Major structure
Overview
Students will learn about core techniques deployed to identify, evaluate and resolve complex business and economics problems. This major is a strong complement to other existing majors within the Bachelor of Commerce.
Your major structure
In your first year, you will complete a compulsory core alongside 4-6 foundation subjects to build your fundamentals, as well as electives and breadth subjects. You'll also commence a sequence of subjects to fulfill the Bachelor of Commerce quantitative requirements.
In your second and third year, you will complete 5 or more business analytics subjects to realise your major, plus a combination of elective and breadth subjects.
Double major
Students can complete a double major by combining any two Commerce majors within the standard 300 credit points of the degree (no extra time required).
Specialisations
From 2026, you can add an optional 50 credit points of learning as specialisations within your major to deepen your studies. The specialisation currently available to the Business Analytics major is:
- Ethics and Sustainability
The Mathematics Pathway
Students can elect to complete the quantitative requirement for the BCom via either the standard or mathematics pathway. The mathematics pathway includes two level 2 quantitative subjects and their prerequisites, as opposed to the standard pathway’s one level 1 and one level 2 quantitative subject requirement.
Students who complete the Business Analytics major and who are wanting to develop more advanced technical analytic skills are encouraged to consider the mathematics pathway.
Breadth subjects
The Melbourne curriculum allows you to incorporate breadth subjects into your degree. This gives you the chance to explore subjects or disciplines outside of commerce and business analytics.
Use breadth to explore creative interests or topics you have always been curious about; or complement your business analytics major and career development with a language, communication skills or technological expertise.
If you have a professional graduate program in mind, such as the Juris Doctor (Law) or Master of Engineering, you can use your breadth subjects to develop your knowledge and meet prerequisites from day one. Breadth subjects can give you the flexibility to qualify for graduate study or explore interests in a field that’s very different from your major.
Concurrent Diploma (Domestic students only)
Students completing the Business Analytics major in the Bachelor of Commerce can also complete a Concurrent diploma alongside their degree. These diplomas allow you to broaden your studies and enhance your employability. Concurrent Diplomas are offered in:
- Computing,
- Mathematical Sciences,
- Music, or
- Languages.
Equivalent to a major from a different faculty (100 points of study) they add a year to the usual full-time study load, but with approval can be completed within 3.5 years with cross-crediting (of up to 50 points) and/or overloading.
Sample course plan
View some sample course plans to help you select subjects that will meet the requirements for this major.
| Accordion | |
|---|---|
Year 1100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
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| Semester 1 · 50 pts |
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| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 3100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts |
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| Accordion | |
|---|---|
Year 1100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts |
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| Accordion | |
|---|---|
Year 3100 pts |
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| Semester 1 · 50 pts |
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| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 1100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 3100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 1100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 2100 pts |
|
| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
| Accordion | |
|---|---|
Year 3100 pts |
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| Semester 1 · 50 pts | |
| Semester 2 · 50 pts | |
Explore this major
Explore the subjects you could choose as part of this major.
Students completing this major are required to complete the Level 1 pre-requisite subject as part of the foundation requirement before commencing the Level 2 major required subjects.
Prerequisite
| Accordion | |
|---|---|
| Foundations of Business Analytics · 12.5 pts |
This subject provides students with a foundational understanding of business needs and technology trends driving investment in business analytics and big data technologies, challenges in data governance and ethical challenges associated with integrating and managing business analytics as a core business capability. Drawing on a diverse set of real‐world applications and contexts (e.g., HRM, operations, marketing, finance analysis, corporate strategy and policy analysis), students will explore fundamentals associated with managing data acquisition, development of large data bases, choice of analytic methods, and tools and implementation in a broad set of business and policy‐based contexts. Through these case studies, students will develop their understanding of the applications of business analytics as well as the social and ethical implications of business analytics, an understanding of emerging directions in business analytics applications and the opportunity to familiarise themselves with the basis of different analytics applications used in a variety of business contexts. |
Students completing this major are required to complete the Level 1 pre-requisite subject as part of the foundation requirement before commencing the Level 2 major required subjects.
