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COMP8016 - Business Intelligence

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Title:Business Intelligence
Long Title:Business Intelligence
Module Code:COMP8016
 
Credits: 5
NFQ Level:Advanced
Field of Study: Computer Science
Valid From: Semester 1 - 2012/13 ( September 2012 )
Module Delivered in no programmes
Module Coordinator: TIM HORGAN
Module Author: LINDA O SULLIVAN
Module Description: Since the 1970s, organisations have mostly focused their investment in new computer systems that automate business processes. In this way, organisations gained competitive advantage through systems that offered more efficient and cost-effective services to the customer. Throughout this period, organisations accumulated growing amounts of data stored in their operational databases. However, in recent times, where such systems are commonplace, organisations are focusing on ways to use operational data to support decision-making, as a means of regaining competitive advantage. This module is designed to cover some of the main technologies associated with Business Intelligence (BI) namely: data warehousing, online analytical processing (OLAP)and data mining.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Describe the main concepts, benefits and problem areas associated with data warehousing
LO2 Describe the various architectures and main components of a data warehouse.
LO3 Design a data warehouse, and be able to address issues that arise when implementing a data warehouse.
LO4 Compare and contrast OLAP and data mining as techniques for extracting knowledge from a data warehouse.
Pre-requisite learning
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
149 SOFT8018 Specialised Database Systems
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Data Warehousing
OLTP systems versus data warehousing. Data warehousing concepts; benefits and problem areas; Architecture and main components of a data warehouse; Managing metadata.
Data Preparation and pre-processing
The need to pre-process data; Data Cleaning; Handling Missing Data; Identifying Misclassifications; Graphical methods for identifying Outliers; Data Transformation; Numerical Methods for Identifying Outliers.
Data Warehouse Design
Corporate Information Factory (CIF) versus Business Dimensional Lifecycle Methodologies. Dimensionality modeling: Star, Snowflake and Starflake schemas
On-line analytical processing (OLAP)
OLAP applications. OLAP operations: roll-up, drill-down, slice and dice, and pivot. OLAP Tools: Multidimensional OLAP (MOLAP), Relational OLAP (ROLAP), Hybrid OLAP (HOLAP) and Desktop OLAP (DOLAP)
Data Mining
Algorithms: Predictive modelling, database segmentation, link analysis and deviation detection. Cross Industry Standard Process for Data Mining (CRISP-DM) specification
Assessment Breakdown%
Course Work30.00%
End of Module Formal Examination70.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Project Data warehouse design, implementation and knowledge discovery project. 3,4 30.0 Week 8
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam End-of-Semester Final Examination 1,2,3,4 70.0 End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory 2.0 Every Week 2.00
Lab Computer-based Lab 1.0 Every Week 1.00
Tutorial Theory/Practical 1.0 Every Week 1.00
Independent & Directed Learning (Non-contact) Independent Study 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Theory 2.0 Every Week 2.00
Lab Computer-based Lab 2.0 Every Second Week 1.00
Independent & Directed Learning (Non-contact) Independent Study 4.0 Every Week 4.00
Total Hours 8.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Thomas M. Connolly, Carolyn E. Begg, 2010, Database Systems: A Practical Approach to Design, Implementation and Management, 5th Ed., Addison Wesley [ISBN: 0321523067]
Supplementary Book Resources
  • Anthony David Giordano, 2010, Data Integration Blueprint and Modeling, 1 Ed., IBM Press [ISBN: 0137084935]
  • Ralph Kimball, Margy Ross, Warren Thornthwaite (Contributor), Joy Mundy (Contributor), Bob Becker (Contributor) 2010, The Kimball Group Reader, Wiley [ISBN: 9780470563106]
  • Daniel T. Larose 2005, Discovering knowledge in data, Wiley-Interscience Hoboken, N.J. [ISBN: 978-0-471-66657-8]
  • Richard Roiger, Michael Geatz 2002, Data mining, Addison Wesley Boston [ISBN: 0201741288]
This module does not have any article/paper resources
This module does not have any other resources
 

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