B.Sc HONOURS
SYLLABI
2
Module Code
Module Title
MATH 4831
Mathematical Methods 2
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathematical
Sciences
Module Author:
Dr. David J. McCarthy
Module Description:
The module contains a treatment of advanced mathematical
techniques applied to linear differential equations, to vector calculus and to complex
variables. The general theory of linear ord
inary differential equations is developed,
including the case of Sturm

Liouville systems; solution techniques for linear partial
differential equations are treated. The integral theorems of vector calculus are
covered. Integral calculus of functions of one
complex variable is treated, including
Cauchy’s Theorem, the Residue Theorem and applications.
Module aim
The aim of this module is to
introduce some basic theorems on the solutions of linear ordinary
differential equations,
develop techniques for
solving second

order linear ordinary differential
equations,
cover solution methods for linear partial differential equations,
investigate the properties of special functions (such as Bessel and Legendre
functions),
treat in detail the integral theorems of
vector calculus(e.g. the theorems of
Green and Stokes) and their applications and
introduce the basic theorems of contour integration, such as Cauchy’s
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Learning Outcomes:
On successful completion of this module the learner will be able to
solve second

order linear ordinary differential equations by using power
series or by using the Frobenius method
apply standard solution techniques to important linear partial differential
equations (such as the wave equation, the heat equation)
evaluate real integrals and sum series by the use of contour integration
apply the integral theorems of v
ector calculus to mathematical problems
which arise in science and engineering.
Learning and Teaching Methods
Lectures 24 hrs/Semester (48 over 2 Semesters)
3
Tutorials/Practical 12 hrs/Semester (24 over 2 Semesters)
Module conte
nt:
Linear Differential Equations
Inner product on a function space. Expansion of functions in series of orthogonal
functions. Mean square convergence.
Sturm

Liouville theory. Expansions of functions in series of eigenfunctions of
self

adjoint (Hermitian
) operators.
General theory of linear ordinary differential equations. Power series solutions of
second

order linear ordinary differential equations. Legendre and Bessel functions.
Solution techniques for linear partial differential equations, including se
paration of
variables, integral transforms, Green’s functions.
Special Functions
Gamma function. Bessel and Legendre functions. Asymptotic expansions.
Vector Calculus
Theorems of Green, Stokes, Gauss and applications.
Integral Equations
Linear equations
of Volterra and Fredholm. Singular integral equations.
Complex Analysis
Cauchy’s theorem and integrals. Taylor and Laurent series, contour integration

evaluation of real integrals. Uniformly convergent series of regular functions

expansion of a meromo
rphic function. Summation of series by residue methods.
Module Assessment
Minor projects/Problem sets 25% of final mark
End of module written examination (3 ½ hours) 75% of final
mark
Essential Reading
Arfken G. B., Weber H. J.,
Mathematical Methods for Physicists
(6
th
Ed.) 2005,
Academic Press
Supplemental Reading
Hassani S.,
Mathematical Physics
–
A Modern Introduction to its Foundations
(2002), Springer

Verlag
Further Details:
Stage 3

Part 7

Linked module over two semesters.
Weekly contact hours:
Lecture 2 hrs
Tutorial 1 hr
Date of
Academic Council approval
………………………….
4
Module Code
Module Title
MATH 4832
Topics in Analysis
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathematical
Sciences
Module Author:
Dr. Susan Lazarus
Module Description:
We begin by reviewing the basic concepts of vector spaces, and then study concepts
of analysis on normed linear spaces, on which we have a notion of vector length. We
then study inner prod
uct spaces and introductory concepts of operators.
Module aim:
To provide a solid foundation in the study of analysis and to develop
the student’s ability to reason in a mathematically rigorous manner.
Learning Outcomes:
On completion of the course
the student should:
(i) be able to demonstrate a thorough understanding of convergence, completeness
and continuity in normed linear spaces and inner product spaces,
(ii) be adept at working with norms and inner products, and
(iii) be able to demonstrate
properties of linear operators on normed linear spaces and
inner product spaces
Learning and Teaching Methods:
Lectures 24hrs/Semester(48 over 2 Semesters)
Tutorial 12hrs/Semeste
r(24 over 2 Semesters)
Module content:
Normed Spaces
Review of vector spaces. Normed linear spaces, convergence, continuity, closed sets,
function spaces, completeness, compactness, Banach’s Fixed

