Last Updated:16 September, 2014 
 
 for general Admin. functions and positions in relation to taught and other programmes  see C.V. Undergraduate Lecturing on years 1 to 4 of undergraduate programmes, predominantly Maths. and Computer Applications degree programmes. Extensive curriculum development on these programmes, e.g. for initial financial/actuarial statistics options including Survival Models, Mortality, Risk; for Computational Science/Scientific Computing Methods, Linear Statistical Models, Time Series. Specific responsibility for Maths./Stats. content on CA streams during recent restructuring of undergraduate programmes  for details, see http://www.computing.dcu.ie/ Supervisions of projects Years 3 and 4 / 3rd_Year_ProjectIdeas_2013.pdf Current Fourth Year ProjectsPostgraduate Lecturing to taught Masters students in M.Sc. Computer Applications (MCA) and M.Sc. Computer Applications for Education, (MCE). Curriculum development for taught Masters programmes, e.g.Data Analysis, Simulation and Q.M., epidemiology and 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 /Computational Modelling, Time Series, Quantitative Methods and Simulation, Linear Statistical Models and Experimental Design, Survival Models, Risk and Reliability, Industrial Statistics, Intro. to Stochastic Processes, Intro. Statistics and so on. Current interests  see below. Current Module specifications for current courses  see some examples CA322 , CA436, CA448, CA449, CA451, CA534, not all of which typically run in any one year. More generally, course info. and coordinator info. http://www.computing.dcu.ie N.B.Courses for joint CA/Maths. Maths. Sci. degree and Maths. FAM programme  maintained also on School of Mathematics Web page. Service courses  see relevant School Web pages. CA151  Introductory Statistics (Maths.(1)). This is a onesemester introductory level Statistics course, which is geared to first year specialist Maths. degree students, who have acquired basic probability concepts in Semester 1 Discrete Mathematics (CA150). Students are expected to build on this in second semester. Topics covered will therefore include: Review of probability concepts, random variables and standard distributions. Properties of expectation and variance but not m.g.f's Statistical inference, including common sampling distributions and simple proofs. Illustrations include  Interval estimation and hypothesis testing  examples for one, two, manysamples, including simple regression analysis. In addition, students are expected to acquire reasonable familiarity with a basic statistical package through independent work, exercises and assignments. CA451–Non Linear Programming (Maths(4/5)). In this module, various advanced techniques of operations research are described and used to solve practical problems. Topics covered include: mixed integer programming; geometric programming  specifically NonLinear optimization through KuhnTucker/Lagrangian methods; network linear programming and maximal flows, with algorithms such as FordFulkerson; branch and bound methodology; dynamic programming and introduction to fractals and iterative function systems. Some general methods of Information Theory will be discussed, together with approaches to realworld applications of material distribution and production planning. Student will be encouraged to explore software relevant to the subject and use this where possible in assignments and exercises. Courses for Enterprise Computing programme CA200  Quantitative Analysis for Business Decisions (EC(2)). This is a onesemester course, which is geared to analysis of practical problems in decisionmaking for business. It involves understanding and applying basic probability and probability distributions, estimation and hypothesis testing for one to manysamples, including regression and correlation. The use of decision trees and decisionmaking under uncertainty, together with risk assessment is also discussed. The module also includes an overview of basic operations research topics, including an outline of linear programming, inventory control and queueing principles. Students are, in addition, expected to acquire reasonable familiarity with a basic statistical/O.R. software, such as R, through independent work, exercises and class practical work/assessments. CA569–Quantitative
Methods and Simulation (Masters(5)). This module was designed as a
refresher and enhancement course for mature students, (at taught M.Sc. in CA
level), to enable them to acquire/review elementary statistical techniques
for summarising and comparing statistical data. A review of socioeconomic
system analysis is briefly included as this is likely to be fundamental to
the type of educational projects envisaged. Simulation techniques are also
briefly covered to facilitate those who wish to model simple generic systems.
Aspects of the course include: review of elementary statistical analysis
techniques; design and management of projects with significant components of
data analysis (in education)  this in conjunction with the Research
Methodology Seminar and Workshop series. Simple models and analyses for
socioeconomic systems; an appreciation of computing 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, MCE9, MCE10 Research Methodology Seminar Series and Workshops (no
mod. spec.). Consists of a set of lecture topics and Workshops, which enable
students to participate in the critical evaluation of research material on
socioeconomic systems. Designed as a further preparation for Research topic
and Dissertation. RM1 , RM2 , RM3 CA570  Dissertation
On completion of course work, students carry out a
piece of research work under the supervision of a lecturer. Students must
normally complete and submit the dissertation in the semester following
completion of final taught components. The focus of the dissertation, guidelines for work plans, project management,
dissertation focus and format are given  in updated form for the current
academic year. CA578
–BioComputing (Masters(5) Life Sciences Stream). A short introduction
to Microarray methods, (covering many ideas to be subsequently discussed), is
given as extended illustrative material (in common CA579). The module
subsequently builds upon foundation courses in BioData Analysis, Computational
Biology and databases from first semester, to provide an overview of
computational methods and tools in Biocomputing. The aim is to improve
student's awareness of how statistics and heuristics supplement efficient
algorithm development in problemsolving in biology. An overview of
scientific computation will be given and model examples will be provided,
with an emphasis on realworld applications. These are necessarily selective
and may vary from yeartoyear. Further, examples of Bioinformatics tools and
programs will be reviewed and students will be given extensive exposure
through Practical Workshops to commonlyused Webbased examples in various
categories. Some may also go on to explore additional options in the
Practicum. Due to course blocks; Microarray methods, general
modelling/computation techniques and Practical Toolbased Workshops, most
material will be provided in class subsequent to Week 4. CA579
–BioMetrics and BioSystem Tools (Masters(5) Computing Stream). Short
introductory section on Microarrays (common CA578). The module then goes on
to build upon the foundation courses in BioData Analysis and Computational
Biology from first semester. The emphasis is on metrics, estimation and
prediction, (selected multivariate statistical techniques), on key modelling
and simulation methods for sequences/structures and on important genetic
techniques, such as mapping. The aim is to promote awareness that good
software design for Bioinformatics implies a sound understanding of the
underlying problems to be solved. Tutorial material on relevant statistical
examples will be provided. Exposure to a range of commonlyused
Bioinformatics/Biomodelling and analysis tools is achieved through Practical
Workshops (some of which are optional for this group). While tools are
predominantly Webbased in Workshops, there are opportunities to research and
download more powerful options for use e.g. in Practicum work. Due to the
"blocked" course structure, (exception tutorials, which impact on
all sections), most material will be provided in class, subsequent to Week 4.
The Statistical Data Analysis module
aims to bring students from diverse backgrounds up to speed on probability
and statistical methods and to introduce them to concepts and techniques,
relevant to their specific study areas. Examples, drawn from the application
areas of interest, will be analysed and discussed in depth. Students will be
encouraged to explore further the concepts and ideas introduced w.r.t. public
repositories and published materials and this will be built upon for the
assignment.
Outline Sample Introductory Statistics Course Notes 
general/basic See stats.ppt, stats1.ppt, 


