Introductory description

The need and ability to gain insights from large data sets using statistical methods, optimisation techniques and predictive models is increasing – often referred to as Business Analytics. In the last 5-10 years, Behavioural Science research has also grown immensely, with many organisations looking to implement behavioural insights as part of their key strategic goals. This module combines these perspectives by considering the analytical techniques managers use today, illustrated by how they have underpinned out increasing understanding of human behaviour.

The module will explore behavioural science insights into consumer and market behaviour, leadership, negotiation, effective team management and organisational culture. The analytics perspective will cover a balance of descriptive, predictive, and prescriptive analytics, involving visualisation, forecasting, data mining techniques and optimisation. This cross-disciplinary training in business models, quantitative methods and data science prepares the manager for today’s work in complex data-driven business situations.

Module web page

Module aims

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.

Lessons 1-5 introduce the two disciplines that are brought together in the module: data sciences and behavioural sciences. Why each has become more important in business today is described and what managers need to know about each. Lessons 6-10 then introduce big data and coding with R. They offer a series of worked examples of how data science is used to underpin data-driven decision-making by managers.

Lesson 1. Introduction: how the module combines the broad field of data analytics with examples of its application in behavioural science. How the module will be assessed.

Lesson 2. Behavioural Science Primer: what you need to know about behavioural science for this module. Core concepts of behavioural science and it’s rise in importance over recent years. Classic and current examples.

Lesson 3. The Evidence behind Behavioural Science: examples of the data and analysis that lies behind popular behavioural science concepts. Examples where conclusive evidence has been hard to obtain.

Lesson 4. Business Analytics Primer: why the discipline of business analytics has risen in importance over recent years. How what we want to know is complex and the data we need to analyse has become vast and diffuse. Core principles and techniques in data-driven decision-making.

Lesson 5. Understanding bias & heuristics: What people do may not be rational and can reflect the biases we all have or heuristics we use. Biases in what we expect, towards risk, in how we learn and how we expect different factors to interact.

Lesson 6: Using R for data science. Introducing data.frames as critical for data science and the elementary building blocks that make R so powerful.

Lesson 7: Machine learning for regression. Key concepts such as overfitting and splitting data into training and test datasets. Models that overfit the noise in training data make worse predictions in new, unseen test data.

Lesson 8: Machine learning for Classification. Making categorical predictions for new test cases.

Lesson 9: Unsupervised learning. Unsupervised learning is different: there is no ground truth with which to supervise learning. We have lots of variables, but no one variable is our target.

Lesson 10: Neural networks and network analytics. Understanding the basic concepts of how to get a neural network to learn and get the intuition behind backpropagation

These lessons feature multiple practical exercises, which will allow students to test and apply your growing coding skills with R.

Learning outcomes

By the end of the module, students should be able to:

• Describe the different types of data available to managers (including relational data)
• Explain the differences between heuristics and biases
• Explain the rules to handling data and appropriate sample selection.
• Distinguish between correlation and causation and demonstrate how this impacts decision making
• Critically evaluate data to inform policy/ operational/ strategic decision making

Indicative reading list

The module provides selected chapters from a series of textbooks, books journal articles online to provide the academic background for the themes covered:

Bazerman, M.H. and Tenbrunsel, A.E. (2012) Blind Spots: Why we Fail to do What's Right and What to do about It. New Jersey: Princeton University Press

Kahneman, D. (2011) Thinking Fast and Slow London: Penguin

Lewis, M. (2003) Moneyball. London: W.W.Norton and Co

Thaler, R. and Sunstein, C. (2008) Nudge: Improving Decisions about Health, Wealth and Happiness. Yale: Yale University Press

Mauboussin, M.J. (2012) The Success Equation: Untangling Skill and Luck in Business, Sport and Investing. Boston: Harvard Business Review Press

Powdthavee, N. (2010) The Happiness Equation: The Surprising Economics of our Most Valuable Asset. London: Icon Books Ltd

Denrell, J. and Liu, C. (2012) 'Top Performers are not the Most Impressive when Extreme Performance Indicates Unreliability' Proceedings of the National Academy of Sciences (USA), 109, pp. 9,331-6

Rosenzweig, P. (2007) 'Misunderstanding the Nature of Company Performance: The Halo Effect and Other Business Delusions' California Management Review, 49, 4, pp. 6-20

Denrell, J. (2005) 'Selection Bias and the Perils of Benchmarking' Harvard Business Review, 83, 4, pp. 114-19

Flyvbjerg, B.; Garbuio, M. and Lovallo, D. (2009) 'Delusion and Deception in Large Infrastructure Projects: Two Models for Explaining and Preventing Executive Disaster' California Management Review, 51, 2, pp. 170-93

Moore, D. A.; Oesch, J. M. and Zietsma, C. (2007) 'What Competition? Myopic Self-focus in Market-entry Decisions' Organization Science, 18, 3, pp. 440-54

Neale, M.A. and Bazerman, M. H. (1985) 'The Effects of Framing and Negotiator Overconfidence on Bargaining Behaviors and Outcomes' Academy of Management Journal, 28, 1, pp. 34-49

Boddy, C.R. (2011) 'Corporate Psychopaths, Bullying and Unfair Supervision in the Workplace' Journal of Business Ethics, 100, 3, pp. 367-79

Eubanks, D.L. and Mumford, M.D. (2010) 'Destructive Leadership: The Role of Cognitive Processes' in Schyns, B. and Hansbrough, T. (eds) When Leadership goes Wrong: Destructive Leadership, Mistakes and Ethical Failures Charlotte, NC: Information Age Publishing

Hunter, S.T.; Tate, B.W.; Dzieweczynski, J.L. and Bedell-Avers, K.E. (2011) 'Leaders Make Mistakes: A Multilevel Consideration of Why' The Leadership Quarterly, 22, 2, pp. 239-58

Liu C et al (2017) ‘Strategizing with Biases: Engineering Choice Contexts for Better Decisions’ Academy of Management Annual Meeting Proceedings (1):10095

Preis, T.; Moat, H.S.; Stanley, H.E. and Bishop, S.R. (2012) 'Quantifying the Advantage of Looking Forward' Scientific Reports, 2, 350

Preis, T.; Reith, D. and Stanley, H.E. (2010) 'Complex Dynamics of our Economic Life on Different Scales: Insights from Search Engine Query Data' Phil. Trans. R. Soc. A, 368, 5, pp. 5,707-19

Research element

Students will engage with a body of knowledge, forming critical opinion of suitability and applicability, and balancing different perspectives. Students will learn to be evidence-based, seeking rigour, reliability and repeatability in any analysis they undertake.

Subject specific skills

Evaluate the most appropriate solution for managing missing data/incomplete datasets

Employ a data visualisation tool/application to simplify the handling of data

Choose appropriate data presentation methods to maximise communication of results

Prepare time series analysis/ panel data analysis

Transferable skills

Written communication