Module Details

Module Code: INFO8012
Title: BI & Data Visualisation
Long Title: BI & Data Visualisation
NFQ Level: Advanced
Valid From: Semester 1 - 2017/18 ( September 2017 )
Duration: 1 Semester
Credits: 5
Field of Study: 4820 - Information Systems
Module Delivered in: 1 programme(s)
Module Description: This module explores the areas of Big Data, Data Warehousing, Data Mining and
Business Intelligence.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Design, implement and administer a Data Warehouse.
LO2 Critically appraise and apply Data Mining techniques for selected Business Scenarios.
LO3 Evaluate, develop and use Business Intelligence systems.
LO4 Critically discuss and apply core Big Data concepts and their relevance to the Business Information Systems discipline.
Dependencies
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 MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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
 
Indicative Content
Data Warehouses
Database Design for Data Warehouses. Enterprise Data Warehouse Development. Data Integration Concepts. Extensions to SQL for Multidimensional Data. Summary Data Storage and Optimization.
Data Mining
Concepts. Data Mining in Business Scenarios. The Process of Data Mining. Statistical Methods. Nearest Neighbour Algorithm. Classifications. Clusters. Machine Learning. Decision Trees. Association Rule Mining. Neural Networks. Application of Prediction, Regression, and Pattern Matching techniques to Data Mining. Selecting a Data Mining technique. Text Mining. Data Mining for Multimedia Content. Application of selected spreadsheet software to Data Mining. Application of selected statistical software to Data Mining problems. Applied Case Studies in Health Informatics, Retail and Finance.
Business Intelligence
Business Intelligence Overview, Factors Driving Business Intelligence, Business Intelligence & Related Technologies, Business Intelligence in Contemporary Organisations, Obstacles to Business Intelligence, Business Intelligence Capabilities, Organisational Memory, Information Integration, Insight Creation, Presentation, Development of Business Intelligence, BI Vendors, Using Social Media, The Internet of Things, Mobile Computing and Big Data for Business Intelligence. Data Visualization. Dashboard Tools. Data Stories. Value Added Analysis. Application of selected reporting tool for Business Intelligence. Application of selected spreadsheet software for Business Intelligence.
Big Data
Why Big Data? Big Datasets. Sources of Big Data: Social Media, Knowledge Repositories. Big Data Processing Architectures. The Batch Layer. Serving Layer. Speed Layer. Big Data Models. The MapReduce Programming Model. Processing a Big Dataset using selected MapReduce software. Governance Frameworks for Big Data. Impacts of Big Data on Information Systems. Information Systems Management in the Big Data Era. Big Data in Business and Society: Applied Case Studies in Health Informatics, Finance and Marketing. Implications of Big Data on the Insurance Industry and its customers. Privacy in Big Data. Risk Assessment. Licensing Big Data. Big Data & Antitrust Laws: Price Fixing, Signaling Risks, Information Sharing Risks, Price Discrimination. Using Big Data to manage Human Resources. Big Data Preservation.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 50
Timing Every Second Week Learning Outcomes 1,2,3,4
Assessment Description
A series of exercises developing practical skills in Data Warehouse development, Data Mining, Business Intelligence Visualisation and Big Data Processing.
Assessment Type Short Answer Questions % of Total Mark 50
Timing Week 12 Learning Outcomes 1,2,3,4
Assessment Description
Theoretical and applied questions on Data Warehouses, Data Mining, Business Intelligence and Big Data.
No End of Module Formal Examination
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 University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lectures on technical and business concepts in Data Warehousing, Data Mining, Business Intelligence and Big Data. Every Week 2.00 2
Lab Contact Design and implementation of a Data Warehouse using selected Systems Modeling and Database software. Application of Data Mining techniques using selected statistical and spreadsheet software. Data Visualization for Business Intelligence using selected spreadsheet and reporting software. Big Data Processing using selected MapReduce software. Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Revision and study of material covered in lectures and labs. Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lectures on technical and business concepts in Data Warehousing, Data Mining, Business Intelligence and Big Data. Every Week 2.00 2
Lab Contact Design and implementation of a Data Warehouse using selected Systems Modeling and Database software. Application of Data Mining techniques using selected statistical and spreadsheet software. Data Visualization for Business Intelligence using selected spreadsheet and reporting software. Big Data Processing using selected MapReduce Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Revision and study of material covered in lectures and labs. Every Week 4.00 4
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • Michael V. Mannino. (2014), Database Design, Application Development, and Administration, 6th Edition. Chapters 16-17, Chicago Business Press, [ISBN: 978098333242].
  • Nathan Marz and James Warren. (2015), Big Data: Principles And Best Practices Of Scalable Realtime Data Systems, 1st Edition. Manning Publications, [ISBN: 978161729034].
  • Tom White. (2015), Hadoop: The Definitive Guide, 4th Edition. O'Reilly Media, [ISBN: 978149190163].
Supplementary Book Resources
  • Jiawei Han, Micheline Kamber and Jian Pei. (2011), Data Mining: Concepts and Techniques, Third Edition. Morgan Kaufmann, [ISBN: 978012381479].
  • Gordon S. Linoff. (2015), Data Analysis Using SQL and Excel, 2nd Edition. Wiley, [ISBN: 978111902143].
  • Cindi Howson. (2013), Successful Business Intelligence, 2nd Edition. McGraw-Hill Education, [ISBN: 978007180918].
  • Alberto Ferrari and Marco Russo. (2013), Microsoft Excel 2013 Building Data Models with PowerPivot (Business Skills), 1st Edition. Microsoft Press, [ISBN: 978073567634].
  • Joshua N. Milligan. (2015), Learning Tableau - How Data Visualization Brings Business Intelligence to Life, Packt Publishing, [ISBN: 97817843911].
  • Mark Gardener. (2012), Beginning R: The Statistical Programming Language, 1st Edition. Wrox Press, [ISBN: 978111816430].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_BBISY_8 Bachelor of Business (Honours) in Information Systems 8 Mandatory