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Analytics Practicum course is the practicum associated with the Analytics 2nd Major. Students pursuing any of the 5 specialized tracks and the ones with No track will work on real world data from industry and create a problem definition, develop the analytical approach / methodology, apply industry tools to create a solution, and present it as a report and succinct presentation. Practicum will help Students learn, end to end analytical skills needed to turn raw data into business insights in order to facilitate decision making and be able to appreciate what analytics can help to achieve in their area of specialization.

The Internet. Of. Things. A world of smart objects. A world in which trillions of devices can sense, communicate, and collaborate over the Internet. A world where the physical and digital worlds are fused seamlessly into a networked matrix, where everything is interwoven and intertwined and interconnected in one colossal network. In this course, we embark on an adventure; sometimes challenging, always exciting. We discover the essential elements of the Internet of Things. Sensors. Actuators. Embedded devices that unite the cyber and physical realms. The Internet of Things technologies that weave and interconnect devices together into the global tapestry of the World Wide Web. How might we apply our newfound skills to various application domains? Through an Internet of Things prototyping project, we unleash our creative energies, our youthful idealism, and our capacity to dream, by designing, developing, and deploying an Internet of Things prototype, targeting a specific domain such as Homes, Buildings, Education, Leisure, or Social networks. This is a journey into the unknown. Yet, a still, small voice deep within us compels us: Courage! Do not be afraid! Put out into the deepest oceans and brave the stormiest seas! Let down your nets for an awesome catch! The adventure reaches its peak at the project showcase, where we witness the work of human hands come to fruition, leaving us inspired to reflect deeply and broadly about how we, as global citizens, can harness the power of the Internet of Things as a potent force in the service of humanity. Sounds lit? Bring some bubble tea, come and see: what’s the tea with IoT? #Slayyy

Computational Thinking equips students to tackle complex computational problems; it trains students to design solutions to solve those problems using a computer program. It draws upon concepts from mathematics and computer science – more precisely, discrete mathematics, data structures and algorithm design. This course will hone students’ analytical skills as they are challenged to think abstractly and computationally. Their minds will be open to the wonders of computing, as they go behind the scene to unravel the fundamental analytics that empower Google, consulting agencies and service companies. NOTE: To facilitate learning in this course, you are required to know and use programming. You are advised to pick up the Python programming language before the course, for instance by practising with online tutorials such as http://learnpython.org. By taking this course, students will: • discover the science of computing • model problems and learn practical problem-solving techniques to tackle complex computational problems (beyond what a spreadsheet is capable of solving) • apply problem-solving techniques to develop more elegant and efficient programs • learn to write programs to represent and manipulate with complex data objects • understand the challenge of scale, not only in dealing with large data sets, but also in appreciating the nature of computing and computability

This course equips students with both foundational computer programming skills and computational thinking skills, through the use of Python, a widely-used programming language. Upon successful completion of this course, students will understand and be able to appropriately apply fundamental programming concepts including variables, functions, parameters, loops and conditions to solve computational problems. The students will also be introduced to basic data structures including arrays (lists in Python) and hash tables (dictionaries in Python). In addition, students will receive a gentle introduction to computational complexity and apply the notion of complexity to analyse simple algorithms.

Data Science has become one of the main drivers in transforming businesses and organizations. With proper data collection, preparation, analysis and modelling, insights can be achieved leading to better decision making and actions that create positive impact. Data analytics is divided into different levels namely, descriptive analytics, predictive analytics, prescriptive analytics and automated (or embedded) analytics. In this course, students will acquire practical skills in modelling and analysis to resolve business problems using software tools including Excel and Tableau. Knowing how to effectively use these tools to build models and analyse data to solve problems will add tremendous value in our students’ future professional career. This course’s primary focus is on using Excel spreadsheet as a platform to build mathematical models from scratch to represent business problems for detailed analysis. The use of such models to drive understanding and consensus towards generating insights and actions enhanced the assurance of execution success. With the data collected, data manipulation and transformation will be needed to prepare the data into useful forms for analysis. In terms of data analytics, this course will cover descriptive analytics, predictive analytics and prescriptive analytics, using both Excel and Tableau. In-class exercises would be used to present business problem modeling and analysis. Students would interactively develop the skills and experience to deal with open-ended questions, unclear assumptions and incomplete information. In addition to the individual assessments (take-home assignments and in-class quizzes), a group project will allow students to apply the knowledge and skills acquired to solve a business problem of their choice end-to-end where they will define the business questions, collect the necessary data, build the models, perform the data analytics, to draw insights and conclusions.

