Master of Science in Business Analytics

Applicants will be granted unconditional admission if they meet all of the following criteria:

1. Academic Qualifications
• Hold a Bachelor’s degree in Business, Information Systems, Computer Science, Engineering, Statistics, or a closely related discipline from a recognized university.
• Have a minimum cumulative GPA of 3.0 out of 4.0 (or equivalent).
2. English Language Proficiency
• Submit a valid IELTS Academic score of 6.0 or its equivalent (TOEFL or other internationally recognized English language qualification), not older than two years at the time of application.
• Exemptions apply to applicants who have completed their undergraduate education in an English-medium institution located in a country where English is the native language.

Conditional admission may be granted, subject to seat availability, for applicants who do not meet the required CGPA or English Proficiency criteria. For full details, please refer to the AU Graduate Admission Policy (pages 4–5).

This course provides students with the fundamental concepts and tools needed to understand the emerging role of data analytics in organizations. It covers managerial statistical tools in descriptive analytics and predictive analytics. Other topics covered include linear regression, forecasting, and data mining. This course shows students how to apply basic analytics tools in a spreadsheet environment, and how to communicate with analytics professionals to effectively use and interpret analytic models and results for making better business decisions. Emphasis is placed on applications, concepts and interpretation of results, rather than theory and calculations. Students use a computer software package for data analysis.

This course is an introduction to quantitative models for managerial decision-making in a complex and dynamic business environment. Students learn to develop linear, integer, non-linear, and multi-criteria optimization models; perform sensitivity analysis; develop heuristics; analyze decisions under uncertainty; and conduct scenario analysis using simulation. While the skills developed in this course can be applied to a very broad range of business problems, the practice examples and student exercises will focus on the following areas: human resources, logistics and supply chain optimization, capital budgeting, asset management, and portfolio analysis. The course relies heavily on the use of Python, Excel and Gurobi solver.

This course introduces AI and machine learning (ML) concepts with a focus on business applications. Students will learn key ML techniques, including supervised and unsupervised learning, predictive modelling, and data-driven decision-making. The course integrates hands-on coding exercises in Python, tailored for students with no prior programming experience, alongside business-oriented AI/ML tools (e.g., Power BI, AutoML, KNIME). Students will gradually develop coding proficiency while applying AI/ML to solve real-world business challenges, emphasizing model interpretation, visualization, and strategic decision-making.

This course, with a focus on business applications, provides a comprehensive introduction to data mining and text mining. Students are expected to explore various data mining techniques and their practical applications to real-world problems, employing the R programming language. The course will cover essential data mining concepts and explore techniques for preprocessing and analyzing text data. Additionally and to ensure a thorough understanding of both fields, students will engage in sentiment analysis, topic modelling, and examine the interplay between data mining and text analytics. The course structure is designed to balance theory with practical application, using interactive sessions, case studies, and project-based learning. This approach teaches technical skills and develops critical analytical abilities, enabling students to lead and innovate in data-driven business environments.

This course applies the techniques developed in business analytics courses to a real-world problem. Students will identify a business problem and related data, solve the problem by applying various analytics and visualisation techniques, and present the findings in a report to senior management.. Student groups will be assigned to consulting projects from businesses and other organizations. Groups will work on their projects under the direction of the course instructor, who will be responsible as your facilitator in guiding you through your knowledge demonstration, and your industry partner, who will provide business guidance on the feasibility of your project. This course does not require you to learn any new material. It does, however, require you to demonstrate knowledge in the prerequisite courses and other courses. As such, this course is recommended to be completed towards the end of the student’s studies.

This course provides a comprehensive exploration of web and social media analytics, covering essential methodologies such as web scraping, Natural Language Processing (NLP), sentiment analysis, and network analysis. Students will gain hands-on experience with industry-standard tools and techniques to extract, analyze, and interpret large-scale social media and web data. By the end of the course, students will be able to transform unstructured digital data into actionable business strategies, enhancing their analytical capabilities and decision-making skills in a data-driven world.

This course provides an introduction to the concepts of business intelligence (BI). It explores how business problems can be solved effectively by using operational data to create data warehouses and then applying analytics to gain new insights into organizational operations. In particular, students learn effective modelling techniques (dimensional modelling), foundations and technologies for the Decision-Making process, the ETL process, Business Performance Management, and analytical modelling (descriptive, predictive, and prescriptive). Students will learn to exploit the demonstrated topics to extract business intelligence and convey it to stakeholders.

This course explores the role of business analytics and artificial intelligence (AI) in driving digital transformation across industries. It examines how organizations leverage data, AI, and emerging technologies to enhance decision-making, optimize operations, and create new business models. Students will gain practical insights into digital transformation strategies, AI-driven automation, and data analytics tools that enable business innovation. Through case studies and hands-on applications, students will learn to assess digital maturity, implement AI-driven business solutions, and navigate challenges related to digital disruption, ethics, and governance.

