Business Analytics - MAN00183M

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Module will run

Occurrence	Teaching period
A	Semester 2 2026-27

Module aims

Business analytics refers to the ways in which organisations such as businesses, non-profits, and governments can use data to gain insights and make better decisions. Business analytics is applied in operations, supply chains, logistics, marketing, finance, and strategic planning among other functions. With the growing availability of data and improved computational power, the ability to use data effectively to drive rapid, precise and profitable decisions has been a competitive advantage for organisations. It is crucial to understand which analytical techniques are employed and how they are applied to improve organisational performance. Students will learn through practical examples and business case studies.

Module learning outcomes

By the end of this module, students should be able to: - Explore the current digital technologies available to organisations to solve problems across functions. - Critically analyse the role of data science, machine learning, and artificial intelligence across various organisations in the value chain. - Develop strategies for leveraging data science, machine learning, and artificial intelligence in various decision-making contexts. - Select the most appropriate (descriptive, predictive, and/or prescriptive) analytics technique(s) for solving specific business problems. - Make evidence-based recommendations to organisations, drawing on insights from business analytics.

Module content

This module is divided into three branches of business analytics: (1) descriptive analytics – using data science to summarise business data to extract insights that inform decision-making and strategic planning, e.g., data aggregation, data visualisation, and statistical analysis; (2) predictive analytics – data mining and machine learning methods applied to business issues, e.g., classification trees, K-nearest neighbours, and hierarchical clustering; (3) prescriptive analytics – using analytical models with available business data to recommend the best or most optimal decisions.

Indicative assessment

Task	% of module mark
Essay/coursework	70.0
Oral presentation/seminar/exam	30.0

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Essay/coursework	70.0
Essay/coursework	30.0

Module feedback

Feedback will be given in accordance with the University Policy on feedback in the Guide to Assessment as well as in line with the School policy.

Indicative reading

Core textbook
Camm, J.D., Cochran, J.J., Fry, M.J. & Ohlmann, J.W. (2020) Business Analytics, 4th edn. Boston, MA: Cengage.

Recommended readings
Goodwin, P. & Wright, G. (2004) Decision Analysis for Management Judgment, 3rd edn. Chichester: Wiley.
Mishra, A. & Mishra, H. (2024) Business Analytics: Solving Business Problems with R, 2nd edn. London: Sage.
Provost, F. & Fawcett, T. (2013) Data Science for Business. Sebastopol: O’Reilly.
Ragsdale, C. (2016) Spreadsheet Modelling & Decision Analysis: A Practical Introduction to Business Analytics, 8th edn. Boston: Cengage Learning.
Wickham, H. & Grolemund, G. (2023) R for Data Science, 2nd edn. Sebastopol: O’Reilly.