Artificial Intelligence (Artificial Intelligence) aims to augment or substitute human intelligence in solving complex real world decision making problems. This is a breadth course that will equip students with core concepts and practical know-how to build basic AI applications that impact business and society. Specifically, we will cover search (e.g., to schedule meetings between different people with different preferences), probabilistic graphical models (e.g. to build an AI bot that evaluates whether credit card fraud has happened based on transactions), planning and learning under uncertainty (e.g., to build AI systems that guide doctors in recommending medicines for patients or taxi drivers to “right\" places at the “right\" times to earn more revenue), multi-agent systems (e.g., to build next generation patrolling systems for critical infrastructure security), image processing (e.g. to build systems that track and/or recognize suspicious people) and natural language processing (e.g., to build chat bots that can automatically and intelligently interact with customers in different service industries).

Machine Learning is one of the fundamental subjects in the field of Artificial Intelligence. Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., learning to recognize image or speech, classify text documents, detect credit card frauds, or drive autonomous vehicles). This course covers both fundamental theory and practical algorithms for machine learning from a variety of perspectives. It includes a range of topics, from supervised learning (such as classification and regression) to unsupervised learning (such as clustering), and from traditional (shallow) learning (such as support vector machine) to recent state-of-the-art deep learning methods. The course is intended to prepare students for basic understanding of machine learning fundamentals and equip students with capability of applied machine learning techniques for real applications. Students are strongly encouraged to have proficiency in IS103 Computational Thinking prior to reading this course. NOTE: While this is an introduction course, it is a technical course and it is highly recommended that students are proficient in programming, probabilities, statistics and linear algebra (e.g., CS103 Linear Algebra for Computing Applications, CS105 Statistical Thinking for Data Science, CS201 Data Structures and Algorithms and CS202 Design and Analysis of Algorithms).

This course introduces students to computational concepts and basic programming. Students will learn the basic programming constructs, and programming techniques to solve problem. An imperative language called C is used as the vehicle of exploration. There is an emphasis on producing clear, robust, and reasonably efficient code using top-down design, and effective testing and debugging.

This course focuses on fundamental concepts of developing programs using an object oriented approach. There will be an emphasis on writing clean and efficient code, and the ability to use an appropriate data structure or algorithm to solve problems. The Java programming language will be taught in depth.

This is an introductory course in Linear Algebra. It teaches the mathematical foundations of Linear Algebra so as to illustrate their relevance to computer science and applications. It prepares the students for advanced numeric methods in computing, especially in machine learning and data analytics.

Information security is not just a technical issue but a critical policy and business issue. The purpose of this course is to help students acquire the knowledge and build up the capability to understand and analyse the key policy and business issues pertaining to information security threats and countermeasures, based on a solid understanding of cyber security ecosystem along with the dynamics of technology innovation and in the context of digital transformation. We approach this purpose by integrating four learning components, including technical countermeasures designed to protect information systems, economics theories as the foundation of information security policy, case studies and getting-into data throughout the teaching process. It is recommended that students have the capability to use at least one of the data processing and analytics tools, e.g., Excel. Students should have basic knowledge about computer system, software and networking.

This course serves as an introduction to the theory of discrete mathematics, which lays the foundation for computer science courses such as data structures, algorithms, relational database theory and cryptography. The topics covered in this course are mathematical logic, elementary number theory, recursion, set theory, functions, combinatorics and graphs. The course will consist of lectures and tutorials to help students understand the covered topics.

This course is an introductory course in probability and statistics. It lays the mathematical foundation to prepare the students for computer science courses and their applications, in particular data science and related areas such as machine learning and artificial intelligence. The main topics covered in this course include probability, random variables, limit theorems, statistics, regression and inference, coupled with hands-on activities to illustrate their relevance to data science.