This course introduces students to the world of Finance Analytics, combining core financial principles with analytical tools and techniques. The course is designed to provide a comprehensive understanding of financial data, risk management, modelling, and forecasting methods. Students will explore the application of analytical techniques to financial decision-making, investment strategies, and risk management.

This course introduces students to the world of Finance Analytics, combining core financial principles with analytical tools and techniques. The course is designed to provide a comprehensive understanding of financial data, risk management, modelling, and forecasting methods. Students will explore the application of analytical techniques to financial decision-making, investment strategies, and risk management.

This course is designed to deepen the understanding of advanced forecasting methods through practical application using R statistical software. Students will engage in rigorous analysis of time series data, learn to perform cross-sectional and temporal analyses, and critically assess the limitations of various analytical methods. The course will cover the interconnections between forecasting techniques and the contexts in which they are most effective. Additionally, it will explore the nuances of forecasting at different organizational levels, from industry-wide forecasts to firm-specific predictions.

This course will provide students with a base knowledge of digital analytics strategies and tactics. Students will learn how to obtain data, how to analyze data and turn it into insights, and how to present and communicate insights into actionable recommendations. We will review key digital analytics concepts and be exposed to a wide variety of platforms and tools throughout the semester.

The fast-changing digital landscape provides new opportunities for marketers to communicate with their customers. This course provides a comprehensive overview of how organizations can get the most out of digital media and technology to achieve their marketing goals. It develops an understanding of the strategic frameworks to understand how to interact with customers at different touchpoints in their customer journey. Conducting a situation analysis of an online marketplace and developing the digital marketing strategies and marketing mix for an online environment are discussed. The course examines concepts like relationship marketing, campaign planning, and developing marketing communications using effective digital media channels. Factors for the successful implementation and performance of the digital channel are evaluated.

For businesses of all sizes, social media marketing is a powerful tool for reaching out to potential customers. This course provides a thorough overview of how businesses can achieve their marketing and branding objectives by leveraging the power of various social media networks. Students will become acquainted with the major social media zones as well as the channels, modes, and vehicles for social media participation. Consumer behaviour will be examined to determine the motivations for participating in social media activities. This course will cover how to plan and develop actionable social media strategies using various platforms and tools, as well as how to optimize results using analytical knowledge.

This course provides an in-depth exploration of Human Resource Analytics (HR Analytics), equipping students with the knowledge and technical skills to leverage workforce data for strategic decision-making. The course integrates statistical analysis, predictive modelling, machine learning techniques, and data visualization to generate actionable insights for HR management. Using tools like Excel, Python, and Oracle, students will develop HR dashboards, analyze employee engagement, performance, turnover, and recruitment data, and apply predictive analytics to workforce planning. Real-world case studies from industries such as finance, healthcare, hospitality, and technology will provide context for applying analytical methodologies. Additionally, the course will emphasize ethical considerations, data privacy, and bias mitigation in HR analytics. The capstone project challenges students to design a data-driven HR solution that demonstrates applied HR analytics in business environments.

This course studies key decision areas in supply chain design and operation using data-driven methodologies. The course introduces students to the key components of supply chains, the role of data in supply chain management, data manipulation, visualisation techniques, customer management approaches, supply management techniques, warehouse and inventory management, demand forecasting, and logistics management. The course integrates Python programming to implement data-driven supply chain strategies.

BAL698 is specifically designed to prepare master's students for the complexities of research within the domain of business management. This course provides students with the foundational skills necessary for effective research, including the development of research questions, the execution of thorough literature reviews, and the formulation of detailed research proposals. Students will explore a variety of philosophical paradigms and research methodologies pertinent to business analytics, ensuring they are well-prepared to design robust research projects.

Throughout the course, emphasis is placed on identifying and applying appropriate analytical frameworks and methods that align with specific research questions within the field of business analytics. This comprehensive preparation is critical for students as they advance towards their thesis work, equipping them with the necessary tools to undertake their research with confidence and academic rigor.

In BAL699, students are required to develop and execute a comprehensive research plan addressing a real-world problem within the field of business analytics. This course guides students through the process of formulating a research question, reviewing relevant literature, and designing a methodology that adheres to ethical standards. Students will collect primary or secondary data, as appropriate, under the supervision of their assigned academic supervisor, who provides ongoing feedback and support.

The analysis phase involves a critical examination of the data, integrating diverse stakeholder perspectives to enhance the depth of the discussion. Students are expected to present their findings in a graduate dissertation format, which includes a detailed discussion on the implications of their results for both theory and practice. The final document will showcase their ability to synthesize complex information and contribute meaningfully to the field of business analytics.