Prerequisite Modules
Description
Business systems intelligence is an
area of increasing importance and interest to organisations involved
in knowledge management. Technologies such as data warehousing and
data mining offer huge potential in the creation of new knowledge
products and services and the enhancement of existing products and
services. The module builds on the student's previous experiences of
working with databases, knowledge tools, techniques and data analysis.
This module covers topics in business systems intelligence relating to
the formulation of data and business models for understanding data,
construction of data warehouses and the application of data mining
techniques.
Aims
To study and practise advanced data
modelling techniques and to understand and practice techniques of data
warehousing and data mining in the context of business systems.
Learning Outcomes
On successful completion of this
module, the student will be able to
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Discuss how to build a business
data model
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Build a dimensional data model
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Discuss the role of data
warehousing and data mining in an organisation
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Analyse and evaluate the
suitability of different data warehouse architectures
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Develop dimensional models for a
data warehouse
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Analyse and evaluate the issues
involved in extracting and loading data into a data warehouse
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Discuss the suitability of
different data mining techniques
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Identify the requirements of
developing a model for data mining
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Develop a model for a data mining
application
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Perform different data mining
techniques to data
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Discuss and evaluate the outcomes
from a data mining process
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
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Business Data Modelling
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Data, Information, Knowledge
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Modelling an activity
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Framing a business model
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Developing a model
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Deploying a model
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Data Warehousing
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Introduction to data
warehousing
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Characteristics of a data
warehouse and how it differs to operational DBs etc
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Extracting and loading data
into a data warehouse
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Dimensional modelling
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Data aggregation
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Data Mining
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Introduction to data mining
and applications of data mining
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Data mining lifecycles
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Data preparation
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Data association techniques
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Data classification techniques
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Data clustering techniques
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Data visualisation
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Data evaluation
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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Kimball & Ross, 2002, The Data
Warehouse Toolkit: The Complete Guide to Dimensional Modeling
(Second Edition), Wiley.
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Han & Kamber, 2001, Data Mining:
Concepts and Techniques, Morgan Kaufmann.
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Inmon, 2005, Building the Data
Warehouse, Hungry Minds.
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Michael J. A. Berry, Gordon Linoff,
1997,Data Mining Techniques: For Marketing, Sales, and Customer
Support, Wiley.
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Richard J. Roiger, Michael Gaetz,
2002, Data mining : a tutorial-based primer, Addison Wesley.
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Olivia Parr Rud, 2000, Data mining
cookbook : modeling data for marketing, risk and customer
relationship management, Wiley.
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Oracle Technology Network,
http://www.otn.oracle.com/
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Database Trends and Applications
website, http://www.dbta.com/
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Data Mining & Knowledge Discovery,
http://www.kdnuggets.com/
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Kimball, 1997, Dimensional
Modelling Manifesto, DBMS Magazine
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Fayyad, 1996, From Data Mining to
Knowledge Discovery in Data, AI Magazine
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DBMS magazine,
http://www.ienterprise.com/
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DMReview,
http://www.dmreview.com
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For more information contact
Ciarán O'Leary
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