Full-time Master of Business Analytics

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.

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.

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.

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.

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.