Business Analytics - MAN00183M

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Module will run

Occurrence	Teaching period
A	Semester 2 2025-26

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

Background Knowledge: Camm, J. D., Cochran, J.J., Fry, M. J., Ohlmann, J. W. (2024). Business Analytics (5th Edition). Cengage Learning. Steif, K. (2021). Public Policy Analytics: Code and Context for Data Science in Government. Chapman & Hall/CRC Data Science Series. Bertsimas, D, O'Hair, A., & Pulleyblank, W. (2016). The Analytics Edge. Dynamic Ideas. Hillier, F. S. & Lieberman, G. J. (2024). Introduction to Operations Research (12th Edition). McGraw Hill. Ghiani, G., Laporte, G., & Musmanno, R. (2022). Introduction to Logistics Systems Management with Microsoft Excel and Python Examples (3rd Edition). John Wiley & Sons, Inc. Mishra, A., & Mishra, H. (2024). Business Analytics Solving Business Problems with R (2nd Edition). Sage. Mount, G. (2021). Advancing into Analytics: From Excel to Python and R. O’Reilly. Wickham, H. & Grolemund, G. (2023). R for Data Science (2nd Edition). O’Reilly. McKinney, W. (2022). Python for Data Analysis (3rd Edition). O’Reilly. VanderPlas, J. (2022). Python Data Science Handbook: Essential Tools for Working with Data (2nd Edition). O’Reilly. Géron, A. (2023). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly. Descriptive Analytics: Anderson, D. R., Williams, T. A., & Cochran, J. J. (2019). Statistics for Business & Economics. Cengage Learning. Berenson, M. L., Levine, D. M., & Szabat, K. A. (2015). Basic Business Statistics: Concepts and Applications. Boston: Pearson Paksoy, T., Kochan, C. G., & Ali, S. S. (2020). Logistics 4.0: Digital Transformation of Supply Chain Management. CRC Press. Bruce, P., Bruce, A., Gedeck, P. (2020). Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. O’Reilly. Predictive Analytics: Provost, F. & Fawcett, T. (2013). Data Science for Business. O'Reilly Media. Ragsdale, C. T. (2015). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. Cengage Learning. Robertson, P. W. (2021). Supply Chain Analytics: Using Data to Optimise Supply Chain Processes. Taylor & Francis Group. Hodeghatta, U. R., & Nayak, U. (2023). Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-Driven Approach (Second Edition). Apress. Prescriptive Analytics: Daskin, M. S. (2021). Bite-Sized Operations Management. Morgan & Claypool Publishers. Cachon, G., & Terwiesch, C. (2019). Matching Supply with Demand (4th Edition). McGraw Hill Education. Hopp, W. J. & Spearman, M. L. (2008). Factory Physics (3rd Edition). Waveland Press. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2021). Designing and Managing the Supply Chain (4th Edition). McGraw Hill Education.