Full-time Master of Business Analytics

This subject is designed to help students develop the skills and knowledge required to effectively manage the early stages of their career. This subject runs across roughly the first half of the full-time course and identifies specific needs of each individual student and then provides ongoing support, training, and opportunities to practice and perfect these skills.

Assessment of the subject includes a quiz on revisionary material, the writing of CVs, group work and group presentations as well as an essay involving ethical concerns within analytics.

The program focuses on four areas:

• Revisionary material: Rudimentary technical knowledge for the course.

• Communication skills: These skills include effective presentations, verbal communication, written communication, public speaking, and communicating technical material to non-technical audiences.

• Career development skills: These skills include interview skills, CV writing, networking, and business etiquette.

• Team skills: These skills include managing conflict, cultural awareness, giving and receiving feedback, and resilience.

These areas are the preparation for industry placement occurring in Professional Development II.

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.

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 covered include:

• cyber security, and;

• cyber ethics and privacy, regarding the collection of individual data.

This component helps students formulate a business problem as a mathematical model and then use computational techniques to estimate and solve the model.

Topics covered include:

• decision-making under uncertainty;

• optimal location/allocation of resources in business processes;

• decision trees;

• linear programming;

• integer linear programming, and;

• Monte Carlo simulations.

The aim of this component, and the follow-on component in Module 3, is to help students learn how to extract relevant information from large amounts of complex data to make improved business decisions.

Topics covered include:

• data exploration;

• resampling methods;

• linear and nonlinear regression;

• parametric classification techniques, and;

• model selection.

This component builds on the material in Module 2’s Statistical Learning and covers advanced analytic methods. It extends the statistical-learning component of Module 2 in three ways.

First, new techniques, such as tree-based methods and neural networks, are introduced. Second, students are introduced to unsupervised statistical-learning techniques, and third, students 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, is also stressed.

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 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, known within the degree as the Analytics-Lab, or A-Lab, involves practical experience for teams of students working on analytics project using data in or from an industry setting, typically undertaken as a five-week group internship.

The five-week project integrates academic learning with practical challenges in implementing data analytics, while developing employability skills and attributes, and improving students’ knowledge of organisations, workplace culture and career pathways.

The assessment week will involve the completion of a report for the subject and a project presentation.

Topics covered include:

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, and;

• 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, and;

• Service delivery.

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 provides students with:

• knowledge of marketing analytics;

• the ability to know which analytics tools to use for which marketing problems;

• the ability to use those tools to solve marketing problems, and;

• the ability to influence marketing outcomes such as satisfaction, choice, loyalty, word of mouth, and customer referrals.

Rapid advancements in technology (particularly the Internet), combined with fast and cheap computing power, has enabled firms to radically transform their industries by developing business models and re-engineering their supply chains.

This component provides students with:

• knowledge of mathematical modelling and analytic tools, relating to logistics and supply chain optimisation problems;

• 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;

• exposure to real world logistics and supply chain decisions through case studies.

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 covered 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.