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Hilbert Spaces
Inner product spaces,
orthogonality, Bessel’s Theorem, Parseval’s Relation, and the
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Theory of Operators
Bounded linear operators. The adjoint of an
operator, self

adjoint operators, unitary
operators. Eigenvalue problems for self

adjoint operators.
Module Assessment:
Assignment 25% of final Mark
End of Module written exam 75% of final mark
5
Essential Reading:
Kreyszig, Erwin, Introductory Functional Analysis with
Applications, John Wiley & Sons, 1989
Recommended Reading:
Saxe, Karen, Beginning Functional Analysis, Springer
Verlag, 2002
Supplemental Reading:
Griffel, D. H.
Applied Functional Analysis, John Wiley &
Sons, 1985
Further Details:
Stage 3

Part 7

Linked module over two semesters.
Weekly contact hours: Lecture 2 hrs/week
Tutorial 1 hr/week
Date of Ac
ademic Council approval
………………………….
6
Module Code
Module Title
MATH 4840
Differential Equations and Numerical Methods
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathematical Sciences
Module Author:
Dr. Chris Hills
Module Description:
Partial differential equations occur throughout mathematical
modelling from meteorology and continuum mechanics, to financial mathematics,
quantum mechanics, mathematica
l biology and electromagnetism. The module
introduces advanced techniques to tackle partial differential equations both
analytically and via numerical methods. The learner will be able to solve practical
partial differential equations and an understanding
of their behaviour will be
developed.
Module aim:
The aim of this module is to
introduce the learner to the ideas and techniques of solving partial
differential equations
develop and analyse the suitability and effectiveness of competing
numerical m
ethods
discover which PDEs can be solved analytically, which require a
numerical approach
discuss modern applications of partial differential equations and appreciate
how models can be constructed
Learning Outcomes:
On successful completion of
this module the learner will be able to
solve first and second order, linear and nonlinear partial differential
equations occurring throughout the sciences both analytically and using
tailor

made numerical algorithms
analyse the effectiveness of numerical
schemes
model simple processes such as traffic flow
apply and analyse numerical techniques to solve real world problems
Learning and Teaching Methods:
Lectures 24 hrs/Semester (48 over 2 Semesters)
Tutorials 12 h
rs/Semester (24 over 2 Semesters)
7
Module content:
Introduction
to
the definitions and terminology associated with partial differential
equations and numerical modelling.
First

order PDEs: linear PDEs, method of characteristics; quasi

linear PDEs,
singularities and consistency, traffic flow; non

linear PDEs, Charpit’s method.
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Module Assessment:
Minor projects/Problem sets 25% of final Mark
End of Module written exam (3 ½ h
ours) 75% of final mark
Essential Reading
Smith G.D.,
Numerical Solution of Partial Differential Equations: Finite Difference
Methods
, 1985, Oxford University Press
Supplemental Reading
Ockendon J., Howison S. , Lacey
A. & Movchan A.,
Applied Partial Differential
Equations
, 2003, Oxford University Press
Sneddon I.M.,
Elements of Partial Differential Equations
, 1957, McGraw

Hill
Williams W.E.,
Partial Differential Equations
, 1980, Oxford Clarendon Press
Further Deta
ils Further Details
:
Stage 3
–
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Date of Academic Council app
roval
………………………….
8
Module Code
Module Title
MATH 4833
Mathematical Control Theory
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathematical Sciences
M
odule Author:
Dr. David J. McCarthy
Module Description:
This module introduces the learner to the techniques and
results that form the basis of control theory. The skills and methods for solving
problems in control theory are developed.
Module aim:
The aim of this module is to
introduce the learner to the concepts and techniques of control theory
ensure that the qualitative theory of differential equations can be applied to
simple dynamical systems
provide an understanding of the state

space approac
h
give an appreciation of optimal control theory
Learning Outcomes:
On successful completion of this module the learner will be able to
implement the state

space approach and solve linear control systems
use mathematical tests to determine the stabi
lity characteristics of
dynamical systems
assess controllability/observability of linear control systems
apply the Pontryagin Principle for optimal control
Learning and Teaching Methods:
Lectures 24 hrs/Semester (4
8 over 2 Semesters)
Tutorials 12 hrs/Semester (24 over 2 Semesters)
9
Module content:
Introduction
to
the mathematical formulation of control problems, state space
representation, linear systems, fundamental matrix, two

dimensional
systems, phase
portraits, critical points.
Stability theory for non

linear systems, Liapunov stability theory, almost linear
systems, periodic solutions and limit cycles.
State transition matrix, controllability and observability, state feedback, system
re
alisation.
Techniques of the calculus of variations, optimal control problems, necessary
conditions for optimality, the Bolza problem, Pontryagin’s principle, bang

