Master of Applied Business Analytics

Marketing

Learn to manage market demand using proven demand‐side strategies. You will learn to recognise, create, grow and protect market‐based assets that influence demand and how marketing investments help develop and translate market‐based assets into outcomes that ensure an organisation’s long‐term survival and success.

Applied Marketing Analytics

In a competitive business landscape, understanding the impact of marketing efforts on revenue and profit growth is paramount. This requires proficiency in utilising marketing analytics, a crucial toolkit for modern business which leverages technology and models to analyse customer and market data and empowers businesses to make informed decisions. Through a hands-on approach utilising real-world case studies, this subject equips students with practical skills in marketing analytics. Participants will gain an understanding of various analytics tools and learn how to apply them effectively to solve complex marketing challenges. By delving into case studies and practical exercises, students will develop the ability to strategically influence key marketing outcomes such as customer satisfaction, brand loyalty, and word-of-mouth referrals.

Operations

Discover the tools and frameworks to understand the role that the operations function plays in a firm’s ability to compete effectively in the marketplace. You will focus on the design, management and continuous improvement of the business processes that help firms exceed customer expectations along measures such as cost, quality, flexibility and innovation.

Applied Supply Chain Analytics

This subject offers students a practical understanding of mathematical modelling and analytical tools essential for optimising logistics and supply chain operations. Through hands-on application, participants will gain proficiency in analysing strategic, tactical, and operational decisions related to inventory management, facility location, and logistics. Real-world case studies will immerse students in the complexities of logistics and supply chain management, providing valuable insights into industry practices and challenges. By bridging theory with practical experience, this subject equips students with the skills and knowledge needed to make impactful contributions in logistics and supply chain roles.

Finance

Establish a solid foundation for valuing financial assets and selecting and managing investment projects. You will discover how to manage a firm to increase the wealth of shareholders, subject to fulfilling contractual and legal obligations to relevant stakeholders.

Applied Investment Analytics

Effective risk management is essential for institutional success and resilience. This subject delves into the practical application of data analytics in risk management, equipping students with the skills to better safeguard an organisation. Through a blend of theoretical concepts and hands-on application, students will gain proficiency in using contemporary data to analyse and mitigate various risks. Real-world case studies will provide insight into applying these techniques in critical scenarios, bridging theory with practical experience. Topics covered include market risk modelling, portfolio management techniques, credit risk assessment, and operational risk evaluation.

To qualify for this project, students must complete Marketing and Applied Marketing Analytics.

The subject integrates academic learning and practical challenges in modelling, solving and implementing an optimisation solution to a concrete business problem via a project undertaken over 20 weeks. The assessment in the subject will include the completion of the report for the subject and a project presentation. This is a team project.

The program focuses on the following areas:

• Problem-solving and method-translation skills: These include understanding and identifying possible analytics solutions to address a typical marketing-related problem of an organisation.
• Communication skills: These skills include effective presentations, verbal communication, written communication, public speaking, and communicating technical material to non-technical audiences.
• Implementation skills: These skills include finding the right software and/or computer language to implement the most promising analytics solution to address the marketing-related problem.

To qualify for this project, students must complete Operations and Applied Supply Chain Analytics.

The subject integrates academic learning and practical challenges in modelling, and implementing optimisation solutions via a project undertaken over 20 weeks. The assessment in the subject will include the completion of the report for the subject and a project presentation. This is a team project.

The program focuses on the following areas:

• Modelling skills: These include understanding the business problem, dealing with ambiguity, and finding a path to transform the business problem into a mathematical optimisation problem.
• Communication skills: These skills include effective presentations, verbal communication, written communication, public speaking, and communicating technical material to non-technical audiences.
• Implementation skills: These skills include (1) selecting the right software and/or computer language to code the optimisation algorithms proposed and (2) handling and cleaning the data provided to use as input to the optimisation algorithm developed.

To qualify for this project, students must complete Finance and Applied Investment Analytics.

In this capstone subject, students will work in groups to build an investment portfolio using one or more of the investment business analytics algorithms below used in the finance industry:

• Fundamental analysis algorithms to analyse financial (e.g. earning forecast) and economic data to evaluate the underlying value of a security.
• Technical analysis algorithms to analyse market data, such as price and volume, to identify trends and patterns that may indicate future price movements.
• Quantitative analysis algorithms including statistical models and machine learning techniques to identify patterns and predict market movements.
• Arbitrage algorithms using price discrepancies between different markets or securities to generate profits.
• Trend following algorithms using historical price data to identify trends and generate trading signals based on the direction of the trend.
• Mean reversion algorithms to look for securities that are trading outside their historical price range and generate trading signals based on the assumption that the security will revert to its historical mean.
• High-frequency trading algorithms to execute trades at high speeds to take advantage of small price discrepancies in the market.
• Sentiment analysis algorithms using natural language processing and machine learning techniques to analyse news and social media data to gauge investor sentiment and predict market movements.
• Portfolio optimisation algorithms use mathematical models to optimise the allocation of assets in a portfolio based on risk and return objectives.