Part-time Master of Business Analytics
A flexible business analytics degree for busy professionals with study options that make it easier than ever to get started.
Accelerate your career or change direction with an analytics degree that fits around you.
Our Part-time Master of Business Analytics is delivered in a hybrid format by our world-class faculty, providing flexibility to attend classes in person or online.
Designed for highly motivated and experienced professionals, it will equip you with the practical data science skills needed to make an immediate impact in your career and grow your earning potential.
You can choose to start with a shorter commitment by studying a Professional Certificate, Graduate Certificate or Graduate Diploma - or dive right into the Master of Business Analytics itself.
#1 Master of Business Analytics in Australia
Melbourne Business School
#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 Master of Business Analytics has really helped me bridge the business and technical sides to deliver answers more clearly.
Senior Data Analyst, KPMG
What You'll Study
Students will complete 10 core subjects and one capstone subject.
It is recommended that students complete all three accelerators prior to commencement.
Mathematics, Statistics and Programming
The subjects within the Master of Business Analytics are technical and assume rudimentary background knowledge in mathematics, statistics and programming. And while most applicants will have reasonable working knowledge in one or more of these areas, we appreciate that some may also be ‘rusty’ with some of this content. We therefore recommend that any applicant undertaking subjects requiring such preliminary knowledge attend the accelerators.
The accelerators run over 2-3 days prior to the beginning of the course and establish the fundamentals in mathematics, statistics and programming that are called upon in many of the subjects within the masters.
Content covered in the accelerators may include concepts within the following areas:
Mathematics: Calculus and Linear Algebra
Statistics: Probability, Distributions, Sample Statistics and Inference
Programming: Variables, Booleans, Functions and Loops
The accelerators run over 2-3 days prior to the beginning of the course and establish the fundamentals in mathematics, statistics and programming.
Business Data Platforms
Data warehouses are designed to provide organisations with an integrated set of high-quality data to support decisionmakers. 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.
Coding 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, cyberethics and privacy, regarding the collection of individual data, will also be discussed.
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.
Statistical Learning for Business
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.
Machine Learning and AI for Business
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.
Causal Analytics for Business
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.
Predictive Business Analytics
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 Analytics for Business
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.
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 the toolkit 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 of Module 5 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.
Applied Business Analytics
To gain practical experience, students will work on a data project with a group. The 10-week project integrates academic learning and practical challenges in implementing data analytics in an organisational context. While undertaking this project, students will examine the ethics surrounding data and data-based decision making and undertake a practical introduction to workplace negotiations.
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.
Individual Research Project
Capstone Subjects - Select one of two options.
Meet With Us
Let us answer your questions.
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.
Preparing for success begins the day you enrol at Melbourne Business School.
In addition to your regular classes, our Career Services provides coaching sessions, industry events, resume reviews, LinkedIn training and mock interviews.Learn more
The 2023 program fee is AUD $4,650 per subject (12.5 credit points equivalent)
Fees are paid per subject, per term in advance*.
FEE-HELP is available for those who meet the eligibility criteria.
*Fees are subject to annual review and may be adjusted to take into account cost increases. The School guarantees that fees will not increase by more than 10 per cent from year to year.
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. Unsuccessful applicants are notified in writing.
Closing dates are as follows:
Round 1: 21 October 2022
Round 2: 9 December 2022
Round 3: 10 February 2023
Round 4: 7 April 2023
Round 5: 19 May 2023
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 showing at least two years work experience.
- A copy of your academic transcripts.
- 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 of 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
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 certified documentation at any time.
You will have an undergraduate degree from a recognised institution, with:
- A minimum weighted average mark (WAM) of 65%.
- Your major may be one of: Mathematics, Statistics, Actuarial Science, Computer Science, Information Systems, Engineering, Physics, Finance, Economics or Science.
- 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.
This program is only open to domestic students (Australian/NZ citizens, Australian PR visa holders or any Australian visas without study restrictions).
- Two years work experience
- An undergraduate degree with a relevant major
- A WAM of 65% or higher
- English proficiency
For more information, visit our application FAQ's page.
This program requires orientation and accelerator classes prior to the commencement of term.
Further details regarding dates and times will be provided upon successful completion of your application.
Jump straight into our Part-time Master of Business Analytics, or start with a shorter commitment first and build towards further studies later.
Equip yourself with essential technical skills for becoming an effective data analytics practitioner.
Develop core business analytical techniques while continuing to work, applying what you learn on the job.
Gain a complete analytics toolkit that will develop your understanding of how to approach data-related business challenges with confidence.
A flexible business analytics degree for busy professionals with study options that make it easier than ever to get started.
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