Full-time Master of Business Analytics
Our degree for aspiring data professionals, with a focus on personal skills as well as technical expertise.
Businesses that can make sense of the flow of information in today's world have a competitive edge.
To succeed in this environment, our Full-time Master of Business Analytics teaches students to become trilingual – fluent in the languages of technology, mathematics and business.
Through an intensive one-year program, you will learn how to define and structure business problems, use data to provide insight and communicate those insights to senior leaders. A personal effectiveness component will also develop your skills in areas such as teamwork, negotiations and ethical decision-making.
Graduates of this program are developed to be exceptional from both a technical and business perspective. They go on to work for organisations including Apple, Amazon, Woolworths, Suncorp and Microsoft.
#1 Master of Business Analytics in Australia
Melbourne Business School
QS World University Rankings, 2023
#1 University in Australia
Times Higher Education, 2022
People come to Melbourne Business School because they want to study with the best.
We attract some of the brightest academics from around the world, who teach in class sizes much smaller than their colleagues overseas and are accessible to you throughout your studies.
Our campus is just four tram stops from the centre of Melbourne and has vibrant communal areas with accommodation available nearby.
We offer opportunities for self-development via a wide range of electives, co-curricular activities, and host regular industry networking events.
Studying the Full-time Master of Business Analytics has really helped me bridge the business and technical sides to deliver answers more clearly.
Senior Data Analyst, KPMG
Subjects and Structure
Introduction to Business Problems
This subject is the introduction to the Master of Business Analytics. It focuses on two issues: (i) introduce business problems, best addressed with analytics, and their complexities, and (ii) the complexities of possible solutions. A broad survey of business frameworks and perspectives are covered in this module to help set the context for the business problems encountered. Team processes will be examined, and project management tools provided, to implement the proposed solutions.
During the module, students will also attend sessions on foundational concepts in maths, statistics, programming and SAS to ensure that all background material required for Module 2 has been reviewed.
Students will be presented with a dataset and a case study of an organisation, facing a significant business problem. Students will be asked to prepare possible solutions to the problem, which will be revisited in the Business Analytics Applications subject at the end of their program of study. Assessment in this subject will focus on the team processes and project management tools applied to this case study.
Business Analytics Foundations
This subject equips students with the foundations and tools needed for a career in Business Analytics. The subject has five distinct components:
Computing and Programming for Business Problems
Solving problems in business often requires computer programming to manipulate, analyse, and visualise data. This component helps students, with little or no background in computer programming, learn how to design and write programs, using a high-level procedural programming language, and to solve problems, using these skills. Topics such as cyber security, cyber ethics and privacy, regarding the collection of individual data, will also be discussed.
Data warehouses are designed to provide organisations with an integrated set of high-quality data to support decision-makers. They should support flexible and multi-dimensional retrieval and analysis of data. Topics covered include data warehousing and decision-making; data warehouse design; data warehouse implementation; data sourcing and quality; online analytical processing (OLAP); dashboards; data warehousing for customer relationship management; and case studies of data warehousing practice.
With the explosion of available data, statistical learning, which refers to the analysis of complex datasets, has become an important field in many business contexts, including marketing, finance, and even human resource management. The aim of this component, and the follow-on component in Advanced Business Analytics, is to help students learn how to extract relevant information from large amounts of complex data to make improved business decisions. Topics covered in this component include data exploration; resampling methods; linear and nonlinear regression; parametric classification techniques; and model selection.
Decision Making and Optimisation
There are an assortment of mathematical methods to obtain efficient solutions to a large variety of complex business problems. This component helps student formulate a business problem as a mathematical model and then use computational techniques to estimate and solve the model. Topics covered may include decision-making under uncertainty, optimal location allocation of resources in business processes, decision trees, linear programming, integer linear programming, and Monte Carlo simulations.
Advanced Business Analytics
This subject equips students with the advanced models, methods and tools required for a deep understanding of the latest analytic techniques. The subject has five distinct components:
This component builds on the material in Statistical Learning and covers advanced analytic methods. It extends the statistical-learning component of Foundations of Business Analytics in three ways. First, new techniques such as tree-based methods and neural networks are introduced. Second, students will be introduced to unsupervised statistical-learning techniques, and third, students will learn how to combine models and techniques to produce ensembles with better predictive capabilities.
Data Analytics models can be used to predict a performance variable. But many business decisions are not about predicting performance per se. They are about choosing the values of key inputs, such as price or advertising spend, to optimise performance. This requires that the effects of the inputs, as coded by the model, are causal. This typically requires further assumptions about how the data was generated.
