What You'll Study

Thinking Critically about the UN SDGs (TCU)

This module aims to equip students with skills in critical and analytical thinking through the exploration of contemporary local and global issues, through the lens of United Nations Sustainable Development Goals (SDGs). Using information literacy skills, students will select a local issue connected to the SDGs, and look for relevant information on the issue. They will use a critical thinking framework to evaluate a diverse range of sources for their validity and reliability, and consider different perspectives in an issue before presenting their assigned stand, substantiated by reasons and evidence. This module also aims to enhance students’ knowledge and appreciation of local and global issues related to the 2030 global agenda, represented by the SDGs. Students will learn about the core principles of the SDGs and find out how the goals apply to their own lives, as well as to Singapore.

Collaboration in the Digital Age (CDA)

This module aims to equip students with the skills to collaborate effectively in a team, particularly in a digital environment. This is crucial in light of the global shift to working online precipitated by the COVID-19 pandemic. Students will learn about team dynamics at various stages of team development. They will use Microsoft Teams to collaborate online. They will also evaluate their own teamwork skills and that of their teammates using the CATME framework.

Data Fluency (DF)

This module aims to equip students with baseline skills in using data to gain insights into the UN SDGs. These skills are related to the data-handling processes such as formulating data-based questions, pre-processing data and analysing data using numerical and graphical summaries. Upon the completion of this module, students should demonstrate the competency in using data to examine one aspect of a UN SDG and/or generate some insights to support one theoretical solution to a UN SDG.

AI and Its Impact (AII)

This module aims to equip students with a very broad overview of Artificial Intelligence, and its societal and ethical impacts in modern society. Students will build a simple AI model with online tools and discuss how to promote good ethics in AI. Upon the completion of this module, students should demonstrate the competency of using AI to address their chosen UN SDG.

Problem Solving with Creative & Computational Thinking (PSCCT)

This module aims to equip students with baseline creative and computational thinking skills for problem solving. Students will learn about the value of focused & diffuse thinking, problem identification and analysis, creative thinking, and the four pillars of computational thinking in the problem-solving process. Students will apply these techniques to tackle wicked problems related to the United Nations Sustainable Development Goals (UN SDGs).

Mathematics

Equips students with knowledge in mathematics and analytical skills to solve problems related to infocomm technology. Topics include matrices, linear transformation, number systems, set theory, logic, Boolean algebra, techniques of counting and probability.

Statistics for Data Science

Provides students with an introduction to elementary probability theory and statistical concepts and principles that lay the foundation to understand and learn the statistical procedures and methods. Topics include descriptive statistics, rules of probability, probability distributions of discrete and continuous random variables, sampling distributions, statistical estimation and hypothesis testing.

Education and Career Guidance

This module aims to help students discover their strengths, values and interests. It also supports students in making informed educational and career choices to achieve their career aspirations.

Front-End Web Development

Aims to equip students with the knowledge and skills in developing effective front-end web applications using Hypertext Markup Language (HTML) and Cascading Stylesheets (CSS). Students will learn to use front-end web development frameworks to further enhance their ability for rapid prototyping responsive web application.

Back-End Web Development

Aim to equip students with the skill in developing database driven web application. Students will learn about server-side programming and be able to create-database-driven web applications using a scripting language and programming frameworks.

Fundamentals of Programming

Aims to help students pick up a programming language and learn how to solve and automate tasks through programming. Students will be taught programming fundamentals such as variables, data types, operators, control structures, methods and data structures such as arrays. At the end of the module, students will be competent in using programming for problem solving.

Programming for Data Analytics

Provides students with the fundamental skills to code applications to retrieve, manipulate, process and analyze data using the Python programming language. Students will learn to code in the Python Programming Language and key concepts such as exploratory data analysis and perform data analysis using Numpy, Pandas and Statsmodels on the data to gain useful insights for business decisions.

AI & Machine Learning

Provides students with the fundamental concepts in Artificial Intelligence (AI) and Machine Learning. The module aims to provide students with hands-on experience in building applications that use machine learning and neural networks. The students will also learn skills to build intelligent agents, such as Chatbots and integrate cognitive service APIs to add intelligence into their applications.

Fundamentals of Computing

This module aims to provide students with an understanding of computer networking concepts and hands-on sessions on with operating systems using Command-Line Interfaces. Students will be taught on the use of various UNIX commands / system tools for user management, software installation, network administration and configuration of services. These topics are essential and prerequisite to an Application Developer for building and deployment of a software system.

Elective 1

Digital Communication for Impact (DCI)

This module aims to equip students with key skills to leverage digital communication to engage, inspire and impact stakeholders digitally. They will build an understanding of various digital communication platforms/tools and their benefits and limitations. They will also learn what it means to be a responsible Digital Citizen. Upon the completion of this module, students should demonstrate the competency to design and apply key principles when crafting impactful digital communication about United Nations Sustainable Development Goals (UN SDGs) or a relevant context.

Effective Writing for the Workplace (EWW)

This module aims to equip students with effective written communication skills necessary for the workplace. At the end of the module, students should be able to demonstrate effective writing skills. They should be able to apply the principles of PAC (Purpose, Audience & Context) and 5 Cs of Communication (Complete, Clear, Concise, Courteous and Correct) to plan and craft effective business messages. In addition, students should be able to use effective visuals, employ appropriate language and follow standard format and conventions in their writing.

