Subjects overview
This program can be completed in 1 year 4 months (4 semesters)
Required Subjects
Students must complete the following ninety credit points (90CP) of subjects.
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BUSN71-200: Responsible and Sustainable Organisations (10CP)
Considering the increasingly complex environmental, social and governance challenges facing today's business organisations, it is essential to develop an integrated understanding of business and its role in society. In this subject, students will be challenged to explore a multinational business from multiple perspectives to develop a systems view of the organisation and its global business environment. Through readings, discussions, case studies, projects and other learning activities, students will develop a more nuanced view of the purpose and functioning of business, the expectations of stakeholders, and the challenges and opportunities inherent in addressing those expectations. This exploration will include understanding the functional areas of business as well as how each can work together in an overall design to enable an integrative and innovative approach to responsible and sustainable business.
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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.
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ENAI71-100: Computational Thinking (10CP)
Computational Thinking is a foundation subject that introduces students to problem-solving used in modern computing and AI environments. Topics include decomposition, recognising patterns, abstracting structures and logic flows, and developing algorithms that are generalisable and reusable. Other important skills covered are debugging and testing. Students will learn to apply these concepts both in theory and practice, building skills that are used to tackle real-world problems. The traditional computational methods are blended with emerging AI. Students will learn how to integrate AI tools into a workflow to produce, optimise and validate code used in business environments
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ENAI71-101: Cybersecurity, Networks and Operating Systems (10CP)
Cybersecurity, Networks & Operating Systems explores the foundational and advanced concepts underpinning modern computing systems and secure networked environments. Students will examine core operating system functionalities, including process and memory management, file systems, input/output handling, and scheduling strategies. The subject also delves into permission models and security mechanisms that safeguard system integrity. Networking topics cover essential protocols such as TCP/IP and the roles of devices such as switches and routers in data transmission. Emphasis is placed on identifying and mitigating cybersecurity threats within operating systems and network infrastructures, preparing students to design resilient systems and apply best practices in secure computing.
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ENAI71-102: Algorithms and Data Structures with AI-Assisted Implementation (10CP)
This subject introduces students to the core principles of algorithms and data structures, equipping them with the skills to design efficient solutions to computational problems. The subject covers fundamental data structures such as arrays, stacks, queues, trees, and graphs. It also covers classic algorithms for sorting and searching, and their time and space complexity are discussed. Key algorithmic paradigms, including divide and conquer, greedy methods, and dynamic programming, are introduced. AI tools are integrated in the learning, supporting the development process and code generation, debugging, and optimisation of code. Through hands-on programming and reflective practice, students will gain technical proficiency and an understanding of how AI can enhance coding practice.
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ENAI71-200: Programming Paradigms and AI Principles (10CP)
This subject explores the modern approaches to programming through the study of three key paradigms: procedural, functional, and object-oriented. Students will develop a deep understanding of structured programming techniques, functional constructs such as immutability and higher-order functions, and object-oriented principles including encapsulation, inheritance, and polymorphism. The subject also covers examples of implementations of algorithms and data structures across these paradigms, showing how to apply computational thinking and design in different programming languages. In addition, students will engage with AI-assisted programming approaches to examine how generative tools can assist in code generation, debugging, and problem-solving. Through hands-on activities and critical reflection, students will gain the ability to select and apply appropriate programming languages and AI tools to solve real-life problems through programming.
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ENAI71-202: Data Management (10CP)
This subject introduces students to the principles and practices of managing data effectively in modern computing environments. It covers foundational topics such as database design, relational models, and the use of SQL for querying and manipulating data. Students will learn to construct Entity-Relationship diagrams, apply normalisation techniques to optimise database structure, and explore strategies for ensuring data reliability through backup and recovery. The subject also examines the evolution of data systems by introducing NoSQL and non-relational databases, highlighting their applications in handling large-scale, unstructured data. Through practical exercises and conceptual analysis, students will gain the skills to design, implement, and maintain robust data management solutions, using AI to support the learning of the syntax for specific database management software.
