERLINK "http://www.actuaries.org.uk/Display_Page.cgi?url=/board_area/life.html" Life insurance - HYPERLINK "http://www.actuaries.org.uk/Display_Page.cgi?url=/board_area/pensions.html" Pensions Simply put, they use their mathematical expertise and statistical knowledge to help financial institutions and governments evaluate the long-term financial implications of their decisions. Traditionally, actuaries have worked in the insurance and pensions industries, but increasingly they are playing a valuable role in areas such as healthcare, banking, business management and risk assessment. Actuaries also help the Government to formulate public policies
EDUCATIONAL AIMS OF THE FIELD
The course will cover the fundamental mathematical and statistical methods required students interested in solving practical problems related to the Actuarial Profession, together with the development of the necessary computing and analytical skills. The course will constitute a coherent, academically sound programme of study which will assist students in their general personal development and produce graduates suited for employment in careers associated with the Insurance, Financial and Actuarial professions where mathematical, statistical, computing and financial economics skills are used, or to go onto postgraduate studies. Embedded within the provision is the opportunity for the development of a range of key skills.
There will be main strands to the core of the course from Mathematics, Statistics, Computing and Financial Economics, these being:
the study of differential equations;
use of mathematical and numerical methods;
basic probability theory and distribution theory.
data analysis, including the use of statistical computer packages
fundamental financial economics theory and practice and accounting principles
the study of financial models and techniques for the banking and insurance industry
Additionally, a limited choice of option modules enables the students to direct their programs of study towards personal interests. In particular the optional project in the second semester of the final year encourages the student to pursue an in depth study related to the Profession and will be particularly useful for students returning after a years work placement
The Actuarial Mathematics and Statistics course aims will be to develop students abilities to:
a) attain a body of knowledge and skills in order to understand the basic principles and methods of the subject and the ability to apply them to a range of problems in mathematics, statistics, financial economics and to the problems of assessing mortality risks and insurance;
b) identify relationships between the various subject areas they have studied;
c) seek, use and communicate relevant information effectively in oral, visual and written forms;
d) work in groups and individually;
e) extend their knowledge in the relevant subject areas by further formal study (for academic or professional qualifications) or by effective use of published work.
Specific aims for each module within the field are given in the module descriptions.
D. LEARNING OUTCOMES (OBJECTIVES) OF THE FIELD
The learning outcomes of the Actuarial Mathematics and Statistics course are to produce graduates who are able to:
1. Knowledge and Understanding
demonstrate an appropriate mastery of theory and techniques of the subject and are able to apply them to a variety of problems;
2. Cognitive (thinking) Skills
formulate problem solutions;
identify appropriate techniques based on theory and practice in Accounting, Economics, Mathematics, Statistics or Computing;
demonstrate research skills.
3. Practical Skills
All students develop practical skills to solve practical problems associated with the Financial, Banking and Insurance industry being aware of relevant computer software to assist in the solution of practical problems;.
4. Key Skills
On completion of the field students will have acquired transferable skills to:
a. Communication Skills
receive and respond to a variety of information e.g. taking part in discussions; selecting, extracting and collating information from appropriate sources; presenting information in a variety of formats/media;
b. Numeracy
apply numerical skills and techniques to quantitative situations e.g. collecting data (where appropriate); evaluating quantitative data; performing basic calculations;
c. Information, Communication and Technology
make effective use of computer systems to aid data manipulation and presentation e.g. presenting different forms of information; searching for and storing information; on-line communication;
d. Teamwork
work effectively as a member of a team, appreciating the value of their own and others contributions;
e. Independent Learning
display self management and organisation leading to attainment of objectives within timelines and personal development e.g. developing research and information handling skills; developing self awareness; monitoring and reviewing own progress.
Table 1 below identifies the key skills associated with summative assessment components for core modules and options from the subject areas of Mathematics, Statistics and Computing. It should be recognised that, in addition, students will be developing these skills extensively away from these summative assessment exercises in classes, in formative assessment exercises, in private study and in extra-curricula activities. All Modules shown are core to the course.