Personal Branding and Career Agility (PBCA)

This module aims to equip students with the requisite skill sets, knowledge and tools that will give them an early start on the process of kick-starting their career. Through the Self-Directed Learning Approach, student-centric engagement, curated resources (eg. Learning Advisement Portal, LinkedIn Learning, and more) and curriculum co-developed by industry partners, students will begin their journey into the world of work. Upon completion of the module, students should be able to develop professional skills and build interpersonal relationships, acquire the skills to seek and secure work, develop the ability to adapt and respond effectively to changes at work, and appreciate and respect the diversity of individuals, culture and work environment.

Sustainable Innovation Project (SIP)

This module, tapping on what students have learnt in the Common Core Curriculum, aims to equip students with a Design Thinking mindset to create an innovative solution to tackle local societal issues mapped to the UN Sustainable Development Goals (UN SDGs). Students collaborate in multi-disciplinary groups to apply design thinking method and tools backed by research and qualitative data, to create sustainable and innovative prototype solutions for real-life issues faced by a local user in need. In the process, they develop a better understanding of themselves, and empathy for a local user in need. Students will also be able to apply their teamwork skills in the course of their sustainable innovation project, and communication skills in the presentation of the project

Persuasive Communication with Data Storytelling (PCDS)

This module aims to equip students with the baseline skills to craft compelling narratives using effective data storytelling techniques. Students will generate data visualisations, and apply narrative elements and persuasive delivery techniques to influence others or shape their perspectives. Upon completion of this module, students should demonstrate the competency in using persuasive communication to convince stakeholders intellectually and affectively to call them into action in tackling the UN SDGs.

Mathematics for AI

Equips students with key mathematical concepts in data mining, machine learning, data preparation and model building. Students will learn linear algebra, singular value decomposition (SVD), and principal component analysis (PCA). Students learn the numerical algorithms to solve mathematical equations. This module also provides students with knowledge of how linear algebra is applied to neural networks.

Data Engineering

The module covers the fundamental concepts to build and work with data pipelines. Students are taught how to work with relational database, non-relational (NOSQL) database, and large data stores such as data warehouses and how to integrate data from multiple data sources into a single repository using Extract, Transform and Load (ETL) workflows. Students are also introduced to concepts of big data and its related challenges.

Data Visualisation

Teaches students techniques to generate reports and dashboards that aid organisations to gain deeper insights into their business data. Students will learn best practices for creating effective data visualizations to support strategic data analysis and data-driven decisions using popular industry software such as Tableau and Python.

Deep Learning

This module teaches students deep neural network architectures and its practical applications. Students will learn to frame problems and prepare machine trainable datasets. They will apply deep learning frameworks such as Tensorflow, Keras, and PyTorch, to train deep learning models. Eventually, students will create and train their own deep learning models in various applications such as image recognition using convolutional neural networks, natural language processing using recurrent neural networks, creative deep learning using generative AI, as well as problem solving using reinforcement learning.

Data Structures & Algorithm (AI)

Aims to teach students advanced Object-Oriented concepts and data structures and algorithms using Python. Through this module, students will learn how to implement stacks, queues, linked lists, dictionaries and solve problems using these data structures. Algorithms to improve code efficiency and search will also be taught.

Practical AI

This module teaches students practical AI skills such as data preparation and feature engineering using cloud platforms for a variety of domains. This module equips students with skills to apply AI to vertical industry domains such as digital marketing, cybersecurity, Fintech and advanced manufacturing. It introduces students to the processes using data from social media platforms. They will also learn to perform in-depth analysis on the data to improve digital marketing.

DevOps & Automation for AI

Aims to provide students with DevOps knowledge in integrating their AI applications with docker and containerized cloud services such as kubernetes. Automating the AI workflow through Infrastructure-as-Code automation tools and services is essential for bringing AI code into production. Robotic Process Automation (RPA) is another software automation tool that enabled AI to be integrated with diverse data sources and service endpoints.

Elective 2

Elective 3

Internship Programme

Provides students with work exposure to an industrial environment, with opportunity to relate what is taught in the classroom to actual work situation. It creates a valuable learning opportunity for students to sharpen their skills and knowledge, as well as contributing to the development tasks of participating organisations.

Applied AI and Analytics Project

Provides students an opportunity to integrate the knowledge and technical skills they have acquired from the course, and experience the AI and Analytics workflow (Problem Framing /Data Exploration; Preparation / Data Engineering / Feature Engineering / Analytics and Computational Modelling / Data Analysis / Data Visualization), problem solving, project management, communication and working in a team to implement an AI and Analytics project. The project can be based on any AI or Analytics application area, subject to the approval of the school.

Computing Elective 1

Computing Elective 2

Computing Elective 3

Internship Programme

This module aims to provide students with work exposure to an industrial environment, with opportunity to relate what is taught in the classroom to actual work situation. It creates a valuable learning opportunity for students to sharpen their skills and knowledge, as well as contributing to the development tasks of participating organisations.

Applied AI and Analytics Project

Provides students an opportunity to integrate the knowledge and technical skills they have acquired from the course, and experience the AI and Analytics workflow (Problem Framing /Data Exploration; Preparation / Data Engineering / Feature Engineering / Analytics and Computational Modelling / Data Analysis / Data Visualization), problem solving, project management, communication and working in a team to implement an AI and Analytics project. The project can be based on any AI or Analytics application area, subject to the approval of the school.

Computing Elective 1

Computing Elective 2

Computing Elective 3