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Module Assessment:
Minor p
rojects/Problem sets 25% of final Mark
End of Module written exam (3 ½ hours) 75% of final mark
Essential Reading
Barnett S. & Cameron R. G.,
Introduction to Mathematical Control
Theory,
2
nd
Edition 1985, Oxford University Press.
Supplemental Reading
Brauer F. & Nohel J. A.,
Qualitative Theory of Ordinary Differential Equations
1990, Dover.
Hocking L.,
Optimal Control
1991
,
Clarendon Press.
Further Details:
Stage 3
–
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Date of Academic Council appro
val
………………………….
10
Module Code
Module Title
MATH 4834
Advanced Algebra
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
None
None
2005
15
School:
Mathematical Sciences
Module Author:
Susan La
zarus
Module Description:
The module begins with a review of important properties of
the integers. It continues to investigate the algebraic systems of groups, rings and
fields.
Module Description:
To provide a solid foundation in the area of abstr
act algebra in
order to prepare students to handle the algebra they will meet in all areas of
mathematics, and form a firm foundation for more specialized work in
algebra.
Learning Outcomes:
On completion of the module, the student will
Be able to d
emonstrate understanding of the fundamental concepts within the
areas of group theory, ring theory and field theory,
have developed the ability to reason formally and present a mathematical
argument in a fully rigorous fashion.
Learning a
nd Teaching Methods:
Lectures 24hrs/Semester(48 over 2 Semesters)
Tutorial 12hrs/Semester(24 over 2 Semesters)
11
Module content:
Preliminaries
:
Integers, Equivalence Relations,
Congruences.
Group Theory
Transformation groups, Homomorphism, Isomorphism, Cayley's theorem,
Cosets, Lagrange's theorem, Normal subgroups, Quotient groups, Isomorphism
theorems, Symmetric groups, Class equation, Sylow's theorems, Direct
products, Finit
e Abelian groups, Soluble groups.
Ring Theory
Homomorphism, Isomorphism, Ideals, Quotient rings, Isomorphism
theorems, Commutative rings, Integral domains, Fields, Principal ideal
domains, Prime ideals, Maximal ideals, Quotient field, Divisibility, Eucl
idean
domains, Polynomial rings.
Field Theory
Extension fields, Simple extensions, Finite extensions, Algebraic extensions,
Minimal polynomial, Algebraic closure, Galois group, Splitting field, Galois
correspondence, Solution by radicals.
Module As
sessment:
Module Assessment:
Assignment 25% of final Mark
End of Module written exam(3
) 75% of final mark
Essential Reading
:
Gallian J. A., Contemporary Abstract Algebr
a, 6
th
Edition,
Houghton Mifflin Co., 2006
Recommended Reading:
Herstein, I. N., Abstract Algebra, 3
rd
Edition, John Wiley
& Sons, 1996
Supplemental Reading:
Fraleigh J. B., A First Course in Abstract Algebra, 7
th
Edition, Addison Wesley, 2002.
Web re
ferences, journals and other:
www.d.umn.edu/~jgallian
Further Details:
Stage 3
–
ma牴‸r