CS106 is an introductory course in computer architecture. It aims to develop an understanding of the hardware environment upon which computing is based, and the interface it provides to higher software layers. The course also introduces basic architectures and hardware-software interfaces of embedded systems. Students will understand a computer system's functional components, their characteristics, and interactions, and acquire hands-on experiences on embedded system programming. Knowledge and experience on C programming is required.

This course builds on students’ earlier programming experiences, moving beyond syntax and logic, to the question of how to build “better” programs focusing on organizing data and designing algorithms for efficiency. The materials as well as the assignments rely heavily on proficiency with Java programming language. Students will learn: • the concept of efficiency, why it is important for programs to be efficient and scalable, how to manage the trade-offs of computational time as well as resources such as memory, and how to compare the efficiency of various algorithms, • problem-solving through judicious organization of data, how abstract data types allow encapsulation and localization, as well as how their concrete implementations in the form of various data structures allow for efficient access and modification of data, • algorithmic strategies such as divide-and-conquer, iteration, recursion, randomization, etc., to achieve more effective problem solving and more efficient solutions. This course is different from Computational Thinking, as it goes into the theoretical underpinnings of efficiency, covers more data structures, and delves deeply into the implementations of those data structures.

This course builds on students’ earlier programming experiences, mathematical knowledge (discrete math and linear algebra), and knowledge of data structures, to the question of how to solve problems by designing algorithms for efficiency. The materials as well as the assignments rely on proficiency with Python programming language. Students will learn: · The different paradigms of algorithm design such as greedy, divide and conquer, and dynamic programming. · Limits of algorithm design via a study of intractability including reductions of given problem to known problems. This includes knowledge of NP/co-NP. The approach will not be via the more rigorous Turing machine/Formal language approach as the students do not have that prerequisite knowledge. · More modern algorithm design concept such as randomization and approximation to achieve more effective problem solving and more efficient solutions. This course will go into the theoretical underpinnings of efficiency, algorithm correctness, and how algorithm design has a basis in identifying mathematical properties of the problem.

This course exposes students to web and micro services development, software design issues, agile processes and project management techniques. The focus is to allow students to experience agile software development and project management by working in a team to develop a web based application.

This course aims to introduce the concepts, design principles and architectures of modern operating systems. The topics will focus on the management of computing resources, including process, memory, storage and file system. Various algorithms for resource scheduling, synchronization, caching and failure recovery will be discussed. Android operating system will be used as the platform for system programming.

This course introduces students to the core concepts and skills underlying successful software product management. Students will learn about the distinct characteristics of software products vis-à-vis other industrial artefacts, and how these characteristics can be leveraged in managing the software product life cycle. With an emphasis on the elements of the software product management framework, students will acquire hands-on product management skills through classroom instruction, term projects, and presentations.

The IT Solution Architecture course integrates design concepts and methods to develop IT solutions from both the software and system-level perspectives. It focuses on the analysis, design and implementation of an IT solution through which business requirements, software qualities and solution elements are transformed into implementable artefacts. By combining critical analysis with hands-on design and development, the course prepares students to participate effectively in the architecture design and development stages of a software-intensive IT solution project. It is highly recommended that students are also proficient in IS442 Object Oriented Programming and Java programming language prior to reading this course.

Historically, the software development process was linear, and code was deployed infrequently. Today, it couldn't be more different: companies like Amazon reportedly deploy new code every 11.7 seconds, and software development culture in general has shifted towards iterating with agility. In IT Solution Lifecycle Management, students will be introduced to some state-of-the-art practices for building, testing, deploying, and maintaining software, in a way that supports frequent and rapid iterations. In particular, they will study the 'DevOps' approach, which embodies the idea that development and operation teams should work closely together throughout the entire software lifecycle. As well as studying examples of this culture through some real-world case studies, students will also gain hands-on experience, by learning how to build loosely coupled systems based on microservices, and automating the process of testing, containerising, and orchestrating them using a modern continuous integration / continuous delivery (CI/CD) pipeline.

