Last Updated:06 October, 2011
- for general Admin. functions and positions in relation to taught and other programmes - see C.V.
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 re-structuring of undergraduate programmes - for details, see http://www.computing.dcu.ie/
Supervisions of projects Years 3 and 4 / Current Fourth Year Projects
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.
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.
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 one-semester 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, many-samples, 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 Non-Linear optimization through Kuhn-Tucker/Lagrangian methods; network linear programming and maximal flows, with algorithms such as Ford-Fulkerson; 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 real-world 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.
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 socio-economic 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 socio-economic 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.
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 socio-economic 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 problem-solving in biology. An overview of scientific computation will be given and model examples will be provided, with an emphasis on real-world applications. These are necessarily selective and may vary from year-to-year. Further, examples of Bioinformatics tools and programs will be reviewed and students will be given extensive exposure through Practical Workshops to commonly-used Web-based 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 Tool-based 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 commonly-used Bioinformatics/Biomodelling and analysis tools is achieved through Practical Workshops (some of which are optional for this group). While tools are predominantly Web-based 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 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