Introductory description

The module provides an opportunity to pursue a more advanced area of analytics (building on the core module in Business Statistics). Today, organisations and businesses collect and store information in data warehouses, and such information is available to be ‘mined' for improved management decision making. Some of that information can be analysed with simple statistics, but much of it requires more complex, statistical techniques. The module thus focuses on the use of a range of applied advanced data analysis techniques to convert information into knowledge.

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.

This course is designed to provide students with a working knowledge of the basic concepts underlying the advanced data analysis techniques, with an overview of actual applications in various fields, and with experience in actually using such techniques on a problem of their own choosing. The course will address both the underlying mathematics and problems of applications. The types of tools to be examined include: Dimension reduction, clustering, Neural Networks/ Deep learning, causal analysis, and A/B testing.

Learning outcomes

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

• Demonstrate a comprehensive understanding of the different advanced data analysis techniques, and how they are applied in practice
• Critically evaluate the relevant issues and measures available to aid selection of the most suitable models.

Indicative reading list

Wheelan, C. (2014). Naked Statistics: Stripping the Dread from the Data. WW Norton & Co
Hair, J.F. et al (2018). Multivariate Data Analysis, 8th ed. Pearson International Edition.
Cunningham, S.(2021). Causal Inference: The Mixtape, Yale University Press
Pearl, J & Mackenzie, D. (2018) The Book of Why: The New Science of Cause and Effect, Penguin Books
Hernan, M.A. and Robin J. M. (2020) Causal Inference: What If, CRC Press.
Aggarwal, C.C.(2019). Neural Networks and Deep Learning, Springer.
Panda ,S.K. et al(2022) Artificial intelligence and machine learning in business management : concepts, challenges, and case studies, CRC Press.
James, G. et al (2021). An Introduction to Statistical Learning: With Applications in R, 2nd ed. Springer.

Subject specific skills

Evaluate and apply advanced data analysis techniques to real data sets using appropriate software.
Investigate and interpret real data sets using modelling
Employ appropriate techniques according to the research context, data type and research question

Transferable skills

Demonstrate numerical and IT skills
Demonstrate problem solving skills
Demonstrate group working skills