Module Code: H7BID
Long Title Business Intelligence and Data Warehousing I
Title Business Intelligence and Data Warehousing I
Module Level: LEVEL 7
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator: Simon Caton
Module Author: Simon Caton
Departments: School of Computing
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Generalise enterprise data in order to produce organised business reports.
LO2 Identify and distinguish compositions of enterprise data that yield value through business intelligence approaches
LO3 Evaluate and apply OLAP methodologies and techniques with respect to inferring business value from enterprise data
LO4 Evaluate vendor solutions for business intelligence in the context of data warehousing.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  
 

Module Content & Assessment

Indicative Content
Foundations of Business Intelligence and Data Warehousing
• Defining Business Intelligence and its Application Areas. • Data Warehousing Fundamentals: Inmon's and Kimbel's Approaches. • Data Warehouse Architectures (e.g. Enterprise Data Warehouse, Federated, Enterprise Service Bus, etc.) • Data Models Kimbel's Front and Back Room Analogy
Analytics for Business Intelligence
• Online Analytical Processing (OLAP). • OLAP. • Cube Building. • OLAP operations (e.g. Slice, Dice, Roll Up, Drill Down). • Vendor Solutions and Tools for OLAP. • Foundations of Data Mining for Business Intelligence.
Visualisation and Reporting
• Business Reporting. • End User Dashboards. • Deriving Value from Business Data Reporting Tools and Vendor Solutions.
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment (0200) % of total: 40
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
Sample Assessments: Learners will apply practical skills developed throughout the module to undertake and simulate business intelligence applications and/or case studies. Learners may also be required to develop and evaluate reporting methods.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
End-of-Semester Final Examination
No Workplace Assessment
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.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Tutorial No Description 1 Every Week 1.00
Independent Learning No Description 7.5 Every Week 7.50
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Practical No Description 2 Every Week 2.00
Independent Learning No Description 7.5 Every Week 7.50
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Cindi Howson. (2013), Successful Business Intelligence: Unlock the Value of BI & Big Data, 2. Mcgraw-Hill Osborne Media, p.320, [ISBN: 9780071809184].
  • Foster Provost and Tom Fawcett. (2013), Data Science for Business: What you need to know about data mining and data-analytic thinking, O'Reilly Media, p.414, [ISBN: 9781449361327].
Supplementary Book Resources
  • Eric Siegel. (2013), Predictive Analytics, Wiley, p.288, [ISBN: 9781118356852].
  • W. H. Inmon. (2005), Building the data warehouse, 4. Wiley, Indianapolis, Ind., p.576, [ISBN: 9780764599446].
  • Ralph Kimball... [et al.]. (2008), The data warehouse lifecycle toolkit, Wiley Pub., Indianapolis, IN, [ISBN: 9780470149775].
This module does not have any article/paper resources
Other Resources
Discussion Note: