About the course

This module will be held at Melbourne Business School in September 2026

The module provides an introduction to data analysis for managerial decision-making. Students will be familiarized with the tools of data analysis, develop the necessary skills for analytical thinking and a quantitative mindset. Students will also familiarize themselves with commonly used terminology in analytics, and the concepts behind these terms.

A key objective of this subject is to show students how to translate data (a collection of numbers) into information (a meaningful summary of the data), with an appreciation that the information is obtained with inherent uncertainty. Consolidations of the technical concepts are undertaken via cases set within business contexts.

The subject introduces the concept of a data distribution, and how the variability in the data distribution impacts the summary statistics that are commonly used in practice. The concept of statistical significance is introduced, and how it impacts on the conclusions that are drawn from statistical summaries: are they real?

Regression analysis will also be introduced as a powerful tool to examine the strength and nature of the complex relationships between various business variables of interest. The use of regression for predicting business quantities and the quantification of uncertainty around such predictions is also highlighted.

AI and large language models will also be introduced from a somewhat high, but technical level. This is aimed to provide students with an insight into how LLM’s such as ChatGPT work and why/how they fail at certain tasks. Summary information on uses of AI in business automation will also be discussed.

Objectives

The module’s purpose is to improve the participants’ understanding of:

Analyse and summarise multivariate data clearly using Excel
Apply the principles of statistical variation in data analysis
Select appropriate performance metrics based on statistical principles
Combine multiple performance metrics quantitatively
Distinguish between correlation and causation in statistical analyses
Construct relevant statistical models from ambiguous business problems
Undertake regression analysis to quantify complex relationships between multiple explanatory variables and a response variable
Identify elements from regression output that are directly relevant to a business problem or question.
Identify and model nonlinear effects and interactions in regression models
Measure and articulate statistically significant relationships between variables
Apply quantitative methods and analyses to identify optimal decision strategies and risks
Evaluate the robustness and appreciate the limitations of data analyses

Fees

AUD$6,900
Please note fees include tuition, materials, accommodation and meals. This course will be a residential module

Applications

Unless you have previously enrolled, you will need to enroll with the Melbourne Business School to complete the module. To do this, download and complete an Application Form and return to FEAL with the supporting documentation. Please note new enrolments can take 2-3 weeks for processing.

If you have previously enrolled with the Melbourne Business School - either for the FEAL/MBS Masters Program or any other course - you do not need to complete the Application Forum again. Please email Katrina Bacon, CEO, FEAL katrina.bacon@feal.asn.au to advise your intention to complete this module.

Taught by

Simon Holcombe, Melbourne Business School

About the lecturer

Simon Holcombe is the Academic Director of the Masters of Business Analytics at Melbourne Business School. He has over ten years’ experience in industry utilising a range of analytics in the design and administration of infrastructure within the financial services sector, during which time he worked on Bayesian filters in email and various banking channels. With a passion for education he has spent over 20 years teaching in tertiary and secondary schools, including data analysis in the executive MBA at Monash university and the Melbourne Business School.

Simon’s expertise is in mathematical-physics where he has made contributions to the field of condensed matter physics, particularly the theoretical modelling of charge diffusion in the surface layers of dielectrics. He has published articles in the Journal of Physics, Physica, AMM, ZAMP and has most recently published in the field of number theory where he formulated a general method for evaluating trigonometric sums and showed, surprisingly, that a simple classical system can display a quantisation of energy.

Data Analysis - Analytics, Uncertainty and AI