CAPP09001 2023 Business Analytics

General Details

Full Title
Business Analytics
Transcript Title
Business Analytics
N/A %
Subject Area
CAPP - Computer Applications
BUS - Business
09 - NFQ Level 9
10 - 10 Credits
Start Term
2023 - Full Academic Year 2023-24
End Term
9999 - The End of Time
Alan Kelly, Breda McTaggart, Cillian OMurchu, Helen Grady, Bryan Coyne
Programme Membership
SG_BDIGI_M09 202300 Master of Science in Digital Business SG_BDIGI_O09 202300 Postgraduate Diploma in Science in Digital Business SG_BAPPL_S09 202300 Postgraduate Certificate in Applied Digital Business SG_SMARK_M09 202300 Master of Science in Marketing SG_SDIGI_S09 202300 Postgraduate Certificate in Digital Marketing SG_SMARK_O09 202300 Postgraduate Diploma in Science in Marketing SG_SMARL_M09 202300 Master of Science in Marketing SG_SMARM_M09 202300 Master of Science in Marketing

The aim of this module is to provide students with an understanding of the field of business analytics. This module will allow graduates to function in the modern business environment with the ability to operate between data scientists and management. It covers a broad range of topics, including areas related to quantitative analysis, statistics and data visualisation. Crucially, it also informs students of business performance metrics which are relevant in the modern working world. Students will learn how to engage with relevant literature, perform quality analysis and reinterpret it for application across all levels of the business.

Learning Outcomes

On completion of this module the learner will/should be able to;


Critically assess a wide range of spreadsheet functions used in a business environment


Demonstrate proficiency with appropriate statistical techniques, including descriptive and inferential analysis, that apply to businesses.


Examine and apply best practice in effective data visualisation techniques.


Critically review relevant business performance metrics and their role in the modern business environment.


Critically engage with relevant literature and report on key business insights arising from relevant analysis.

Teaching and Learning Strategies

Lectures will introduce the students to the theoretical aspects of Business Analytics, and this learning will underpin their capacity to learn practical skills that will enable them to undertake effective analysis and communicate the findings succinctly.  

Learners will gain practical experience of gathering, handling, analysing and reporting on business-relevant data.  

A UDL approach will be embedded in the delivery of this module to ensure it is accessible for all learners.  Learners will be encouraged to engage in reflective practice to help them identify how they can improve their learning during the semester.  

Module Assessment Strategies

Learners will complete several components of continuous assessment which are designed to ensure they meet the module learning outcomes and gain practical experience of applying Business Analytics in an educational setting.  

Their assessment will allow students an opportunity to showcase their understanding and ability to conduct statistical analysis and apply correct data visualisation techniques. Assessment will comprise an applied project where students must critically review relevant literature, demonstrate knowledge of key business performance metrics and apply both as part of a case study that can be tailored to the students current or expected employment.

Repeat Assessments

The repeat assessment strategy will be dependent on overall grades and will be decided and documented at the Progression and Award Boards.  

Indicative Syllabus

LO 1 Apply a wide range of spreadsheet functions used in a business environment

  • Understand popular data collection methods
  • Implement and understand popular data cleaning processes (irrelevant data, duplicates, structural errors, missing data, outliers, validation)


LO 2 Demonstrate proficiency with appropriate statistical techniques, including descriptive and inferential analysis, that apply to businesses.

  • Execute appropriate descriptive analysis
  • Execute appropriate inferential analysis
  • Generate and conduct data analysis (regression, hypothesis, context, discourse analysis)


LO 3 Understand and apply best practice in effective data visualisation techniques.

  • Understand relevant data visualisation practices and software
  • Compare applications of data visualisation in the modern business environment
  • Conduct appropriate data visualisation of popular data structures (e.g. cross-sectional, time series and longitudinal data).


LO 4 Critically review relevant business performance metrics and their role in the modern business environment.

  • Evaluate business performance metrics
  • Interpret business case for analytics
  • Develop sustainability metrics


LO 5 Engage critically with relevant literature and report on key business insights arising from relevant analysis.

  • Engaging with relevant literature and developing critical review skills
  • Designing a case study related to applying Business Analytics to consider performance across key metrics
  • Develop effect report writing relevant to the topic.

Coursework & Assessment Breakdown

Coursework & Continuous Assessment
100 %

Coursework Assessment

Title Type Form Percent Week Learning Outcomes Assessed
1 Data Analysis Project Coursework Assessment Individual Project 40 % Week 8 1,2,3
2 Applied Project Coursework Assessment Individual Project 60 % End of Semester 2,3,4,5

Full Time Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Lecture Theatre Lecture 3 Weekly 3.00
Independent Learning Not Specified Independent Learning 6 Weekly 6.00
Total Full Time Average Weekly Learner Contact Time 3.00 Hours

Online Learning Mode Workload

Type Location Description Hours Frequency Avg Workload
Lecture Online Online Lecture 3 Weekly 3.00
Independent Learning Not Specified Independent Learning 6 Weekly 6.00
Total Online Learning Average Weekly Learner Contact Time 3.00 Hours

Required & Recommended Book List

Required Reading
2018-10-05 Business Research SAGE Publications, Incorporated
ISBN 1544307829 ISBN-13 9781544307824

Business Research: A Guide to Planning, Conducting and Reporting Your Study bridges the academic foundation and the practical application of research methodology through an in-depth and insightful tour of the research processexploring, planning, creating, conducting, collecting, analyzing, and reporting. The text weaves together timeless principles, emerging ideas, contemporary examples and modern tools in a narrative that is both authoritative and supportive. Integrating a unique Roadmap framework throughout, Business Research navigates students from the start of their initial inquiry to their final stop in reporting their findings, building their confidence as they move point-to-point in their journey. Written with exceptional clarity and focus, Donald Cooper has created a guide to research that will be valuable to students in their academic pursuits as well as their professional careers.

