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DATA9004 - IT and Analytics for Business

Title:IT and Analytics for Business
Long Title:IT and Analytics for Business
Module Code:DATA9004
Duration:1 Semester
Credits: 5
NFQ Level:Expert
Field of Study: Data Format
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 3 programme(s)
Module Coordinator: David Goulding
Module Author: Aengus Daly
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:
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.
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 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

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

Module Content & Assessment

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.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Essay 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. 1,2,5 50.0 Week 6
Project Investigate and solve a data analytics problem in a business situation. 3,4,5 50.0 Sem End
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 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 Delivery of content and material underpinning learning outcomes. 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Reading, research & case studies. 5.0 Every Week 5.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Delivery of content and material underpinning learning outcomes. 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Reading, research & case studies. 5.0 Every Week 5.00
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 Ed., 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 Ed., Pearson Education Limited [ISBN: 0273757873]
  • McNurlin B., Sprague R. and Bui T. 2013, Information Systems Management, 8th Ed., Pearson [ISBN: 1292023546]
  • Hallows, H. 2005, Information Systems Project Management: How to Deliver Function and Value in Information Technology Projects, 2nd Ed., 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 Ed., 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 Ed., Prentice Hall Press Upper Saddle River, NJ, USA [ISBN: 013610729X]
  • Andy Field, Jeremy Miles 2010, Discovering Statistics Using SAS, 1st Ed., 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 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, 148
  • 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_BAACC_9 Master of Science in Applied Accounting 3 Mandatory

Cork Institute of Technology
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