Teaching Summary
- for general Admin. functions and positions in relation to taught and other prog
see C.V.
Undergraduate
Last Updated:14 October, 2003
Lecturing on years 1 to 4 of undergraduate programmes, predominantly Maths.
Computer Applications degree programmes. Extensive curriculum development
programmes, e.g. for initial financial/actuarial statistics options including Surviv
Models, Mortality, Risk; for Computational Science/Scientific Computing Meth
Linear Statistical Models, Time Series. Specific responsibility for Maths./Stats.
CA streams during recent re-structuring of undergraduate programmes - for deta
http://www.computing.dcu.ie/
Supervisions of projects Years 3 and 4 / Current Fourth Year Projects
Postgraduate
Lecturing to taught Masters students in M.Sc. Computer Applications (MCA) an
Computer Applications for Education, (MCE). Curriculum development for taug
- Masters programmes, e.g.Data Analysis, Simulation and Q.M., epidemiology an
biostatistics.
Research Methodology seminar coordinator (MCE).
Dissertation Coordinator (MCE). Examiner /Supervisor to MCA and MCE.
Course range
Other example courses currently/previously taught include: Advanced /Computa
Modelling, Time Series, Quantitative Methods and Simulation, Linear Statistica
and Experimental Design, Survival Models, Risk and Reliability, Industrial Stat
Intro. to Stochastic Processes, Intro. Statistics and so on. Current interests - see b
Current
Module specifications for current courses - see some examples CA322 , CA436
CA449, CA451, CA534, not all of which typically run in any one year. More ge
course info. and coordinator info. http://www.computing.dcu.ie
N.B.Courses for joint CA/Maths. Maths. Sci. degree and Maths. FAM programm
maintained also on School of Mathematics Web page. Service courses - see rele
School Web pages.
CA151 - Introductory Statistics (Maths.(1)). This is a one-semester introducto
Statistics course, which is geared to first year specialist Maths. degree students,
acquired basic probability concepts in Semester 1 Discrete Mathematics (CA150
Students are expected to build on this in second semester. Topics covered will th
include: Review of probability concepts, random variables and standard distribu
Properties of expectation and variance but not m.g.f's Statistical inference, inclu
common sampling distributions and simple proofs. Illustrations include - Interva
estimation and hypothesis testing - examples for one, two, many-samples, includ
simple regression analysis. In addition, students are expected to acquire reasona
familiarity with a basic statistical package through independent work, exercises
assignments.
CA451–Non Linear Programming (Maths(4/5)). In this module, various adva
techniques of operations research are described and used to solve practical probl
Topics covered include: mixed integer programming; geometric programming specifically Non-Linear optimization through Kuhn-Tucker/Lagrangian method
linear programming and maximal flows, with algorithms such as Ford-Fulkerson
and bound methodology; dynamic programming and introduction to fractals and
function systems. Some general methods of Information Theory will be discusse
together with approaches to real-world applications of material distribution and
planning. Student will be encouraged to explore software relevant to the subject
this where possible in assignments and exercises.
CA569–Quantitative Methods and Simulation (Masters(5)). -This module w
designed as a refresher and enhancement course for mature students, (at ta
M.Sc. in CA level), to enable them to acquire/review elementary statistical t
for summarising and comparing statistical data. A review of socio-economi
analysis is briefly included as this is likely to be fundamental to the type of
educational projects envisaged. Simulation techniques are also briefly cover
facilitate those who wish to model simple generic systems. Aspects of the co
include: review of elementary statistical analysis techniques; design and ma
of projects with significant components of data analysis (in education) - this
conjunction with the Research Methodology Seminar and Workshop series
models and analyses for socio-economic systems; an appreciation of compu
methods in the handling of quantitative material. The MCE degree is now
incorporated into the more general M.Sc.IT , with the option of pursuing a
specialised dissertation.
Notes: MCE1, MCE2, MCE3, MCE4, MCE5, MCE6, MCE7, MCE8,
MCE10
Research Methodology Seminar Series and Workshops (no mod. spec.). Co
set of lecture topics and Workshops, which enable students to participate in
critical evaluation of research material on socio-economic systems. Designe
further preparation for Research topic and Dissertation. RM1 , RM2 , RM3
CA660
The Data Analysis module aims to bring students from diverse backgroun
speed on probability and statistical methods and to introduce them to conce
techniques, relevant to their specific study areas. Examples, drawn from th
application areas of interest, will be analysed and discussed in depth. Stude
encouraged to explore further the concepts and ideas introduced w.r.t. pub
repositories and published materials and this will be built upon for the assig
CA660_DATA_ANALYSIS_1
CA570 - Dissertation
On completion of course work, students carry out a piece of research work
supervision of a lecturer. Students must normally complete and submit the
dissertation in the semester following completion of final taught component
focus of the dissertation, guidelines for work plans, project management, di
focus and format are given - in updated form for the current academic year
Outline Sample Introductory Statistics Course Notes - general/basic
See stats.ppt, stats1.ppt,
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