Master of Business Analytics Modules

 


Module 1

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.

Module 2

Foundations of Business Analytics

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.

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 1

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.

Personal Effectiveness Program (PEP)

Delivered every Friday during module 2.

Module 3

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:

Statistical Learning 2

This component builds on the material in Statistical Learning 1 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 Visualisation

Data visualisation reveals the underlying structure of datasets, using representations that utilise the human visual-perceptual system. The topics covered include the algorithms and systems for visually exploring, understanding and analysing large, complex datasets. This includes the visualisation of multivariate, temporal, text-based, geospatial, hierarchical, network and graph-based data.

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.

Personal Effectiveness Program (PEP)

Delivered every Friday during Module 3.

Module 4

Industry Practicum

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.

Here's a LinkedIn post about our students in action during their practicum in August 2015:

Module 5

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 reengineering 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.

Personal Effectiveness Program (PEP)

Delivered every Friday during Module 5.

The Personal Effectiveness Program (PEP)

Analytics professionals with a strong blend of soft and technical skills are in demand. At the conclusion of the PEP, you will have a well-rounded understanding of the link between analytics, business and decision-making. The ability to communicate findings to guide decision-making is just as important as data manipulation for the analytics professional, because C-suite executives can make the wrong decision if good data is communicated to them poorly.

The PEP runs across the course. It identifies specific needs of each individual student and provides ongoing support, training, and opportunities to practise and perfect these skills. The program focuses on four core areas:

  • Communication skills, including effective presentations, verbal communication, written communication, public speaking and communicating technical material to non-technical audiences
  • Career development skills, including case practice, interview skills, CV writing, networking, and business etiquette
  • Team skills, including managing conflict, cultural awareness, giving and receiving feedback, and resilience
  • Business knowledge, including understanding the business and industry context in which analytics professionals operate, how different parts of organisations interact, and meeting and networking with business leaders.

In 2015, part of PEP included having Melbourne Playback Theatre facilitate a series of experiential workshops, providing the class with tools to communicate more effectively in a range of business settings. By practicing and rehearsing the elements of effective face-to-face communication, students developed confidence to speak up and be influential in meetings, network with clients, excel in job interviews, enter difficult conversations and give strong business presentations that use story to influence:

Academic experts and business leaders are invited to speak to the class about a range of subjects to increase knowledge and understanding of business environments. Topics could include:

  • Big data ethics
  • Decision-making biases
  • Privacy law
  • Analytics in web-based companies
  • Credit risk
  • Accounting
  • Finance
  • Supply chains
  • Human resources

Seminars and Workshops, delivered through PEP, could include:

  • Managing your career
  • How to find and use a mentor
  • Managing your next career transition
  • Technical writing for a non-technical audience
  • Difficult conversations and managing up
  • Storytelling with data
  • Presentation skills
  • Networking skills
  • Behavioural interviewing
  • Case interview techniques
  • Leading a team
  • Project management
  • How to set up your LinkedIn profile
  • Using LinkedIn for your job search.

The final important element of the PEP is one-on-one feedback and coaching. Students engage in teamwork throughout the program and receive feedback at various points about their interactions. Through coaching by program staff, students learn to understand this feedback and make changes in their approach that enhance their ability to lead and work in teams.