This subject introduces students to the fundamentals of computational thinking and their application in developing programming solutions to problems. Topics covered include programming concepts, simple data structures and programming techniques.

This subject introduces students to coding principles and practices using an object-oriented approach. The subject also introduces the development of an IT application using the latest technologies. Topics covered include object and classes, composition, simple data structures, application architecture, design and development.

This subject will introduce the fundamental concepts of relational database systems, the design methods specific to relational database, database manipulation using a database query language, and the techniques of implementing relational databases. It will also cover implementation of simple application to access relational database.

This subject covers the data analytics lifecycle, including gathering, cleaning, processing and visualizing of data. Exploratory data analysis methods, descriptive and predictive analytics and the presentation of insights will also be covered.

This subject equips with knowledge and skills in the emerging field of data science. It covers the data science life-cycle, history and context, as well as its landscape.  Topics covered include data exploration and analysis techniques to discover new knowledge from data to aid data-driven decisions in an intelligent and informed way.

The subject covers graphing fundamentals, graphing properties and building dashboard for storytelling and reporting purposes using relevant statistical modelling and analysis techniques. Topics covered include the preparation of reports on data analysis to support managerial decision-making and applying the data storytelling framework and principles of data visualisation to enable business users to effectively communicate and narrate findings and insights relevant to business contexts.

This subject introduces the fundamentals of machine learning principles and practices. It covers the concepts of supervised and unsupervised learning, and how the trained model can be deployed in an application.

This subject introduces students to the fundamental principles of deep learning and how it is applied to a collection of computer vision tasks to implement object recognition. It covers the concepts and architecture of convolutional neural networks such as the various layers within, and the hyperparameters involved, using available tools and libraries.

This subject provides students with insights on the usage and implications of AI in daily life. It touches on the risks of applying AI without a certain set of moral and ethical principles, and discusses issues brought about by machine learning, such as the four types of bias: sample bias, prejudice bias, measurement bias, and algorithm bias.

This subject covers the concepts of Distributed System Architecture like Service-Oriented Architecture, Representational State Transfer (REST) and Web Services, identification of technology and design principles for connected devices as well as prototyping techniques for developing web services.

This subject equips students with the skillsets for developing and deploying machine learning applications to a cloud platform maintained by cloud computing providers such as Amazon, Google, etc. It covers the storage of data and the use of application programme interfaces (APIs) and tools provided by the cloud platform.

This subject introduces students to the techniques of using an automation tool to automate tasks within a business process. It touches on the various use cases of robotic process automation (RPA) and provides a platform for students to creatively apply the concepts to different scenarios. It also discusses the challenges and limitations of RPA such as integration with unsupported third-party tools, security and governance, etc.

This subject equips students with the knowledge and skills to apply the concepts of natural language processing to chatbot development using available tools and libraries.

This subject equips students with the knowledge and skills to apply the concepts of natural language processing to text classification using available tools and libraries. It also explores the use of text classification in applications such as sentiment analysis and spam detection.

This subject equips students with the knowledge and skills to apply the concepts of natural language processing to speech recognition using available tools and libraries.