Required Reading
2019-04-15 Microsoft Excel 2019 Data Analysis and Business Modeling Microsoft Press
ISBN 1509305882 ISBN-13 9781509305889

Master business modeling and analysis techniques with Microsoft Excel 2019, and transform data into bottom-line results. Written by award-winning educator Wayne Winston, this hands-on, scenario-focused guide shows you how to use the latest Excel tools to integrate data from multiple tables-and how to effectively build a relational data source inside an Excel workbook.

Required Reading
2021 Basic Statistics for Business and Economics
ISBN 1260716317 ISBN-13 9781260716313

"The objective of Basic Statistics for Business and Economics is to provide students majoring in management, marketing, finance, accounting, economics, and other fields of business administration with an introductory survey of descriptive and inferential statis-tics. To illustrate the application of statistics, we use many examples and exercises that focus on business applications, but also relate to the current world of the college stu-dent. A previous course in statistics is not necessary, and the mathematical requirement is first-year algebra"--

Recommended Reading
2022 Cost Accounting
ISBN 1119624398 ISBN-13 9781119624394

"The text provides numerous discussions on how decision-makers are increasingly relying on data analytics to make decisions using accounting information. Accounting software systems collect vast amounts of data about a company's economic events as well as its suppliers and customers. Business decision-makers take advantage of this wealth of data by using data analytics to gain insights and therefore make more informed business decisions. Data analytics involves analyzing data, often employing both software and statistics, to draw inferences. As both data access and analytical software improve, the use of data analytics to support decisions is becoming increasingly common at virtually all types of companies."--

Recommended Reading
2021-02-15 Succeeding with OKRs in Agile
ISBN 1912832062 ISBN-13 9781912832064

OKRs are about goals bigger than the next story. OKRs prioritise purpose and strategy over backlogs. Objectives are big goals; key results are smaller goals that build towards the objective. Does your agile team get lead astray by burning fires? Do you struggle to keep your agile team focused? Do you feel the need for more than just doing the top of the backlog every two weeks? Are you using, or want to use, OKRs with an agile team? Then this is the book for you. Acclaimed author Allan Kelly has written a short guide to OKRs, writing them, organizing to deliver and the pitfalls. Allan is the author of multiple books on agile and has given advice and training for over 10 years. Now he turns his attention to OKRs. In this book he doesn't try to sell OKRs - others can tell you why OKRs are great. Allan describes his practical experience working with an agile team adopting OKRs, day-by-day, quarter-by-quarter. Allan's advice includes: be really specific in setting goals, involve the whole team in setting OKRs, think broad when setting then execute narrowly, set analogue not binary OKRs and, most controversially, throw away your backlog and let OKRs drive everything you do. Initially sceptical about OKRs Allan found them a good fit with agile; OKRs became an effective means of focus teams, exposing problems, communicating with senior managers and a powerful means of asking bigger questions about product strategy and value. OKRs and agile work well together because they are both outcome oriented and results focused. When used right OKRs give power and authority to teams - one could even say OKRs create test first management. Yet OKR can be a double edge-sword, used poorly they can re-introduce command-and-control and hinder agile working. Allan addresses problems with predictability, aspirations, culture, targets and annual reviews. "Easy read, super useful book for my current context at work right now!" @c_combe on Twitter "I especially like the honest portrayal of top-down MBO OKR-setting and its problems. And providing tips on how to go about using OKRs in a different way." @anttiki "I recommend heartily and have done so openly on the book seller's site - brilliant, balanced and lived experience and feedback from Allan" @rj_number_one "Initially, I was thinking to join some OKR training but honestly felt this book is good enough to get one on the right path!" @ProdScrumMaster

Recommended Reading
2017-09-19 Competing on Analytics: Updated, with a New Introduction
ISBN 1633693724 ISBN-13 9781633693722

From two pioneers in business analytics, an update of the classic book on how analytics and business intelligence are transforming competition and how leading organizations build and compete on an analytical capability.

Module Resources

Non ISBN Literary Resources

Updated Literary Resources
Journal Resources

Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44.

Al-Htaybat, K., & von Alberti-Alhtaybat, L. (2017). Big Data and corporate reporting: impacts and paradoxes. Accounting, Auditing & Accountability Journal, 30(4), 850–873.

Warren, D. J. J., Moffitt, K. C., Byrnes, P., Warren, J. D., Moffitt, K. C., & Byrnes, P. (2015). How big data will change accounting. Accounting Horizons, 29(2), 397–407. 

Bhimani, A., & Willcocks, L. (2014). Digitisation, “Big Data” and the transformation of accounting information. Accounting and Business Research, 44(4), 469–490.

Davenport, T. H. (2006, January). Competing on Analytics. Harvard Business Review.

URL Resources
  • The latest relevant links and resources will be provided during the course of the module,
  • See VLE
Other Resources

Other resources will be added as required on an ongoing basis.

Additional Information