The learning and teaching strategies of the course seek to ensure that students learn actively and effectively, thus laying the foundation for future careers, particularly as Professional Actuaries and/or further study.
CommunicationNumeracyICTTeamworkIndependent LearningLevel 1MA1010Mathematical Science IC, GC, G, T, EGG, EMA1020Mathematical Science IICC, T, ECEMA1030Introduction to Linear AlgebraC, T, ECEST1210Introduction to Probabiity and StatisticsC,T,ECECO1000Fundamental Programming Concepts I, LCI, L, TI, L, TCO1040Object-Oriented Programming with Java C, RC, RC, R, TFE1118AccountingC,RC,RCELevel 2MA2010Mathematical Methods IC, EEMA2020Ordinary Differential EquationsC, EEMA2401Actuarial Methods ,Planning and ControlCCCMA2402ContingenciesCC,EC,EST2220Statistical DistributionsCC, EEST2210Regression ModellingG(R)G(R),T,EG(R)G(R)G(R),EST2353Operational Research TechniquesCC, ECEFE2118Corporate Finance 1C,RC,RI,L,TELevel 3MA3010Partial Differential Equations & Approximation TheoryCC, ECC, EMA3401Advanced ContingenciesCC,EEMA3444Mathematical FinanceCC,ECGEST320Time SeriesG(R)I(R), C, EI(R)I(R), C,EST3360Stochastic Modelling in FinanceC, I(R) G(R),T, EG(R)G(RG(R),EST3370Further Inference & Bayesian MethodsCC, EEFE3148Financial Risk ManagementC,RC,RI,L,TE
Table 1 - Key Skills Summary
Key:
C - Coursework Assignment,
D - Project Development,
E Examination,
I - Individual Case Study or Self-Study/Research Exercise,
G - Group Case Study or Self-Study,
L - Library Workbook,
R Report,
T - In-class Test.
E. FIELD STRUCTURE
The course structure aligns with the specifications of the UMS. The course draws on the Mathematics, Statistics, Financial Economics and Computing modules for most of its provision taken with option modules as specified in the course diagram. The modules at each level total to a credit value of 120 points.
The sandwich year is an optional element in the programme, taken between levels 2 and 3. Students who opt for the sandwich mode will spend a minimum period of 36 weeks in an approved placement in industry or commerce.
Level 1
At Level 1, students will be introduced to a wide variety of topics, laying the necessary foundation for further work in the course. The study of mathematical methods will include calculus, linear algebra, ordinary differential equations, an introduction to numerical methods and exposure to symbolic algebra and linear algebra packages. Core statistics and computing modules are included to underpin those subjects and to provide pre-requisites for later options. In the statistics module students will meet some fundamental concepts in probability and statistics, as well as be provided with the techniques, including practical tools, with which to apply these concepts to various problems. In the computing modules, notions of object-oriented programming and design are introduced, as well as emphasizing good software engineering principles. Java, which supports the object-oriented paradigm, is used as the vehicle for introducing concepts such as object and inheritance and for developing fundamental programming skills
In the first semester a significant proportion of the content of Mathematical Science I is designed to overlap selected A-level material in accordance with the aim of widening participation. No previous knowledge is assumed of Statistics or Computing.
The level 1 modules contributing to the field are:
Semester A
Module TitleCodeMathematical Science IMA1010CoreIntroduction to Probability & StatisticsST1210CoreFundamental Programming ConceptsCO1000Core
Semester B
Module TitleCodeMathematical Science IIMA1020CoreIntroduction to Linear AlgebraMA1030CoreObject Oriented Programming with JavaCO1040CoreAccountingFE1118Core
Level 2
Eight core modules will run through the course at level 2. The first semester module Mathematical Methods I will build on the Level 1 work, introducing additional techniques and providing a foundation for numerical and analytical treatments of ordinary and partial differential equations later in the course. Then, in semester 2, the study of ordinary differential equations is extended to systems in the second semester where a symbolic algebra package (MAPLE) will be used to assist qualitative understanding with graphical representations. ST2210, Regression Modeling, extends knowledge of simple linear regression to multiple linear and logistic regression and the students also learn to use the Statistics software package SAS. In ST2220, Statistical Distributions, students gain further knowledge of discrete and continuous distributions, including joint distributions and parameter estimation.
