IB122-15 Business Analytics
Introductory description
Business Analytics is used by companies committed to data-driven decision-making. It is the practice of methodical and intelligent exploration of an organisation’s data to gain insight and drive business strategy and operational decisions
Module aims
The overall aim of this module is to enable students to develop an analytic mind-set and give them a basic data literacy useful for the business environment. The module aims to give students an understanding of the three main types of business analytics:
Descriptive analytics – provide insight into the past and answer the question “what has happened?”
Predictive analytics – use of quantitative data modelling techniques to provide insight into the future and answer the question “what might happen?”
Prescriptive analytics – investigate the actions needed to achieve predicted desired outcomes and answer the question “what should I do?”
Teaching is via lectures and workshops with an emphasis on solving real-life problems using case study material. The workshops offer students the opportunity to apply the concepts introduced in the lecture material. Assessment is via examination and assignment, offering students the opportunity to demonstrate their ability to understand and apply the material to relevant problems.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
An introduction to descriptive, predictive and prescriptive analytics.
Statistical and mathematical modelling of ‘real’ business data to answer descriptive, predictive and prescriptive business questions.
Learning outcomes
By the end of the module, students should be able to:
- Be able to describe the 3 main types of analytics (Descriptive, Predictive and Prescriptive) and understand their importance in the business decision making process
- Be able to demonstrate familiarity with basic quantitative analytic concepts and some specific business analytic techniques, e.g. statistical modelling of multivariate data, mathematical programming and optimisation methods for decision making.
- Use appropriate software to analyse business data and carry out some of the more common descriptive, predictive and prescriptive analytic methods/techniques, e.g. statistical modelling of multivariate data, mathematical programming and optimisation methods for decision making.
- Understand and interpret the output / results of such analytic techniques in a business context.
- Problem solving skills.
- Critical and logical thinking.
- Analytical mindset.
Indicative reading list
Illustrative bibliography: Evans, J. (2016) 2nd - Edition, Business Analytics: Methods, Models and Decisions, Pearson, ISBN: 9781292095448. Powell, S.G & Baker, K.R. (2017), 5th Edition, Business Analytics: The Art of Modeling With Spreadsheets, Wiley, IBSN:9781119298427. Schniederjans, M.J. & D.G & Starkey, C.M. (2014), Business Analytics Principles Concepts and Applications: What, Why and How, Pearson, ISBN: 9780133552188. Albright S.C. & Winston W.L. (2015), 5th Edition, Business Analytics: Data Analysis and Decision Making, Cengage Learning, ISBN:9781133629603. Jank, W. (2011), Business Analytics for Managers, Springer-Verlag, ISBN: 9781461404057. Hillier, F. (2015) Introduction to Linear Programming: Introduction to operations research. United Kingdom: McGraw-Hill, pp.25- 70. Denardo, E.V., (2011). Linear programming and generalizations: a problem-based introduction with spreadsheets (Vol. 149). Springer Science & Business Media, Chapter 1 (pages 3-11), Chapter 2 (pages 33-63).
Subject specific skills
Be able to apply some practical real world analytical approaches for business decision making / problem solving. Have developed a working knowledge of appropriate analytical software.
Transferable skills
Problem solving skills. Critical and logical thinking. Analytical mindset. Competency in understanding and producing numerate / quantitative communication. Developed a basic data literacy useful for the business environment.
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 1 hour (13%) |
| Seminars | 9 sessions of 1 hour (12%) |
| Online learning (independent) | 10 sessions of 1 hour (13%) |
| Private study | 48 hours (62%) |
| Total | 77 hours |
Private study description
Private Study.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group D9
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Group Work (15 CATS) | 30% | 22 hours | No |
| In-person Examination | 70% | 51 hours | No |
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Exam ~Platforms - AEP
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Feedback on assessment
Marker's comments on typical errors are posted to my.wbs.
Pre-requisites
To take this module, you must have passed:
Post-requisite modules
If you pass this module, you can take:
- EC239-15 Economics 2: Macroeconomics
- EC239-15 Economics 2: Macroeconomics
- EC204-30 Economics 2
- EC226-30 Econometrics 1
- IB2B2-15 Financial Econometrics
- EC205-15 Development Economics (Macroeconomics)
- EC238-15 Economics 2: Microeconomics
- EC238-15 Economics 2: Microeconomics
- IB3BE-15 Business Experimentation for Data-Driven Decision-making
- IB320-15 Simulation
- EC354-15 Development Economics (Macroeconomics) - For Finalists
Courses
This module is Core optional for:
- Year 1 of UGEA-RN21 Undergraduate German and Business Studies
- Year 1 of UIPA-L8N1 Undergraduate Global Sustainable Development and Business