Prerequisite Modules
Description
This module presents further
theoretical aspects of computer sciences which are necessary to
support and enhance other modules on the course. In particular topics
covered on this module will be required in computer technology,
databases, object oriented programming, graphics programming, software
engineering, programming and algorithms. This module builds on the
Computing Fundamentals 1 and also provides
the foundations of statistics, graphs, lattices, and algebras.
Aims
The aim of this module is to provide
the student with the theoretical foundations for other modules on the
programme.
Learning Outcomes
Knowledge and Understanding
On successful completion of this
module the student will be able to:
-
demonstrate a knowledge of the
application of statistics to database systems
-
identify foundational issues when
they are encountered in other modules
-
apply fundamental theory to other
modules
Skills and Know-How
On successful completion of this
module the student will be able to:
-
use the course topics to solve
computing problems
-
use software and related tools
Competence
On successful completion of this
module the student will be able to:
-
use module topics on a variety of
problems
-
use appropriate software to solve
problems
Learning and Teaching Methods
Lectures, self-study, labs,
tutorials, and any combination of discussion, case study,
problem-solving exercises, readings, seminars, and computer-based
learning.
Content
Graph Theory:
Definition, properties, graph representation, types, paths, cycles,
isomorphism of graphs, planar graph, application of graphs to
computing.
Statistics:
range, mode, median, mean, standard deviation, variance, sampling and
sampling distributions, probability, hypothesis testing, applications
of statistics (e.g. analysis of data stored in a relational database)
Lattice Theory:
lattice notation and definition, relations, closure of relations,
ordered sets, partial orders, linear orders, application of lattices
to computing (e.g. pre and post conditions in software engineering).
Algebraic Structures and
Techniques: algebras, theories, models,
composition, abstract data types (ADTs), languages for algebraic
specification and programming, applications to lists, strings, queues,
sacks, trees, etc.
Supporting software:
The above topics will be supported by software tools such as general
statistical packages, statistical extensions to SQL, functional and
logic based programming languages
Assessment
The methods of assessment to be used
to measure the learning objectives stated above are written
examination and continuous assessment including one or more of
assignment, essay, problem-solving exercise, oral presentation, and
class or lab tests.
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Continuous Assessment: 30%
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Examination: 70%
Recommended
Reading
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Seymour Lipschutz, 1987, Essential
Computer Mathematics, Schaum's Outline series, ISBN 0-07-0379990-4.
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Winfred Karl Grassmann and
Jean-Paul Tremblay, 1996, Logic and Discrete Mathematics A Computer
Science Perspective, Prentice Hall, ISBN 0-13-501206-6.
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Trueblood and Lovett, 2004, Data
Mining and Statistical Analysis using SQL, Apress.
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Seymour Lipschutz and Marc Lars
Lipson, 1997, Discrete Mathematics, Schaum's Outline series, ISBN
0-07-038045-7.
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Cordelia Hall and John O'Donnell,
2000, Discrete Mathematics Using a Computer, Springer-Verlag, ISBN
18522330899.
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For more information contact
Ciarán O'Leary
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