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ENAI71-301: Software Design, Maintenance and AI integration (10CP)
This subject provides an overview of software engineering principles, focusing on the design, maintenance, and evolution of software systems in modern development environments. Students will explore the software development life cycle, foundational design concepts, and the importance of modularity, testing, validation, and version control. Emphasis is placed on quality assurance practices and the use of tools to support collaborative development. The subject also introduces AI-integrated approaches to software design and maintenance, examining how AI tools can assist in code generation, refactoring, testing, and documentation. Through practical exercises and critical analysis, students will gain the skills to design robust software systems and integrate AI tools effectively and ethically into the software engineering workflow.
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ENAI71-304: Applied Enterprise AI Project (10CP)
This subject is the capstone project at the end of the AI Development and Enterprise. Students are required to apply the concepts, theories and frameworks from their entire program to complete a research project. Working under the supervision of an academic staff member, students will navigate the research process, develop research questions that are relevant to both the industry and the academic community, synthesise the relevant literature, and use appropriate AI techniques to achieve the objectives of the project. Projects may be created internally or may be sourced from the industry.
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Enterprise AI Option
Students must choose twenty credit points (20CP) of the following subjects.
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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.
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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.
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DTSC71-305: Financial Trading Systems (10CP)
This subject provides the opportunity to learn the tools and strategies used by investment and hedge fund managers to invest and trade in a number of financial instruments, including equities, futures, FX and ETFs in both low and high-frequency environments. Using financial data drawn from a variety of sources including Bloomberg, you will learn to model and benchmark these strategies using Python. The overall aim of this applied, research-focused subject is to explore quantitative trading strategies used to capitalise on market anomalies.
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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.
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ENAI71-111: AI for Business Professionals (10CP)
This subject equips professionals and emerging business leaders with the strategic, ethical, and practical knowledge to leverage artificial intelligence (AI) both personally and within modern organisations. Students will explore AI's capabilities, limitations, and implications, and learn how to apply AI tools to improve decision-making, innovation, and operations. Through a combination of theoretical content, real-world case studies, and hands-on tool exploration, students will be prepared to lead AI-driven transformation within their industries.
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FINC71-201: Financial Applications and Analysis (10CP)
An introduction to statistical techniques used in financial analysis and decision-making. Specific applications include capital budgeting, capital asset pricing model, arbitrage-pricing, portfolio modelling and the study of co-movements of different financial assets. The use of spreadsheets and related software tools is central to the learning experience of this subject to provide extensive opportunities to develop practical skills in financial analysis and modelling.
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MKTG71-315: Marketing Analytics (10CP)
This subject enables students to apply appropriate marketing analytical tools and techniques to address a range of marketing challenges. Importantly, it will also highlight the role and importance of marketing analytics to support a data-driven approach to marketing strategy and decision making. Through a variety of applied activities and assignments, students will have the opportunity to develop a thorough conceptual understanding of marketing analytics and its applications.
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Innovation Choice
Students must choose 10 credit points (10CP) of the following subjects.
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ENFB71-104: Entrepreneurship and Innovation (10CP)
This subject is designed to help students develop fundamental entrepreneurial knowledge and skills. This includes the processes, heuristics and tools that inform entrepreneurial strategy, whether applied to the creation and growth of a new venture or leading change and innovation in the context of an established, mature organisation. Using a case-based, experiential approach, students examine start-up and innovation strategies that new and established companies have used to become world-class leaders in their industries. This subject is open to all disciplines and programs.
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MGMT71-311: Leading Innovation and Change (10CP)
A ubiquitous challenge in organisational life is how to effectively initiate, implement and sustain desired change. Although organisations continue to expend staggering amounts of time and resources on change initiatives, the majority of such efforts do not achieve their intended outcomes. In this subject, students will explore the underlying reasons for these failures to examine the fundamental nature of change and the challenges that change agents at different organisational levels face as they plan and execute change. Since organisations consist of people, students begin with an examination of individual change to explore fundamental concepts before extending and expanding their scope to consider change at the organisational level. Students will have the opportunity to apply and test their understanding of change management principles through readings, case study discussions, exercises, role plays and individual and group projects.
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Major / Minors / Specialisations Finder
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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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