Module Details

Module Code: INFO7016
Title: Business Analytics
Long Title: Business Analytics
NFQ Level: Intermediate
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 introduces students to the key Business Analytics techniques required by IT professionals as well as discussing the applicability of these techniques to the main business disciplines.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Discuss the impacts of Business Analytics upon Business & Society.
LO2 Evaluate the application of Business Analytics in organisational functions and processes.
LO3 Interpret Regression Analysis Models.
LO4 Evaluate and Interpret Statistical Learning Models.
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).
10554 MATH6051 Essential Maths& Stats for Bus
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
Business Analytics & Corporate Strategy
The Business Analytics Model. The role of the Systems Analyst in Business Analytics. The role of the Data Scientist. Business Analytics at the Functional, Analytical, Data Warehouse & Strategic Levels. Business Analytics Processes: Web & Text Mining, Visual Analytics, Data Mining, Statistical Learning. Achieving Competitive Advantage with Business Analytics. Intellectual Property. Intangible Assets. Redesigning Business Processes. Information Prioritization. Competency Centres. Critical Success Factors. Case Studies in business functions such as Finance & Marketing e.g. Customer Behaviour Modelling, Link Predictions & Social Recommendations, Fraud Detection & Financial Statement Analysis.
Technology & Societal Impacts
Enabling technologies. Sources of data: Big Data Technologies. Impacts & use of social networks. Impacts of Cloud Computing. Legal, privacy & ethical issues. Analytics ecosystems & industry clusters. Selected Case Studies.
Linear Regression
Principles of Linear Regression. Interpreting Linear Regression Analysis Results. Conducting Regression Analysis with selected Statistical Software.
Statistical Learning
Data Mining Concepts. Decision Trees, Conducting Statistical Learning Using Selected Spreadsheet and Statistical Software.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Practical/Skills Evaluation % of Total Mark 50
Timing Every Second Week Learning Outcomes 3,4
Assessment Description
A series of exercises applying practical Business Analytics methods to a selected business scenario. Students will also be required to collate the output of these exercises into a business report.
Assessment Type Project % of Total Mark 50
Timing Sem End Learning Outcomes 1,2
Assessment Description
This project will require the student to evaluate the role of Business Analytics in a business scenario.
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 the main Business Analytics techniques available to IT professionals and their applicability to the main business disciplines. Every Week 2.00 2
Lab Contact Lab sessions using selected spreadsheet and statistical software to solve business problems. Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Revision on lecture and lab material. Practical exercises using Business Analytics techniques. 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 the main Business Analytics techniques available to IT professionals and their applicability to the main business disciplines. Every Week 2.00 2
Lab Contact Lab sessions using selected spreadsheet and statistical software to solve business problems. Every Week 1.00 1
Independent & Directed Learning (Non-contact) Non Contact Revision on lecture and lab material. Practical exercises using Business Analytics techniques. 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
  • Foster Provost and Tom Fawcett. (2013), Data Science for Business: What you need to know about data mining and data-analytic thinking, 1st Edition. O'Reilly Media, [ISBN: 978144936132].
Supplementary Book Resources
  • Douglas C. Montgomery, Elizabeth A. Peck and G. Geoffrey Vining. (2012), Introduction to Linear Regression Analysis, 5th Edition. Wiley, [ISBN: 978047054281].
  • Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning, 5th Edition. Springer, [ISBN: 9781461471370].
  • Alberto Ferrari and Marco Russo. (2013), Microsoft Excel 2013 Building Data Models with PowerPivot (Business Skills), 1st Edition. Microsoft Press, [ISBN: 978073567634].
  • Nina Zumel, John Mount and Jim Porzak. (2014), Practical Data Science with R, 1st Edition. Manning, [ISBN: 978161729156].
  • Kaursen and Thorlund. Business Analytics for Managers:Taking Business Intelligence beyond Reporting, [ISBN: 9780470890615].
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 5 Mandatory