Melbourne Business School Degree Programs Master of Business Analytics

Master of Business Analytics

Our degree for aspiring data professionals, with a focus on personal skills as well as technical expertise.

CRICOS Code: 084058J

Businesses that can make sense of the flow of information in today's world have a competitive edge.

Our Master of Business Analytics will teach you how to use data analytics to solve a variety of business problems and give you the personal skills to explain those solutions to others.

With an intensive one-year program, you will learn how data can drive business decisions through statistical and quantitative analysis, explanatory and predictive modelling and fact-based management.

You will also improve your personal effectiveness with individual support and training in the areas of communication, presentation, networking and career development.

Student Experience

People come from all over the world to Melbourne Business School because they want to study with the best.

People come from all over the world to Melbourne Business School because they want to study with the best.

We attract some of the brightest academics in the field from institutions like Yale, Stanford and INSEAD, who teach in class sizes much smaller than their colleagues overseas.

Our campus is just four tram stops from the centre of Melbourne and has vibrant communal areas and accommodation for students who stay on-site during their classes.

We have a number of student clubs dedicated to areas including marketing, consulting and finance, and host regular industry networking events.

Success Story

Studying the Master of Business Analytics has really helped me bridge the business and technical sides to deliver answers more clearly.

EMMELINE WU
Senior Data Analyst, KPMG

Read more

Subjects and Structure

Master of Business Analytics - Course structure

Modules

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 Warehousing
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.

Statistical Learning
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:

Machine Learning
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
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 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 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.

Analytics Lab

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
  • Schedules
  • 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.

Finance 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.

Marketing Analytics
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.

Want to take a deeper dive?

Download the brochure to find out more about studying a Master of Business Analytics at Melbourne Business School.

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Career Management Centre

Students participating in the personal effectiveness program

Our Personal Effectiveness Program will help you build the soft skills, knowledge and attributes you need to compete and succeed in every job market.

Recruitment Partners

By partnering with leading organisations, our Careers Management Centre can connect you to top-tier firms in Australia and around the world.

Individual coaching session at the hub

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.

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. 

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Investment

Program Fee

The 2020 program fee for our Full-time Master of Business Analytics program is AUD$55,000, with fees paid per module. Please be advised that there is no option for part-time study or credit transfers.

FEE-HELP

FEE-HELP is available for those who meet the eligibility criteria.

Other Costs

International students:

  • Please budget approximately $560 for visas.
  • You must also take out medical insurance for the duration of your stay in Australia. The cost is approximately $729 per year (based on 2018 prices; more for a couple or family) and is paid with your first fee instalment.

Scholarships

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

Application process

Apply
  1. Commence an application to become familiar with the application process.
  2. Meet with us to find out more about the School and program.
  3. Complete and submit your application by the application closing dates.

 

Interview

Shortlisted candidates will be invited to attend a compulsory interview, which can be conducted in person, over the phone or by teleconference. The interview will last approximately 20 to 30 minutes.

 

Offer

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.

Application deadlines

Closing dates for our Master of Business Analytics program are as follows:

Round

Application deadline

Round 1

31 March

Round 2

31 May

Round 3
(international closing date)

31 July

Round 4
(domestic closing date)

15 October

The deadline for all applications is 11.59pm AEST.

Application requirements

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.
  • The names and contact details of two professional referees.

          

Academic Requirements

You will have an undergraduate degree from a recognised institution, with:

  • A minimum weighted average mark (WAM) of 70%.
  • Your major will 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.


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.

*For IELTS, you will need a minimum score of 7.0 overall, with no individual score less than 6.5. For TOEFL iBT, you will need a minimum score of 102, with a written score minimum of 24 and no individual score lower than 21.

Entry Requirements

  • A university degree with a relevant major
  • WAM of 70% or higher
  • Two professional referees
  • English proficiency

Apply Now

 

January 2021
Round 1 applications close 31 March 2020
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