Prescribed
Students must complete the following subjects:
| Accordion | |
|---|---|
| Visualisation and Data Wrangling · 12.5 pts |
This subject provides an introduction to the essential skills for the management of business data to enable the application of analytics and evidence-based decision-making in business. It entails the study of the principles and tools for business data management and modelling. The focus of the subject is enabling business decision-making, and includes consideration of effective presentation and reporting of business data. Data sets will be drawn from multiple industries and business disciplines. |
| Critical Thinking with Data · 12.5 pts |
This subject teaches students to become critical interpreters and users of data-based evidence. Future data scientists working across many disciplines will develop skills in identifying the strengths and weaknesses of arguments based on quantitative evidence, and learn to evaluate reasoning that uses probabilistic ideas and the results of statistical analysis. They will develop skills in interpretation, principled reporting and communication of statistical evidence. Data-based evidence is foundational in all of science. The growth of “big data” and interest in data science has accentuated the need for a well-developed understanding of how scientific studies are designed, analysed and communicated. The media, other academic research and many aspects of everyday life also use and build on data presented and processed in many different ways. The subject examines methods of judging the quality of data-based evidence, and the strength of conclusions drawn from it, including concerns in establishing causality. It provides students with frameworks for evaluating study quality and deals with quantifying uncertainty in conclusions, describing how data-based evidence can contribute to the accumulation of scientific knowledge. The subject emphasises the skills needed to use, interpret and communicate statistical and data science related ideas and findings in real world contexts. |
Students completing this major are required to complete the Level 1 pre-requisite subject as part of the foundation requirement before commencing the Level 2 major required subjects.
Capstone
Students must complete the following subject:
| Accordion | |
|---|---|
| Business Analytics Challenge · 12.5 pts |
This capstone subject for the Business Analytics major provides students with an opportunity to demonstrate and apply a framework for a business analytics project. Students can choose to work as part of a group with an industry partner to address a complex problem or challenge the organisation faces, or on an individual analytics project. The project will enable students to work with a range of structured and/or unstructured data sources in which different analytics techniques might be applied to address an organisational or policy challenge. Over the course of the subject students will be provided with guidance on project planning and design, techniques of identifying and appropriating relevant data, and application of analytic approaches learned over the course of the major. The emphasis of the subject is on translating the concepts, models and theory derived from business and economics disciplines to frame a business or policy challenge, design an analytics project and apply tools of business analytics in practical situations. |
Group A Elective
Students must complete ONE of the following subjects:
| Accordion | |
|---|---|
| Business Data Governance and Ethics · 12.5 pts |
This subject focuses on challenges associated with the management and regulation of business data systems and analytic practices, including privacy, accountability, cybersecurity, and risk management. Students will also gain a deeper understanding of the ethical challenges and principles associated with data collection, storage and maintenance, application in business intelligence and decision‐making, data governance, and addressing the potential biases associated with algorithmic design and the application of data analytic tools. These issues will be considered as both a business use case and consideration of deeper philosophical and societal challenges associated with the fast‐changing pace of analytic technologies and capabilities faced with the application of generative AI technologies and expanding computational capabilities influencing the application of data science to business decision making. |
| Unstructured Data Analytics for Business · 12.5 pts |
Organisations and government agencies collect large quantities of market and individual data that is inherently unstructured with ill-defined qualitative features – these include text, images, audio and video data. These forms of data are understood as unstructured because of the non-systematic form they take and the fact that their features vary and are ill-defined. In this subject we explore how these forms of unstructured data can be manipulated using different methods for categorisation, visualisation and exploration that help understand and predict behaviour of consumers, employees, suppliers, and other economic actors. Using various cases, the subject explores how different techniques can be employed in different business and economic settings to solve real world problems. |
| Machine Learning & AI for Business · 12.5 pts |
Machine Learning is an integral tool in a business analyst’s arsenal and plays a critical role in predicting a range of outcomes, including consumer behaviour, future performance and other organisational outcomes of interest. In real‐time, data collection and data wrangling are the important steps in deploying machine learning models. This subject covers the application of a range of supervised and unsupervised learning techniques from machine learning in a range of semi‐ or non‐structured decision problems in a data rich environment including senior team decision support, decision optimisation across firm boundaries (e.g., supply change and supplier coordination), customer facing algorithms to support product selection and other decisions. The techniques covered will include decision trees, regression trees, neural networks and clustering methods. Ethical concerns associated with the analysis of business problems using the methods taught in the subject will also be integrated into the subject. |
Group B Elective
Students must complete ONE of the following subjects:
| Accordion | |
|---|---|
| Analysis of Firms & Financial Statements · 12.5 pts |
The subject is a capstone subject in accounting. The subject is designed to teach the underlying concepts and applied contemporary techniques that enable the users of financial reports to assess the performance of a firm, value a firm and evaluate its managers. The course is seen as an extension and integration of both accounting and the also the finance, economics and statistics subjects you studied earlier in your degree. The applied concepts taught will be useful to students in practice both as preparers and as users. The objective of this course is to equip students with both the concepts and techniques to be able to: (a) understand that information is imperfect and be able to understand the determinants of bias and random errors in information (b) use financial reports to assess the level and drivers of firm performance; (c) value firms and (d) consider whether markets are efficient in the use of information and if regulation over the production of external financial reports (and other information) is required. |