The gold standard for establishing causality is a randomised experiment, which is becoming more common in business contacts. The course covers basic principles and practice of experimentation from A-B testing to randomised incomplete block designs. All these methods give rise to estimates of causal effects.
Predicting key business and economic variables is increasingly important, as it drives both objective decision-making and improved profitability. This component aims to cover the main methods used to predict business and economic variables, based on historical data. These methods include traditional regression, time series, multivariate and econometric models, as well as emerging methods, such as ensemble forecasts. Both point and density prediction will be considered, along with metrics for the quality of both. Throughout, the focus will be on introducing methods in the context of substantive business and economic problems, using a wide range of prediction methods. The importance of benchmarking different methodologies, and the use of prediction in decision-making frameworks, will also be stressed.
Text and Web Analytics
This component helps students develop an understanding of the key algorithms used in natural-language processing and text retrieval for use in a diverse range of applications, including search engines, cross-language information retrieval, machine translation, text mining, question answering, summarisation, and grammar correction. Topics to be covered include text normalisation; sentence boundary detection; part-of-speech tagging; n-gram language modelling; sentiment analysis; web mining and analysis; network analysis (including social network analysis); and text classification.
This subject involves practical experience for teams of students, working on a real analytics project for an organisation. The five-week project integrates academic learning, practical challenges in implementing data analytics in an organisation, employability skills and attributes, and an improved knowledge of organisations, workplace culture and career pathways.
What type of topics could be covered?
Data analysis on datasets, investigating issues such as:
- Customer churn/loyalty
- Logistics and supply chain
- Forecasting demand
- Optimal product or category portfolio
- Marketing-mix optimisation
- Credit risk
- Employee selection, retention and training
- Analysis of social media or other unstructured data sources
Optimisation of processes, such as:
- Call centre operations
- Logistics and delivery routes
- Allocation of marketing resources across products
- Service delivery
The assessment week will involve the completion of a report for the subject and a project presentation.
Business Analytics Applications
This subject’s primary focus is the application of data analytics in business contexts. Three of subject’s components address common applications of business analytics: Finance Analytics, Marketing Analytics, and Supply Chain Analytics. The business case study, introduced in the Introduction to Business Analytics subject, is revisited so that students can view and find solutions to the same comprehensive business case with the benefit of the knowledge obtained over the course of study. Students will also be introduced to other contemporary applications of business analytics.
Quantitative analytics have become an invaluable part of managing financial institutions, not only for profitability but also for safeguarding the organisation against risk. In this component, students will be applying data-analytic skills to finance applications. Topics include financial performance benchmarking; modelling and computation of financial risks; dynamic portfolio management; computational derivative pricing; and modelling fixed income securities. The focus of the component will be on both theoretical development and practical implementation, using contemporary data from the financial market.
It has become increasingly important to know how marketing actions translate into revenue and profit growth. The tools that enable this translation are part of a tool-kit called ’marketing analytics’. Marketing analytics is a technology-enabled and model-supported approach to harness customer and market data and enhance marketing decision-making. This component provides students with (i) knowledge of marketing analytics, (ii) the ability to know which analytics tools to use for which marketing problems, (iii) the ability to use those tools to solve marketing problems, and (iv) the ability to influence marketing outcomes such as satisfaction, choice, loyalty, word of mouth, and customer referrals.
Supply Chain Analytics
Rapid advancements in technology (particularly the internet), combined with fast and cheap computing power, has enable firms to radically transform their industries by developing business models and re-engineering their supply chains. This component provides students with (i) knowledge of mathematical modelling and analytic tools, relating to logistics and supply chain optimisation problems, (ii) the ability to use these tools and techniques to analyse strategic, tactical and operational decisions, pertaining to inventory management, facility location, logistics and other supply chain, management-related decisions, and (iii) exposure to real world logistics and supply chain decisions through case studies.
Business Case Study
This component revisits the case study examined in the subject Introduction to Business Problems earlier in the course. The primary goal of this component is to use the analytics knowledge and skills obtained throughout the course to recalibrate solutions to the business problem in the case study. The secondary goal is to introduce students to some emerging applications in the form of a special-topics component. These topics will vary, depending on emerging trends.
Career Management Centre
Our Personal Effectiveness Program will help you build the soft skills, knowledge and attributes you need to compete and succeed in every job market.
