DATA9004 - IT and Analytics for Business

Module Details

Module Code: DATA9004
Title: IT and Analytics for Business
Long Title: IT and Analytics for Business
NFQ Level: Expert
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4816 - Data Format
Module Delivered in: 4 programme(s)
Module Description: This module will provide an in depth study of the important themes in the growing field of IT and data analytics within a business context. The learner will study the established methods and technologies in IT systems and related strategies. Emphasis will also be placed on the context and use of data analytics in organisations, within decision support systems, business performance management and business process improvement. A number of important analytical methods will be assessed e.g. time series analysis, statistical techniques, machine learning, predictive modelling and how these can be applied to real world settings.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Identify and critique an organisation's business and operations processes from an IT and Data perspective with a view to identifying data use related opportunities to increase the effectiveness and efficiency of the business.
LO2 Evaluate the information sources available within organisations which support the implementation of Information Systems and ensure organisations make better and faster decisions.
LO3 Give a detailed overview of the main approaches to developing a data analytics/mining project.
LO4 Investigate and assess a number of business related data mining and business intelligence concepts and techniques.
LO5 Evaluate the impact of data protection, data privacy and other ethical issues in an IT and business context.
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
Technologies for Business
Networks and Connectivity, Storage, Security, Virtualisation, Cloud Computing, Mobile Computing, Collaborative Technologies.
Software and Information Systems
System and Application Software; Information Systems Components, Activities and Classification; Business Role of Information Systems, Enterprise Systems.
Project Management for Information System Implementation
Methodologies, Selection, Funding, Budgeting, Sourcing/Developing.
Business Process Improvement Methods
Lean Six Sigma, DMAIC, DMADV, key performance indicators (KPIs), balanced score cards, performance prism.
Data Analytics in a Business Context
Investigate the data science and analytics landscape, its historical development, terminology and technologies; big data concepts, structured and unstructured data types; how data analytics is incorporated into an organisation’s strategy and vision.
Data Analytics Project Life Cycle
Use of the CRISP-DM framework to manage a data analytics project with the variety of actors and challenges. Investigate case studies in data analytics, looking at a variety of approaches and technologies, including successes, failures, new developments and unusual applications of analytics.
Data Analytical Techniques
Overview of data mining techniques and algorithms - exploratory data analysis, regression and classification, pattern recognition, anomaly detection, visualisation techniques.
Ethics, Privacy and Security
Investigate ethics, privacy, security, data protection legislation, including GDPR, and other related topics in data governance.
Applied Analytics in a Business Context
Use of real life and simulated scenarios and datasets to apply data analytical techniques, e.g. predictive analytics and time series analysis of financial statements from the hotel management virtual software game HOTS.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Essay % of Total Mark 50
Timing Week 6 Learning Outcomes 1,2,5
Assessment Description
Assignment that evaluates a student’s ability to critically evaluate an organisation's current Information Technology/Information System strategy with regard to its level of competitiveness. The student will then develop a revised strategy that will ensure that the organisation achieves its business goals and objectives.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 3,4,5
Assessment Description
Investigate and solve a data analytics problem in a business situation.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

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 Delivery of content and material underpinning learning outcomes. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Reading, research & case studies. Every Week 5.00 5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivery of content and material underpinning learning outcomes. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Reading, research & case studies. Every Week 5.00 5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
 
Module Resources
Recommended Book Resources
  • Foster Provost, Tom Fawcett. (2013), Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, O'Reilly Media, Cambridge UK, [ISBN: 1449361323].
  • Matthew North. (2012), Data Mining for the Masses, 1st. Global Text Project, [ISBN: 0615684378].
  • Jelassi T., Enders A. and Martinez-Lopez F.J.. (2014), Strategies for E-Business: Creating Value through Electronic and Mobile Commerce, 3rd. Pearson Education Limited, [ISBN: 0273757873].
  • McNurlin B., Sprague R. and Bui T.. (2013), Information Systems Management, 8th. Pearson, [ISBN: 1292023546].
  • Hallows, H.. (2005), Information Systems Project Management: How to Deliver Function and Value in Information Technology Projects, 2nd. Amacom, [ISBN: 0814472737].
  • George M.L., Rowlands D., Price M. and Maxey J.. (2005), The Lean Six Sigma Pocket Toolbook, McGraw-Hill, [ISBN: 0071441190].
  • McDonald M.. (2010), Improving Business Processes: Expert Solutions to Everyday Challenges, Harvard Business Review Press, Boston, Massachusetts, [ISBN: 9781422129739].
Supplementary Book Resources
  • Vijay Kotu and Bala Deshpande. (2015), Predictive analytics and Data mining: Concepts and Practice with RapidMiner, 1st. ELSEVIER SCIENCE & TECHNOLOGY, San Francisco, United States, [ISBN: 0128014601].
  • Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. (2013), An Introduction to Statistical Learning, Springer-Verlag, New York, [ISBN: 9781461471387].
  • Efraim Turban , Ramesh Sharda, Dursun Delen. (2010), Decision Support and Business Intelligence Systems, 9th. Prentice Hall Press, Upper Saddle River, NJ, USA, [ISBN: 013610729X].
  • Andy Field, Jeremy Miles. (2010), Discovering Statistics Using SAS, 1st. Sage Publications Ltd, London, United Kingdom, [ISBN: 1849200920].
  • Project Management Institute. (2013), A Guide to the Project Management Body of Knowledge (PMBOK Guide), 5th ed. Project Management Institute, [ISBN: 9781935589679].
  • Boyer J., Frank B., Green B., Harris T. and Van De Vanter K.. (2010), Business Intelligence Strategy: A Practical Guide for Achieving BI Excellence, MC Press Online, [ISBN: 9781583473627].
Recommended Article/Paper Resources
  • Vijay Khatri, Carol V. Brown. (2010), Designing data governance, Communications of the ACM, Volume 53 Issue 1, January 2010, p.148,
    https://dl.acm.org/citation.cfm?id=16292 10
  • Hugh Watson. (2011), Business Analytics Insight: Hype or Here to Stay?, Business Intelligence Journal, vol. 16, No. 1.
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_BBADM_9 Master of Business Administration 3 Elective
CR_BSTRA_9 Master of Business Administration in Strategy 2 Mandatory
CR_BAVMA_9 Master of Business Administration with Aviation Management 2 Elective
CR_BAACC_9 Master of Science in Applied Accounting 3 Mandatory