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Date of Academic Council approval
………………………….
12
Module Code
Module Title
MATH 4835
Operations Research
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
M
athematical Sciences
Module Author:
Dr. David J. McCarthy
Module Description:
This module introduces the learner to the concepts and basic
methods of Operation Research. Linear programming is introduced and is used to
formulate and solve applied problem
s. Scientific and industrial models are used to
apply the theory of both queuing theory and Markov processes. Software packages are
used to solve and simulate problems in each topic.
Module aim:
The aim of this module is to
introduce the learner to th
e concepts and methods of operation research,
provide an understanding of both the basic models and applications of
queuing theory and
provide the learner with sufficient theory and examples in stochastic
processes to enable models in sci
ence and industry to be formulated and
solved.
Learning Outcomes:
On successful completion of this module the learner will be
able to
formulate and solve applied problems in linear programming,
design and analyse queuing models in applied areas,
apply
the theory of stochastic processes acquired to resolve problems using
these techniques and
assess and solve models in all these areas using various software packages.
Learning and Teaching Methods:
Lectures 24 hrs/Semester (48 hrs over
2 Semesters)
Tutorials 6 hrs/Semester (12 hrs over 2 Semesters)
Laboratory 6 hrs/Semester (12 hrs over 2 Semesters)
13
Module content:
Queuing Theory
Introduction to queuing models. Poisson arrival pattern. Negative exponentia
l service
pattern. Different queuing models

M/M/
1 and
M/M/
S infinite models,
M/M/
1 and
M/M/
S finite models.
M/M/
1 with varying arrival patterns and service rates.
Derivation and analysis of mean queuing times, mean number of customers in the
system, etc.
for above models.
Stochastic Processes
Review of matrix analysis. Definition of primitive/imprimitive matrices,
reducible/irreducible matrices. Definition of a stochastic process and Markov chains.
First

order and higher order transition matrices. Classif
ications of states of a Markov
chain

absorbing, persistent, transient, periodic, null, non

null, ergodic. Theorems
relating states, long

term probabilities, etc. Existence of limits for irreducible ergodic
chains.
Linear Programming
Introduction to and e
xamples of linear programs. Linear programs in standard form.
Definitions of feasible, basic feasible and optimal solutions. The fundamental theorem
of linear programming (with proof). Relations to convexity. Simplex method

pivots,
vectors to leave and e
nter basis, determining a minimum feasible solution. Artificial
variables. Variables with upper bounds. Duality

dual linear programs, the duality
theorem. Simplex multipliers. sensitivity and complementary slackness. Dual Simplex
method. Primal

dual alg
orithm. Reduction of linear inequalities.
Practical
Use of software packages to solve problems and set up simulations.
Module Assessment:
One minor project 25% of final mark
End of modul
e written examination ( 3 ½ hours) 75% of final mark
Essential Reading
Taha H. A.,
Operations Research

An Introduction
(8
th
Ed.) 2006, Prentice Hall.
Luenberger D.G.,
Linear and Non

linear Programming
(2
nd
Edition) 2003, Kluwer
Academic
.
Supplemental Reading
Medhi J.,
Stochastic Processes
(2
nd
Ed) 2000, Wiley.
Further Details
Stage 3

Part 8

Linked module over two semesters.
Weekly contact hours:
Lecture
2 hrs
Tutorial 0.5 hrs
Laboratory 0.5 hrs
Date of Academic Council approval
………………………….
14
Module Code
Module Title
MATH 4836
Quantum Theory
ISCED
Code
Pre

Requisite
Modul
e Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathematical Sciences
Module Author:
Dr. Emil M Prodanov
Module Description
: This module introduces the student to the main concepts of
quantum mechanics. The material provides a sound knowledge of the most important
topics from physical perspective and also through the mathematical formalism.
Module aim:
The aim of this module is to
introduce the student to the fundamental concepts
of quantum mechanics,
provide thorough knowledge of the most important areas,
promote clear, precise and analytical thinking.
Learning Outcomes:
On successful completion of this module the learner will be
able to:
Fit quantum mechanics in the general
picture of theoretical physics and
thoroughly understand the interplay between quantum mechanics and the
other major physics theories, classical mechanics in particular.
Understand the mathematical apparatus of quantum mechanics.
Formulate and solve the m
ost standard problems of quantum mechanics
including linear harmonic oscillator, potential wells and barriers, hydrogen
atom.
Demonstrate confidence when using quantum mechanics in both
the Heisenberg and Schrödinger formulations.
Lear
ning and Teaching Methods:
Lectures 24hrs/Semester(48 over 2 Semesters)
Tutorial 12hrs/Semester(24 over 2 Semesters)
15
Module content:
. THE WAVE FUNCTION
i. The Schrodinger E
quation
ii. The Statistical Interpretation
iii. Probability
iv. Normalization
v. Momentum
vi. Heisenberg’s Uncertainty Principle
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Module Assessment:
Minor projects/Problem sets 25% of final mark
End of module written examination (3 ½ hours)
75% of final mark
16
Essential Reading
:
(1) David J. Griffiths,
Introduction to Quantum Mechanics
2/e, Benjamin Cummings
(2004), ISBN

