Subjects overview
This program can be completed in 1 year 4 months (4 semesters)
Required subjects
Students must complete the following eighty credit points (80CP) of subjects.
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BMBA70-810: Individuals, Teams and Leadership (5CP)
This intensive MBA subject provides a review of the theory, evidence and practice of effective teams and how they are created. It also uses the team context to examine the process of leadership and explores its adaptation to other organisational contexts. Students will learn to apply these principles to the creation and development of their teams and reflect on ways to become a more effective team member and leader.
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BMBA70-820: Interpersonal Dynamics (5CP)
This intensive MBA subject explores the interpersonal dynamics of groups and organisations, with a specific emphasis on effective communication, empathy, understanding and using power and other social and psychological factors affecting relationships.
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BMBA70-830: Effective Decision Making (5CP)
Decision-making is one of the key tasks of a manager. This intensive MBA subject offers a multi-disciplinary perspective on decision-making, with a specific emphasis on ethical, responsible and sustainable considerations for effective decisions. The subject will also explore obstacles and common mistakes to effective decision-making and approaches for overcoming them.
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BMBA70-840: Motivation and Organisation (5CP)
Motivation is a central concept to individual and collective learning and performance. In this intensive MBA subject, students will examine the nature of motivation to more effectively influence individual and collective behaviour. Specific organisational applications of motivation theory will be examined in detail so that students learn to evaluate approaches to employee motivation and to understand how individual and contextual factors interact to affect human effectiveness and well-being.
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BMBA70-850: Managerial Economics (5CP)
This subject is designed to enable students to analyse economic environments and to develop an understanding of the value of economic reasoning to evaluate strategic business decisions. It is also intended to provide an opportunity to apply this understanding to develop fundamental economic analysis and evaluation skills to support managerial decision making.
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BMBA70-860: Marketing for Managers (5CP)
In today’s business world, creating customer value through marketing is essential to a company’s growth strategy. Marketing involves several interrelated processes like understanding the business environment, conducting market research, analysing customer needs and finally providing customer value through the 4Ps strategy (product, price, place and promotion). This subject is designed to enable students to develop a concise understanding of marketing principles and concepts. Students can further apply evidence based knowledge to find solutions to real-world marketing problems.
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BMBA70-870: Accounting for Managers (5CP)
This subject is designed to develop students’ managerial financial literacy and decision-making skills. It is organised around three sets of accounting fundamentals: financial reporting mechanics; measurement; and management. The main objective is to enable students to understand, decompose and interpret financial statements to assess a firm’s business model, operating performance and overall financial health.
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BMBA70-880: Financial Decision Making (5CP)
This intensive MBA subject is designed to enable students to recognise corporate financial problems and opportunities, communicate effectively with finance professionals and participate fully in financial decision making. This subject addresses the key decisions faced by senior managers: determining optimum capital structure, managing financial risk and valuing business opportunities. The primary objective of this subject is to develop analytical frameworks and practical skills through the use of readings, cases, lectures, discussions and role-plays.
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BMBA71-301: Data Analytics for Decision Making (10CP)
This subject develops the student’s facility for evidence-based decision making, by introducing students to the use and application of data. As the business world has increasing access to data, and in the availability of big data sets which allow greater understanding of customers and other business related data, effective use of the data will enable decisions to become more informed. This subject will consider the role of data in an evolving business system, discuss and review common sources of data and processes for developing superior data sets, and will introduce the quantitative methods that are needed for understanding what the data tells us re the decision we need to make. It develops an understanding of modern computational methods to solve quantitative problems in business decision making, using a case-based approach to using data.
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BMBA71-302: Strategic Insight (10CP)
Strategic Insight is the capstone subject concentrating on strategy development and implementation at the top management level where major decisions are made. Advanced problems in determination, execution, and control of the strategic management process in light of complex environmental change will be focused on in this subject. The aim of the subject is to provide students with a broad overview of the basic concepts in strategic management. Students will be exposed to a number of frameworks and models to better understand and analyse the macro-environment, the industry environment and firm-level resources. At the completion of the subject, students should be able to think strategically, as opposed to only having a functional orientation and students should be able to formulate and to implement creative and innovative strategies that are conducive to the demands of the firm and the environment in which it resides.
