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 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 on the 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 helps students to understand how to be an effective team player, according to the CATME model of team member effectiveness. Students will practise writing a team charter to set the team up for success. They will also be exposed to various digital collaboration tools and best practices, practise holding effective team meetings online and offline, and work on their interpersonal communication skills.

Effective Writing for the Workplace (EWW)

The 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, follow standard format and conventions, and cite using the Harvard Style in their writing.

Data Fluency (DF)

In this module, students will be equipped with the baseline skills in using data to gain insights into the Sustainable Development Goals (SDGs). These skills are related to the data-handling processes such as formulating data-based questions, pre-processing data and analyzing 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 an SDG and/or generate some insights to support one theoretical solution to an 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 Sustainable Development Goal (SDG).

Problem Solving with Creative & Computational Thinking (PSCCT)

In this module, students will be equipped 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 Sustainable Development Goals (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

Choose an elective from a wide range of elective modules offered by various schools and departments. Dive into diverse subjects and tailor your education to your interests!

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 the Sustainable Development Goals (SDGs) or a relevant context.

Personal Branding and Career Agility (PBCA)

In this module, students will be equipped 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 (e.g., CareersFinder, LinkedIn Learning, and more) and curriculum co-developed by industry partners, students will begin their journey into the world of work.

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 Sustainable Development Goals (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. They 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 Sustainable Development Goals (SDGs).

Mathematics for AI

The module equips students with key mathematical concepts in data mining, machine learning, data preparation, and model building. Students will delve into topics such as linear algebra, principal component analysis (PCA), differentiation, applications of derivatives and partial derivatives. Additionally, students will gain proficiency in numerical algorithms used to solve mathematical equations and programming skills to carry out higher-order differentiation.

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.

Data Visualisation

This module aims to equip students with techniques for creating impactful reports and interactive dashboards, enabling organisations to extract deeper insights from their business data. Students will learn best practices for developing effective data visualisations using popular industry tools such as Tableau and Python. By the end of this module, students will be proficient in building and deploying comprehensive visual reporting solutions that drive informed business decisions.

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.

Agile MLOps

This module provides students with an overview of Agile methodologies and Machine Learning Operations (MLOps) to enhance the efficiency and reliability of machine learning (ML) project development and delivery. It covers essential practices for planning ML project using the Agile framework, managing data and ML models, developing ML applications with continuous integration and deployment, and monitoring of ML models in the production environment. Students will learn Agile project management, MLOps and Container tools and gain hands-on experience in automating workflows to build robust machine learning projects.

Elective 2

Choose an elective from a wide range of elective modules offered by various schools and departments. Dive into diverse subjects and tailor your education to your interests!

Elective 3

Choose an elective from a wide range of elective modules offered by various schools and departments. Dive into diverse subjects and tailor your education to your interests!

Generative AI with Large Language Models

The module introduces students to Generative AI and Large Language Models (LLM). The module offers a deep dive into Transformer architecture, the backbone of LLM. Students will extend their knowledge beyond theory, gaining practical skills in fine-tuning LLMs and developing applications using LLM frameworks like LangChain, LlamaIndex, HayStack, RAGatouille and Embedchain. They will also master techniques such as prompt tuning and prompt engineering to refine LLM output, and interface LLMs and agents with their datasets to create AI applications.

Graph Analytics for Data Science

This module aims to teach students the fundamental concepts of graph theory and knowledge graphs, equipping them with the skills to effectively design graph data models, write graph query language and build graph applications. Students will learn how to apply the algorithms such as pathfinding, community detection, centrality measures to solve real-world problems. Through hands-on exploration, students will gain a deep understanding of graph traversal algorithms and their practical applications, enabling them to extract valuable insights and meaning from complex graph structures.

Big Data Technologies

This module provides students with an understanding of what Big Data is, why it is needed and how Big Data pipelines are being implemented using Apache Spark. Apache Spark is a powerful platform that provides ways to store and utilize big data. Students will analyze data using Hadoop & Spark SQL and demonstrate how to create a streaming analytics application using Spark Streaming.

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

University module conducted by the Accelerated Pathway Programme (APP) partner universities.

Computing Elective 2

University module conducted by the Accelerated Pathway Programme (APP) partner universities.

Computing Elective 3

University module conducted by the Accelerated Pathway Programme (APP) partner universities.

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.