By partnering with leading organisations, our Careers Management Centre can connect you to top-tier firms in Australia and around the world.
Our career coaches will help you develop your job-hunting skills, maximise your future opportunities and increase your chances of success.
Meet With Us
Let us answer your questions face-to-face.
The best way to find out more about studying at Melbourne Business School is to speak with us. We can meet with you one-on-one at our Carlton Campus, or set up a video conference for an online face-to-face conversation.
We can assist you with information on subjects and study materials, campus life, funding your course or any other areas you might be interested in.
If you're interested in studying at Melbourne Business School, please join us for an online information session or contact us to arrange a time that suits you.
The program fee for our Full-time Master of Business Analytics program is AUD $70,400 (2024) with fees paid per module. Please be advised that there is no option for part-time study or credit transfers.
FEE-HELP is available for those who meet the eligibility criteria.
- Please budget for Study visa fees and charges.
You must also obtain Overseas Student Health Cover (OSHC) for the duration of your stay in Australia. Melbourne Business School has selected a preferred OSHC provider. Payment is required as part of the acceptance process.
The costs are:
- AUD $831* (Singles cover)
- AUD $2,953* (Couples/Single Parent cover)
- AUD $4,967* (Family cover)
*premiums quoted as at November 2022.
We have a wide range of scholarships available to support your study at Melbourne Business School. Our scholarships encourage diversity, provide opportunity and reward talent.
You will automatically be considered for a scholarship when you apply – but to be eligible for some, you need to apply by a certain date.
For more information, visit our Scholarships page.
How to Apply
- Commence an application to become familiar with the application process.
- Meet with us to find out more about the School and program.
- Complete and submit your application by the application closing dates.
Shortlisted candidates may be invited to attend a compulsory interview, which can be conducted online or in person. The interview will last approximately 20 to 30 minutes.
Applications will be processed as soon as all materials are received. We strive to respond to applications as quickly as possible, usually within four weeks of receipt. Successful applicants will receive a formal Letter of Offer. The offer may include the award of a scholarship. Unsuccessful applicants are notified in writing.
Credit and Substitution
Due to the high level of integration of disciplines and program delivery, it is not possible for the School to grant subject credits for students based on prior learning.
2024 closing dates for our Full-time Master of Business Analytics program are as follows:
Round 1: 24 April 2023
Round 2: 19 June 2023
Round 3: 21 August 2023 (final off-shore international closing date)
Round 4: 23 October 2023 (final on-shore and domestic closing date)
The deadline for all applications is 11.59pm AEST.
Late applications may be considered on a case-by-case basis. Please contact us for further advice.
Outcomes are provided approximately 6-8 weeks after the application deadline.
For a successful application, you will need:
- An up-to-date CV (work experience is not mandatory).
- A copy of your undergraduate academic transcript.
- A passport or verified document showing current citizenship / residency status.
- An excellent command of English, with a copy of your IELTS or TOEFL score if you are a non-native speaker**.
**IELTS: Academic English test with a minimum score of 7.0 overall and with no individual band less than 7.0.
TOEFL iBT, with a minimum score of 94 (written score minimum 27 and no individual score lower than 24)
PTE: Overall score minimum of 65+, with writing skills of minimum 65 and no other communicative skill below 65
Meeting these requirements does not guarantee selection.
It is a university requirement that applicants provide evidence that they meet the published entry requirements. Uncertified documentation does not provide this evidence; however we accept uncertified documents for the purpose of selection, and reserve the right to request your original certified documentation at any time.
You will have an undergraduate degree from a recognised institution, with:
- A minimum weighted average mark (WAM) of 65. As a guide, a typical student will have a WAM of greater than 70.
- Typically your undergraduate degree major or specialisation may be in either Commerce, Mathematics and/or Physics, Computer Science or Information Systems or Engineering. Given the heavy quant-based nature of the program, the primary focus will be on a student’s quantitative abilities.
- Demonstrated academic success in quantitative subjects
- A completed sequence of two or three undergraduate or graduate statistics courses that include probability theory and regression analysis, although demonstrated mathematical preparation and quantitative aptitude may be considered sufficient in some cases.
The Australian Department of Home Affairs is responsible for issuing visas for entry to Australia. Please refer to the department's immigration and citizenship webpages for information about visas.
- An undergraduate degree with a relevant major
- A WAM of 65 or higher
- English proficiency
For more information, visit our application FAQ's page.
Fill out the form below with details of your enquiry and our team will respond to your request within three business days,