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BMBA71-303: MBA Capstone (10CP)
In this capstone subject, you will examine a large-scale, complex issue that requires considerable independent study and broad, integrated application of what you have learned in the MBA program to date. Your research project can address an organisational problem or opportunity encountered professionally or a conceptual issue encountered academically. In consultation with the subject coordinator and a chosen academic mentor, you are expected to identify and clearly define the ‘problem’; integrate and apply relevant theory; collect and analyse appropriate data; and formulate reasonable, evidence-based conclusions. The expected deliverables are a professional written and formatted report detailing all aspects of the project and an executive presentation of key elements.
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MGMT71-326: Project Innovation and Change (10CP)
This subject immerses students in a practical, user-centric approach for the creative, evidence-based resolution of problems. This iterative, collaborative process draws heavily from design thinking and is applicable to a broad array of societal, organisational and project challenges. Key elements include a focus on understanding and empathising with the user, challenging assumptions, considering multiple perspectives, generating and exploring creative ideas, making and learning from mistakes, questioning implications and adaptively planning the implementation of validated solutions. It is a way of thinking and working as well as a collection of hands-on methods, and is especially useful in addressing problems that are ill-defined, complex or unknown, leading to innovative change. The approach can help project teams learn faster and achieve more effective and creative outcomes, while reducing the risks associated with launching new ideas or implementing change initiatives. This subject is open to all disciplines and programs to support the interdisciplinary problem-solving nature of this approach.
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General Electives
Students must choose forty credit points (40CP) of postgraduate subjects from across the University.
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University Postgraduate Electives
Students may choose from all postgraduate subjects across the University that are available as general electives.
Optional Specialisation
Students are encouraged to tailor their study with an optional Specialisation. This will replace forty credit points (40CP) of elective subjects.
Opportunities
Students may take advantage of the following opportunities to have an international, real-world, or internship experience, provided they meet the requirements. This would replace the equivalent credit points of electives and may incur additional costs. Please discuss this with an Enrolment and Student Engagement Officer in the Student Assist for more information.
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Postgraduate Opportunities
Students may have the opportunity to participate in an international study tour experience or internship as a general elective. Those interested should consult with an Enrolment Officer in Student Assist for guidance and to check eligibility requirements (e.g., GPA, language proficiency, prerequisites). Students should make informed decisions and ensure their chosen international experience or internship aligns with their academic and personal goals.
Participating in such an opportunity may involve additional costs, which may vary depending on the opportunity's location, duration, and nature. Students are responsible for all associated expenses, including travel, accommodation, visa fees, insurance, and any program or placement fees that may be applicable.
Opportunities
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Required Subjects
Students must complete the following ten-credit point (10CP) subject.
- DTSC71-200: Data Science (10CP)
Organisations use their data for decision support and to build data-intensive products and services. The collection of skills required by organisations to support these functions has been grouped under the term Data Science. This subject will articulate the expected output of data scientists and then equip students with the ability to deliver against these expectations. A particular focus will be given to the tools required to model, store, clean, manipulate, and ultimately extract information out of stored data.
Data Analytics Options
Students must choose thirty credit points (30CP) of subjects from the Data Analytic options.
- ACSC71-307: Survival Analysis (10CP)
The focus of this subject is analysing the time until an event happens, such as the illness or death of a person, or the failure of a business. The issue of censored data is common in such scenarios and how to handle censored data will be discussed throughout this course. The theory, estimation and application of a variety of survival models for censored data are covered, spanning parametric, semi-parametric and non-parametric models. Machine learning methods suitable for censored data are also covered.
- DTSC71-100: Business Analytics Coding (10CP)
This subject is an introduction to programming. There is a focus on writing computer code to solve problems in business, which promotes the development of problem-solving skills. The necessary foundation concepts are covered, including expressions, variables, data structures, control structures, functions, commenting and debugging. Although it can be taken as a stand-alone subject, it is specifically designed for students interested in future study in data science and big data analytics. Two widely popular programming languages for data science, R and Python, will be used as vehicles for learning programming. Cutting-edge R and Python packages used by data scientists will be covered in this subject. Prior coding knowledge and experience is not a requirement for this subject
- DTSC71-110: Cyber and Fraud Threats in Organisations (10CP)
All organisations today face cyber and fraud threats: small and large businesses, non-profits, health organisations, government and more. Valuable corporate data is highly sought after in the criminal and business communities. Emerging intellectual property and organisational data provides an insight into competitors as well as being valuable commodities to sell on the criminal markets. In this subject, you will be introduced to cybercriminals, learn their motivations and methodologies, and identify potential vulnerabilities and proactive strategies to protect the organisational network, its employees and its data.