| Organisational Performance Management · 12.5 pts |
This subject focuses on a range of strategic performance management control system issues including performance measurement, incentives, reward systems and risk; profit analysis; planning and budgeting; and, strategic investments. These issues are explored in a number of different organisational settings and relationships. |
| Actuarial Analytics and Data I · 12.5 pts |
This subject aims to provide students with basic training on modern data analytics methods, which include linear regression, classification, resampling methods, spline-based methods, generalised additive models and support vector machines. This subject focuses on applying the above methods to modelling non-life insurance claims frequency and severity. |
| Basic Econometrics · 12.5 pts |
This subject examines multiple regression analysis and its use in economics, management, finance, accounting and marketing. Topics will include the properties of estimators, hypothesis testing, specification error, multicollinearity, dummy variables, heteroskedasticity, serial correlation, panel data and methods for discrete dependent variables. These methods are consolidated through a Capstone project involving hypothesis-formation, real-world data collection and writing-up the results in a formal report. |
| Econometrics 2 · 12.5 pts |
Extensions of the multiple regression model are examined. Topics include causal and statistical interpretations of regression models, instrumental variables, panel data and time series regression models and relevant statistical theory. |
| Applied Microeconometric Modelling · 12.5 pts |
This subject examines estimation and testing of microeconometric models based on cross-sectional and panel data and quantitative and limited dependent variables. Illustrative application topics normally will include labour economics, consumer demand and finance. The computer software used is Stata. |
| Time Series Analysis and Forecasting · 12.5 pts |
Normally topics will include current techniques used in forecasting in finance, accounting and economics such as regression models, Box-Jenkins, ARIMA models, vector autoregression, causality analysis, cointegration and forecast evaluation, and ARCH models. |
| Computational Economics and Business · 12.5 pts |
This subject covers the application of computer based techniques to solve the problems encountered in economics and business. The techniques covered include the construction and use of hierarchical data sets, the use of multivariate graphics and statistics in the context of data mining applications, the elements of computer simulations, and the application of linear programming for the analysis of productivity in the context of data envelopment analysis. One aspect of this subject is the introduction of students to different software options. Possible software to be considered will be SAS, Stata, GAUSS, SPSS, TSP, EMS, Scientific Word, and Eviews. |
| Algorithmic Trading · 12.5 pts |
Global equity markets have changed fundamentally over the last decades Regulatory reforms to promote competition for trading services have led to considerable fragmentation of markets. New entrants and new technology have contributed to innovative new trading mechanisms and pricing structures. Today, markets are overwhelming electronic, with trading occurring using algorithms rather than manually. Graduates wishing to pursue careers in financial markets need to understand the new market structure that exists and have skills to understand and implement trading strategies in this environment. This subject will ensure students develop these skills and knowledge, through a combination of lectures and hands-on experience of manual and robot trading in online experimental markets. The class is quite unique. Despite growing importance of computerised trading in financial markets, there exist hardly any finance classes that expose students to the issues, let alone allowing them to develop the skills to conceive robot traders themselves through participation in experimental online markets. |
| Machine Learning in Finance · 12.5 pts |
Machine learning has been revolutionizing the financial industry, offering the potential to disrupt traditional structures and practices. This subject is meticulously organized around several real-world issues to introduce fundamental economic and financial problems and demonstrate how machine learning can provide transformative solutions to these problems. Throughout the course, case studies will be used to illustrate these core concepts, allowing students to see the practical application of theoretical knowledge. The emphasis is on building a strong foundation in machine learning techniques and their financial applications, ensuring that students can confidently apply these concepts in novel situations they may encounter in their professional careers. Additionally, students will engage in hands-on projects and exercises that reinforce the material covered in lectures. This practical approach ensures that they not only understand the theoretical underpinnings but also gain the skills needed to implement machine learning solutions in real-world financial contexts. By the end of the course, students will have a comprehensive understanding of how machine learning can address various economic and financial challenges, preparing them to drive innovation and improvement in the financial industry. |
| Analytics for Supply Chain & Operations · 12.5 pts |
This subject focuses on quantitative models for informing supply chain and operational decisions. It explores techniques for how organisations can more effectively deploy operational and supply chain modelling using analytics to evaluate and analyse real time data to gain insights from them, including: demand forecasting, newsvendor optimal ordering, bullwhip effect and supply chain optimal ordering, optimizing production planning, queuing, and staff allocation. The subject will also equip students with the ability to apply big data analytic techniques and effectively communicating insights from their analyses with non-expert decision-makers and stakeholders (suppliers, customers, managers). |
| Human Resource Analytics · 12.5 pts |
This subject applies the creation, analysis, utilisation, and application of “people-oriented” analytics to understand organisational behaviour at work and support human resource (HR) decision-making processes. Students will learn fundamental principles and philosophical underpinnings of adopting an evidence-based approach to managing human resources and human capital. Throughout this subject, students will develop both quantitative and qualitative analytical skills necessary to analyse HR data effectively, informing decisions in human resource management. Moreover, this subject explores the integration of cutting-edge technologies, such as artificial intelligence (AI) and machine learning, in various HR and people management areas, including recruitment, professional development, performance evaluation, compensation, and succession planning. |