13: 978

0131118928
(2) J. J. Sakurai,
Modern Quantum Mechanics
2/e, Addison

Wesley (1993), ISBN

13:
978

0201539295
Supplemental Reading:
L. D. Landau and L. M. Lifshitz,
Quantum Mechanics: Non

Relativistic Theory
,
Butterworth

Heinemann (1981), ISBN

13: 978

0750635394
Further Details:
Stage 3

Part 8

Linked module over two semesters.
Weekly contact hours:
Lecture 2 hrs
Tutorial 1 hr
Date of Academic Council approval
………………………….
17
Module Code
Module Title
MATH 4837
Fluid Mechanics
ISCED
Code
Pre

Requisite
Mod
ule Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathematical Sciences
Module Author:
Dr Brendan Redmond
Module Description:
This module presents the basic and more advanced principles
of fluid mechanics and illustrates them by application to a variety of problems of
scientific interest.
Module aim:
The aim of this module is:
To introduce the fundamental aspects of both classic and modern fluid
mechanics and to provide techniques f
or solving specific classes of fluid flow
problems.
Learning Outcomes:
On completion of this module the student should be able to
understand the phenomena which are associated with various properties of
fluids
apply appropriate mathematical techniqu
es in solving problems in fluid
dynamics.
Learning and Teaching Methods:
Lectures 24hrs/Semester(48 over 2 Semesters)
Tutorial 12hrs/Semester(24 over 2 Semesters)
18
Module conte
nt:
Preliminaries
Properties of fluids, dimensional reasoning, the continuum model, Newtonian fluids.
Fundamental Equations
Conservation laws, the rate of strain matrix. Mathematical methods
–
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Flow Kinematics
Acceleration of a
fluid particle, flow lines, vorticity.
Ideal Fluid Flow
Stream function, complex potential and complex velocity, complex variable
techniques, aerofoil theory.
Solutions of the Navier Stokes Equations
Couette flow, flow in a pipe, flow down an inclined pla
ne.
Slow Viscous Flows
The biharmonic equation and some solutions of it.
Water Waves
Surface gravity waves, sinusoidal waves on deep water, particle paths for travelling
waves.
Module Assessment:
Assignment
25% of final mark.
End of module written examination(3
) 75% of final mark.
Essential Reading:
Batchelor G. K., An Introduction to Fluid Dynamics, Cambridge Univer
sity Press,
2000.
Currie I. G., Fundamental Mechanics of Fluids, Marcel Dekker, 2002.
Supplemental Reading
:
White F. M., Fluid Mechanics, McGraw

Hill, 2008.
Young D.F., Munson B.R., Okiishi T.H., A brief introduction to fluid mechanics,
Wiley, 2004.
Further Details
:
Stage 3

Part 8

Linked module over two semesters
Weekly contact hours Lectures 2 hrs/week
Tutorial 1 hr/week
Dat
e of Academic Council approval
………………………….
19
Module Code
Module Title
MATH 4838
Financial Mathematics
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Modules
None
2005
15
School:
Mathe
matical Sciences
Module Author:
Mr Anthony Byrne
Module Description:
This module introduces the terminology of financial markets
and many relatively new mathematical techniques used in valuing financial
instruments. Stochastic methods are employed to va
lue stocks. Black

Scholes
analysis is introduced and used to price options. Extensive use is made of financial
packages in the formation of models and the solution of problems relating to the
valuation of options.
.
Module aim:
The aim of this module
is to
introduce the concepts and terminology of stocks and financial derivatives,
analyse the application of stochastic processes in modeling option pricing,
derive the Black

Scholes partial differential equation,
apply Black

Scholes analysis to the valua
tion of options and stock indices
and
devise models using financial packages ( e.g. Matlab) and use these
packages to determine the values of options
Learning Outcomes:
On successful completion of this module the learner will be able to
identify dif
ferent types of financial instruments and their particular
characteristics,
model option prices using stochastic processes,
apply Black