The level 2 modules contributing to the course are:
Semester A
Module TitleCodeMathematical Methods IMA2010CoreRegression ModellingST2210CoreStatistical DistributionsST2220CoreCorporate Finance 1FE2118Core
Semester B
Module TitleCodeOrdinary Differential Equations: MA2020CoreOperational Research TechniquesST2353CoreActuarial Methods, Planning and ControlMA2401CoreContingenciesMA2402Core
Level 3
The seven core modules combine advanced Mathematics, Statistics and Financial topics and prepare the students on graduation for entry into the professional field. One of the final year modules comprises an optional module to provide a degree of choice into other advanced mathematics.
The level 3 modules contributing to the field are:
Semester A
Module TitleCodePartial Differential Equations and Approximation TheoryMA3010CoreTime SeriesST3320CoreFinancial Risk ManagementFE3148CoreAdvanced ContingenciesMA3401Core
Semester B
Module TitleCodeStochastic Modelling in FinanceST3360CoreFurther Inference & Bayesian MethodsST3370CoreMathematical FinanceMA3444Core
F. FIELD REFERENCE POINTS
Actuarial Mathematics & Statistics has no benchmark statement as reported at the Quality Assurance Agency web site given in Section A. In designing the course cognizance has been taken of the relevant portions of the associated benchmark statements for Accounting, Economics, Mathematics, Statistics and Operational Research (MSOR) none of which of course apply in total to the proposed course. (MSOR being the major component of the course may be deemed most relevant).
The vocational nature of the course and its relation to the courses offered by the Institute and Faculty of Actuaries for professional accreditation has been recognized in the design of the course whereby it covers major portions of the courses CT1, CT2 .CT9 as described at www.actuaries.org.uk
The course recognises its obligation under the Special Educational Needs and Disability Act 2001 (SENDA) and acts within the University guidelines to meet these.
G. LEARNING AND TEACHING STRATEGIES
The learning and teaching strategies reflect the course aims and learning outcomes, student background, potential employer requirements and the need to develop a broad range of technical skills, with the ability to apply them appropriately. The strategies ensure that students have a sound understanding of some important areas in mathematics, statistics and financial economics and have acquired the transferable skills expected of modern-day undergraduates.
150 hours of study time is allocated to each module. Typically, this includes 55 hours of contact time per module at level 1 and 44 hours at levels 2 and 3, leaving the remainders for self-directed or guided study time. There is more contact at level 1 to provide initial academic support and students are encouraged to develop as independent learners as they progress through their degree course. Contact time can consist of lectures, tutorials, problems classes, practicals or PAL sessions, dependent on individual module requirements. From October 2004 the University declared that all first year modules should have no end of semester examination at the end of the first semester and that extra time (i.e. beyond the normal 11 teaching weeks) should be devoted to instruction to assist the students over the initial hurdle of adjusting to University life. Generally, subject material and corresponding techniques will be introduced in lectures; for many modules, practical activities are regarded as essential to the understanding of the material and the development of relevant skills. In problems classes students typically work through formative exercises under guidance and in PAL sessions second year students help those at level 1 to develop their study skills.
In Level 1 several modules have an associated study guide containing core material and formative exercises. The latter, and worksheets in computing practical sessions, help develop self-paced learning and independent study. Most higher level modules have lecture notes available; much of the material is available from the University Learning Management System, Black Board. KU Tables are produced and sold to students at a nominal fee; these give basic mathematical and statistical formulae and a number of statistical tables; these may be used in lectures, problem classes, tests or examinations.
Students will be expected to develop their skills, knowledge and understanding through independent and group learning, in the form of both guided and self-directed study. In most modules students will be given regular formative exercises or practical work through which they can develop learning skills, knowledge and techniques. Further they will have the opportunity to work individually and in groups on assignments, practicals, case studies and projects. These activities and their assessment are designed to enable students to meet the specific learning outcomes of the field.
