Short Title:Business Intelligence with Analytics
Full Title:Business Intelligence with Analytics
Language of Instruction:English
Module Code:BIPM H6005
 
Credits: 10
Field of Study:Computing
Module Delivered in 4 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:SEAN MC HUGH
Module Description:The aim of this module for a student to understand business analytics; to analyse the design and implementation of Business Intelligence Systems in supporting the decision support requirements in enterprises; to demonstrate a critical understanding of data mining techniques employed in business knowledge discovery; To apply appropriate data mining techniques and tools for the data mining task at hand including cloud services for decision making.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Evaluate the processes, tools and technologies used to implement data warehouses and big data environments.
LO2 Critically examine the techniques for modelling data from a logical and physical perspective to assist the data analytic requirements of an enterprise.
LO3 Select and apply business intelligence tools to perform the types of analysis that are required by decision makers.
LO4 Formulate appraisals on data mining applications and evaluate their methods and techniques.
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Continuous Assessment CA1 Typical task: Given particular business requirements and data characteristics, choose and design relevant data architectures from a logical and physical perspective. Design and implement appropriate extraction, transformation and loading rules to populate the data architecture using operational data sources available and perform analytical analysis. 1,2 25.00 Week 4
Continuous Assessment CA2 Typical task: Using particular data sources, build a Business Intelligence (BI) metadata repository. Using BI analytical tools develop and produce appropriate responses to decision support questions as well as deciding on and using appropriate data mining techniques to guide the knowledge discovery process; 3,4 25.00 Week 11
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 50.00 End-of-Semester

TU Dublin – Tallaght Campus 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 In class contact 1.00 Every Week 1.00
Lecturer/Lab In class laboratory 1.00 Every Week 1.00
Lab Laboratory contact 1.00 Every Week 1.00
Independent Learning Self directed study 4.00 Every Week 4.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture In class contact 1.00 Every Week 1.00
Lecturer/Lab In class laboratory 1.00 Every Week 1.00
Lab Laboratory contact 1.00 Every Week 1.00
Independent Learning Time Self directed study 4.00 Every Week 4.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Required Book Resources
  • Jiawei Han, Micheline Kamber, Jian Pei, 2011, Data Mining: Concepts and Techniques, Third Edition, 3rd Ed. [ISBN: 0123814790]
  • Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal 2017, Data Mining: Practical Machine Learning Tools and Techniques, 4th Ed., Morgan Kaufmann [ISBN: 978-012804291]
  • Nina Zumel, John Mount 2014, Practical Data Science with R, ; Manning Publications Company [ISBN: 1617291560]
  • Ralph Kimball, Margy Ross, Warren Thornthwaite (Contributor), Joy Mundy (Contributor), Bob Becker (Contributor) 2010, The Kimball Group Reader, 2nd Edition Ed., Wiley [ISBN: 0470563109]
Recommended Book Resources
  • Tom White 2015, Hadoop: The Definitive Guide, 4th Edition Ed., O'Reilly Media [ISBN: 1-4493-1152-0]
  • Pramod J. Sadalage, Martin Fowler, 2013, NoSQL Distilled, Pearson [ISBN: 9780321826626]
  • Tan Pang Ning, Introduction to Data Mining, 2nd Ed., Pearson Academic Computing [ISBN: 0133128903]
  • Daniel T. Larose 2014, Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Edition Ed. [ISBN: 978-0-470-908]
  • Foster Provost and Tom Fawcett 2013, Data Science for Business [ISBN: 978-1-449-361]
  • Daniel T. Larose 2006, Data Mining Methods and Models, Wiley
  • Stephane Tuffery 2011, Data Mining and Statistics for Decision Making, 1 Ed., Wiley [ISBN: 9780470688298]
  • Stephen Few, O‚ÄôReilly 2008, Information Dashboard Design: The Effective Visual Communication of Data
This module does not have any article/paper resources
Other Resources
  • Website and Software: WEKA Data Mining Tool
  • Website and Software: R for Statistical Computing
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KDMCO_M M. Sc. in Distributed and Mobile Computing 1 Elective
TA_KITMG_M M.Sc. in Information Technology Management 1 Elective
TA_KDMCO_PD Postgraduate Diploma in Distributed and Mobile Computing 1 Elective
TA_KITMG_PD Postgraduate Diploma in Information Technology Management 1 Elective