- DTSC71-300: Infrastructure for Data Analytics (10CP)
Using an information systems approach, this subject outlines the design principles and techniques necessary to produce appropriate infrastructure specifications for different data analytic systems. These requirements can be specified in terms of people, procedures, data, software, and hardware. Successful designs will allow systems to automatically extract insights from vast amounts of available data. Topics include, but are not limited to, key modern issues such as job roles in data analytic ecosystems, the operation of organisations, security and data integrity principles, business processes, blockchains, NoSQL databases, cloud solutions, software options and fundamental tenets of computing. The knowledge of these, and understanding how the components interact together, allow students to design efficient systems that are robust to change and conform to best practice.
- DTSC71-301: Deep Learning Through Neural Networks (10CP)
Computer vision, natural language processing and personalised recommendations are just a few of the uses of artificial neural networks that are increasingly relevant to real-world problems that pose challenges for traditional data analysis techniques. This subject introduces students to the foundational ideas associated with the many variations of these models that have been developed for domains involving image data, temporal data, and natural language. This includes feed-forward, fully connected neural networks, convolutional neural networks, recurrent neural networks, and the transformer architecture. Class discussions will introduce the technical underpinnings of the models and applied sessions and assessments provide students the opportunity to experiment and apply them to a wide range of practical, real-world problems using Python.
- DTSC71-302: Statistical Learning and Regression Models (10CP)
This subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered. All techniques will be investigated via practical application on real data using the statistical software package R.
- DTSC71-306: Modern Machine Learning Models (10CP)
This subject is designed for students who already have a basic understanding of machine learning and want to deepen their knowledge using more advanced techniques. The subject focuses on advanced machine learning methods that are relevant and effective in many real-life and business applications. Students will be provided the necessary tools to wrangle data, implement and train machine learning models, and evaluate the performance and feasibility of these models in the context of the environment where these models are going to be applied. Advanced visualisation tools will be used to create dynamic visual representations of data.
- DTSC71-307: Advanced Statistical Learning Models (10CP)
This subject extends the investigation of modern statistical modelling techniques initiated in Statistical Learning and Regression Models. Topics include models for correlated data including spatial and mixed-effects models, as well as Bayesian hierarchical models including discussion of Markov chain Monte Carlo (MCMC) techniques for calculating posterior estimates, and modern applied re-sampling methods for developing robust measures of model accuracy. The programming language R will be used in this subject.
- ECON71-200: Linear Models and Applied Econometrics (10CP)
Econometrics is a sub-discipline of both statistics and economics and presents one interface between statistical theory and the real world. It provides the tools with which to test hypotheses and to generate forecasts of business activity. Topics include the classical regression model, remedial measures for violation of regression assumptions, binary choice models, panel data models and their applications. The technique such as hypothesis testing and its application will allow students to specialise in areas such as market research and other disciplines. The skills that students will develop in this subject are crucial in any applied work and will constitute an essential ingredient in most jobs in the field of business application, whether in the public or private sector.
- ECON71-300: Advanced Econometrics (10CP)
Many types of economic and financial data naturally occur as a series of data points in temporal order. Stock market indices are a classic example of such time series. Standard statistical methods are not appropriate for such data. This subject provides an introduction to time series econometrics with an emphasis on practical applications to typical economic and financial issues. Emphasis will be placed on determining when it is appropriate to use the various time series econometrics techniques and the use of appropriate software to conduct the analysis.
Study plan
Take the guess work out of planning your study schedule. Your program's study plan has been carefully curated to provide a clear guide on the sequential subjects to be studied in each semester of your program. Your study plan is designed around connected subject themes to equip you with the fundamental knowledge required as you progress through your course.
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