A particularly important component of the degree is the project, which develops the students confidence and ability to carry out individual and/or group pieces of scholarship or research and then communicate their results in both written and oral forms. The project will be related to the course aims on a topic which draws on the integration of the fields studied.
H. ASSESSMENT STRATEGIES
Assessment enables students abilities to be measured in relation to the aims of the course; assessment also serves as a means for students to monitor their own progress at prescribed stages and enhance the learning process.
The assessment strategy has been devised to reflect the aims of the course and to complement the learning and teaching strategies described above. Throughout the field students are exposed to a range of assessment methods, thus allowing them to develop technical and key skills and enabling the effectiveness of the learning and teaching strategies to be evaluated.
The methods of assessment have been selected so as to be most appropriate for the nature of the subject material, teaching style and learning outcomes in each module. Some modules are assessed entirely by in-course work, while others have, in addition an end-of-module examination. No module is assessed by an end-of-module examination alone. In particular, the balance between the various assessment methods for each module reflects the specified learning outcomes.
The assessments are designed so that students achievements of the courses learning outcomes can be measured. A wide range of assessment techniques will be used to review as accurately and comprehensively as possible the students attainments in acquiring sound factual knowledge together with the appropriate technical competence and understanding, so that they can tackle various types of problems.
Components of Assessment
In the course as a whole, the following components may be used in the assessment of the various modules:
- Multiple choice or short answer in-class tests: to assess competence in basic techniques and understanding of concepts
- Long answered structured questions in coursework assignments: to assess ability to apply learned techniques to solve simple to medium problems and which may include a limited investigative component
- Long answer structured questions in end-of-module examinations: to assess overall breadth of knowledge and technical competence to provide concise and accurate solutions within restricted time
- Practical exercises: to assess students understanding and technical competence
- Individual case studies: to assess ability to understand requirements and to provide solutions to realistic problems. The outcomes can be:
- Written report, where the ability to communicate the relevant concepts, methods, results and conclusions effectively will be assessed.
- Oral presentation, where the ability to summarise accurately and communicate clearly the key points from the work in a brief presentation will be assessed.
- Poster presentation where information and results must be succinct and eye-catching.
- Group-based case studies: contain all of the assessment objectives of individual case studies and in addition to assess ability to interact and work effectively with others as a contributing member of a team
- Project: The individual project module is similar to an extended case study. The problems tackled may be of a more open-ended nature, allowing students to increase their knowledge of actuarial mathematics and statistics by studying a topic in greater depth and/or by applying techniques learned in a new situation. As such the assessment here will place a greater emphasis on ability to plan work, manage time effectively, and research background information, although students will also be expected to produce written reports and to be interviewed about their work.
In addition to any specific criteria, the following features are expected in work that is submitted for coursework assignments:
- Technical competence: the generated system or solution performs the requirements stated in the best possible implementation.
- Completeness: all aspects of the work are attempted and full explanations of all reasoning are given.
- Clarity: all explanations are clear and concise. Arguments follow a logical sequence and are laid out in a clear format.
- Neatness: all reports are produced using a word-processor. Tables, graphs and diagrams are neat and suitably labelled. Assignments with a high mathematical content may be submitted in neat handwriting.
Assessment Procedures
It is policy that in-course work is returned to students within three working weeks. Feedback can be model solutions and/or comments on the work. All examination papers, coursework and tests are internally moderated and those for levels 2 and 3 also reviewed externally by external examiners. Projects are double-marked; examination scripts are checked to ensure that all work has been marked and scores correctly totalled.
The formal assessment procedure is specified in the general regulations of the UMS.
Assessment Summary
Table 2 indicates the methods of assessment to be used in all the modules. Further details are given in the module descriptions.
