Module overview
In today's era of "big data", business analytics has become a key part of management decision making. Modern managers must now routinely understand the use and value of both qualitative and quantitative data in order to manage risk more effectively. This module provides an overview of the key analytical tools and techniques to improve decision-making in an uncertain business environment.
Aims and Objectives
Learning Outcomes
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Thinking critically and arguing effectively;
- Information technology and computing using commercial software;
- Use problem structuring methods to analyse complex problems.
- Interpreting and analysing quantitative data related to business issues, using appropriate financial and/or statistical skills and models to solve problems;
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The impact of risk and uncertainty in a range of business scenarios;
- The different uses of data analytics to inform business decisions.
- A range of analytical modelling approaches to support business decisions.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Analyse and evaluate business risk scenarios, based on relevant data and statistical techniques;
- Apply a range of business analytics to support business decisions.
- Critically evaluate the appropriate application of different analytical approaches;
Syllabus
Basic principles underpinning descriptive, predictive and prescriptive analytics such as, but not limited to:
- Statistics analysis
- Visual analytics
- Decision making under uncertainty
- Forecasting and classification
- Simulation
Applications of descriptive analytics tools
Applications of predictive analytics tools
Applications of prescriptive analytics tools
Learning and Teaching
Teaching and learning methods
- Lectures
- Problem solving sessions
- Case studies
| Type | Hours |
|---|---|
| Teaching | 30 |
| Independent Study | 70 |
| Total study time | 100 |
Resources & Reading list
Journal Articles
Kunc, M., and O'Brien, F.A. (2018). The role of business analytics in supporting strategy processes: Opportunities and limitations. Journal of the Operational Research Society, 1-12.
Textbooks
Robinson, S. (2004). Simulation: The Practice of Model Development and Use. John Wiley & Sons.
Kunc, M. (2018). Strategic Analytics: Integrating Management Science and Strategy. John Wiley & Sons.
Curwin, J. and Slater, R (2004). Quantitative Methods: A short course. Thomson.
Render, B., Stair, R.M. and Hanna M.E. (2009). Quantitative Analysis for Management. Prentice Hall.
Evans, J.R. (2014). Business Analytics: Methods, Models, and Decisions. Upper Saddle River, NJ: Pearson.
Wisniewski, M. (2010). Quantitative Methods for Decision Makers. Prentice Hall.
Morris, C. (2003). Quantitative Approaches in Business Studies. Prentice Hall.
Assessment
Formative
This is how we’ll give you feedback as you are learning. It is not a formal test or exam.
In-class formative opportunities
- Assessment Type: Formative
- Feedback: Individual feedback on exercises will be provided for students in class.
- Final Assessment: No
- Group Work: No
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Report | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Report | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Report | 100% |
Repeat Information
Repeat type: Internal & External