Scholes analysis to determine the value of options and
use financial packages to model and solve a range of problems i
n stock and
option pricing.
Learning and Teaching Methods:
Lectures 24 hrs/Semester (48 hrs over 2 Semesters)
Tutorials 6 hrs/Semester (12 hrs over 2 Semesters)
Laboratory 6 hrs/Semester (12 hrs over 2 Semesters
)
20
Module content:
Financial Markets
Introduction to financial markets. Derivatives. Forwards and futures.
Option and option positions. Margins and swaps.
Trading and Option Pricing
Factors affecting option prices, Put

call parity. Forward and futu
re pricing. Trading
strategies. Introduction to binomial trees and option pricing.
Stochastic Methods and Valuations
Asset price modelling. Wiener processes. Ito’s Lemma.
Black

Scholes Analysis
Introduction to Black

Scholes analysis. Put

call parity.
Arbitrage.
Boundary and final conditions. Derivation of the Black

Scholes equation.
Applications of the Black

Scholes equation.
Variation on the Black

Scholes model
Dividend paying assets. Forward and future contracts. American Options.
Free boundary
problems. Exotic and barrier options.
Software Packages
Use is made throughout the module of software packages (e.g. Matlab) to form
models and solve applied problems.
Module Assessment
One minor project
25% of final mark.
End of module written examination (3 ½ hours) 75% of final mark.
Essential Reading:
Wilmott P., Howison S., Dewynne J.,
Mathematics of Financial
Derivatives
1995, Cambridge University Press
Re
commended Reading:
Higham D.,
An Introduction to Financial Options:
Mathematics, Stochastics and Computation
2004, Cambridge University Press.
Supplemental Reading:
Capinski M., Zastawniak T.,
Mathematics for Finance
–
An Introduction to Financial Engineer
ing
(2005) Springer
Web references, journals and other:
Further Details:
Stage 3

Part 8

Linked module over two semesters
Weekly contact hours Lectures 2 hrs
Tutorial/Lab 1 hr
Date of Academic Council approval
………………………….
21
Module Code
Module Title
MATH 4839
Major Project
ISCED
Code
Pre

Requisite
Module Code(s)
Co

Requisite
Module Code(s)
Last Revision
Date
ECTS
Credits
461
Stage 2 Mod
ules
None
2005
15
School:
Mathematical Sciences
Module Author:
Mr. Anthony Byrne
Module Description:
This module offers the learner the opportunity of choosing a
major project as an optional module at Stage 3. The choice of topic will be chosen in
di
scussion with staff attached to the programme
Module aim:
The aim of this module is to give the learner the opportunity of
pursuing an in

depth investigation into a topic in which they have a particular interest.
It might also be chosen in connection
with their employment.
Learning Outcomes:
On successful completion of this module the learner will have
gained an in

depth knowledge of a distinct mathematical topic,
greatly enhanced their ability to research and manage a significant project
and
imp
roved their ability to coordinate and present a research document.
Learning and Teaching Methods:
The teaching method in this module will be
flexible. Regular meetings/tutorials will be provided where the learner will be assisted
and encouraged. Use
will be made of the extensive facilities of this College and,
where appropriate, other institutions.
Module content:
The content of this module will be chosen in consultation with the
academic staff on the programme. It is envisaged that, in many case
s, the choice of
topic may originate from the learner.
Module Assessment:
This module will be assessed under the following criteria
Oral Presentation 15%
Project Report 45%
Understand
ing/Conduct 40%
Understanding refers to the level of comprehension as assessed by the supervisor
and the second internal examiner.
22
Conduct refers to the candidate’s behaviour with reference to punctuality in attending
arranged meetings with the su
pervisor, promptness in carrying out supervisors
instructions, timeliness of completion, etc..
A log book will be given to the candidate which will form a record of supervision and
include details regarding agreed programmes of work, a timetable of work c
ompleted
etc.. The oral presentation will be assessed by at least three members of the
Programme team. The report in typed, bound form will be assessed by two internal
examiners (one of whom is the project supervisor). The project supervisor will report
o
n the candidate’s understanding of the material and their conduct during the period
involving the project. The project will be moderated by the external examiner.
Essential Reading
As prescribed by project supervisor
Supplemental Reading
Texts
and papers
as prescribed by project supervisor
Further Details:
Stage 3
–
Part 8
–
Linked module over two semesters
Weekly contact hours Tutorial 1 hr
Research 2 hours
Date of Academic Council approval
………………………….
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