Module TitleCodeTestsWritten
AssignmentsPractical/
Case StudyExaminationLevel 1Mathematical Science IMA1010***(Group)Mathematical Science IIMA1020***Introduction to Linear AlgebraMA1030***Introduction to Probability & StatisticsST1210***Fundamental Programming ConceptsCO1000***Object Orieneted Programming with JavaCO1040***AccountancyFE1118Level 2Mathematical Methods IMA2010**Ordinary Differential Equations: Analytical and Computational MethodsMA2020**Regression ModellingST2210**(Group)Statistical DistributionsST2220***Operational Research TechniquesST3353**Actuarial Methods, Planning and ControlMA2401*(Group)*ContingenciesMA2402**Corporate FinanceFE2118**Level 3Partial Differential Equations and Approximation TheoryMA3010**Advanced ContingenciesMA3401***Mathematical FinanceMA3444***Time Series & Forecasting MethodsST3320*(Group)*Stochastic Modelling in FinanceST3360***Further Inference & Bayesian MethodsST3370**Financial Risk ManagementFE3148**
Table 2 - Assessment Summary (Indicative)
I. ENTRY QUALIFICATIONS
1. The minimum entry qualifications for the field are:
The general entry requirements for the course are those applicable to all programmes within the UMS.
2. Typical entry qualifications set for entrants to the field are:
For the Actuarial Mathematics and Statistics course a minimum of 260 points, including two 6-unit awards, with an A-Level in mathematics (or its equivalent) are required.
A foundation year is available for students without formal entry qualifications. Mature applicants and those with qualifications not specified above will be considered individually.
J. CAREER OPPORTUNITIES
In addition to providing a route to studying for higher degrees, the AM&S field graduate will be equipped for employment, for example, as:
Commercial, industrial and public sector managers
Business and finance associate professionals
Actuaries
Chartered accountants
Statisticians
Business analysts
Scientific and engineering professionals
Marketing, sales and advertising professionals
Teaching professionals.
K. INDICATORS OF QUALITY
School subject log, reviewed by Faculty Course Quality Assurance Committee (annual)
External examiners report, reviewed by Faculty Course Quality Assurance Committee (annual)
Validation event panel (2006)
L. APPROVED VARIANTS FROM THE UMS/PCF
There are no variants from the UMS scheme.
BSc (HONOURS) ACTUARIAL MATHEMATICS & STATISTICS
LEVEL 1LEVEL 2OPTIONALLEVEL 3Mathematical Science I
MA1010 (D)Mathematical Science II
MA1020B (D)
MA1010Mathematical Methods
MA2010A (D)
MA1020, MA1030Ordinary Differential Equations
MA2020 (C)
MA2010Industrial Placement YearFinancial Risk Management
FE3148 (B)
Stochastic Modelling in Finance
ST3360 (E)
MA1020,ST1210,ST3320Introduction to Probability and Statistics
ST1210 (E)Introduction to Linear Algebra
MA1030B (A)
MA1010Statistical Distributions
ST2220A(E)
ST1210, MA1010Actuarial Methods, Planning and Control
MA2401 (G)
MA2010PDEs & Approximation Theory
MA3010 (F)
MA2010, MA2020Further Inference and Bayesian Methods
ST3370 (G)
ST1210, ST2220Fundamental Programming Concepts
CO1000 (B)Accounting
FE1118 (E & G)Corporate Finance 1
FE2118 (A &B)
FE1118Operational Research Techniques
ST2353(F)
ST1210, MA1030Time Series
and Forecasting Methods
ST 3320 (E)
ST2210Mathematical Finance
MA3402 (A)
ST1210, MA3010
Free ChoiceObject-Oriented Programming with Java
CO1040 (F)
CO1000Regression Modeling
ST2210 (B)
ST1210, MA1030Contingencies
MA2402 (A)
MA2010Advanced Contingencies
MA3401 (A)
MA2402Option 3B
Note 1: Free Choice module chosen from a wide range of science/language/business modules.
Option 3B: Choose ONE from:
MA3150 Functional Analysis & Variational Methods for PDEs (F)
MA2010, MA2020
MA3190 Space Dynamics (C) MA2020
MA3120 Intro. To Calculus of Variations & Optimal Control (C)
MA2010, MA2020
MA3990 Project
FIELD SPECIFICATION KINGSTON UNIVERSITY
FILENAME Actuarial Mathematics and Statistics, BSc (Hons) 2008-2009
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