A key challenge in many AI systems is being able to handle (reason, plan or learn) problems with uncertainty. For instance, a self-driving car needs to handle imperfect visibility of the world and also uncertainty about the movements of other cars or obstacles. Similarly, in aggregation systems (e.g., Grab, Food Panda), positioning supply (taxis, delivery boys) at the right locations requires handling uncertainty about customer demand. Playing strategic games like Go, Chess etc. requires learning strategy that works against opponents whose strategy is not known in advance. This course will equip students with core concepts and practical experience in doing reasoning, learning and planning in the presence of uncertainty.

Search and optimization are the fundamental building blocks of AI. Almost all problems in AI (including machine learning) involve some sort of search or optimization. This course will cover the basics of search and optimization. Broadly, the course will be split along two axes: one is discrete vs continuous optimization, and another is heuristic methods vs exact techniques. The applications of the topics covered in class is immediate, for example, shortest path problems are used by all mapping services such as Google Maps. Travelling salesman problems are used to design routes for parcel delivery. Fundamental topics such linear and convex programming with gradient descent will help in understanding techniques in deep learning which will be useful for other AI courses. Recent applications of linear programs such as in designing security of critical infrastructure will be discussed in class.

Generative AI has revolutionized how we create and manipulate visual data, enabling machines to generate images, videos, and 3D models that are increasingly indistinguishable from reality. This course delves into the cutting-edge techniques and models that drive generative AI in the visual domain, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models. Students will explore the core mathematical and computational concepts behind generative AI, including deep learning and probability theory. The course also emphasizes practical implementation, allowing students to build generative models that can synthesize images, enhance existing visual content, and even create novel, realistic objects. By the end of the course, students will not only have a deep understanding of how generative models are designed and trained but will also critically assess their applications and limitations in various fields.Students are expected to have a good mathematical foundation and programing skills. Foundational Mathematical Courses (i.e., CS103 or CS105) will be an advantage but are not insisted on. The primary programming language of the course is python. Having a good GPU at home will help with course work.

Natural Language Communication (NLC) is the convergence of a diverse set of human language technologies that enable computer systems to interact reasonably with people in a natural and human-like way. NLC requires considering human language as the central part of communicative channel, where the computer should be able to perform a series of language processing actions: - It should correctly process our written or spoken utterances as input in order to respond accordingly; - It should allow technology to understand complex sentences, which may contain multiple pieces of information and many turns of requests; - It can then react by reasoning and/or interrogating and synthesising various data from third-party systems or external knowledge, and use that information in generating sensible responses. In this course, we will cover diverse fundamental methods and techniques across the themes of natural language processing, understanding and generation that are indispensable for constructing modern NLC systems. We will be focused on introducing and discussing the underlying computational models, data resources, toolkits, and practising them in developing interactive information seeking, dialogue, and cross-language communication systems. This includes but is not limited to exploration of a few conversational AI applications such as question answering, chatbots, virtual personal assistants, and dialogue management.

In this course, we introduce agent-based modeling and simulation (ABMS) as an approach for studying complex business and social processes. With examples from domains such as transportation, economics, finance, and urban planning, we show how ABMS can help us better understand complex business and social phenomena. ABMS systems are particularly powerful if we want to describe a system populated by many independent and heterogeneous decision makers (who can be collaborators or competitors). ABMS systems can also be used in performing policy evaluations and generating decision supports, as we can then computationally test how changes in parameters at different levels would affect various performance indicators. Besides covering theoretic foundations of ABMS, we focus heavily on hands-on learning as well. In particular, we will expose students to NetLogo, an intuitive yet powerful modeling language for building ABMS systems. We will be learning NetLogo by building several classical ABMS examples incrementally in class. Objectives Upon successful completion of this course, a student will be able to: • Understand what is an ABMS. • Evaluate the pros and cons of using an ABMS system in describing selected real-world phenomena. • Utilize ABMS systems in policy/strategy evaluations. • Appreciate the importance of considering uncertainty and opponent modeling when designing strategic, tactic, and operational policies. • Complete the full cycle of building an ABMS system using the NetLogo programming language: o Design an ABMS system with a proper level of granularity and fidelity (defining agents and means of communications). o Validate and calibrate the built ABMS. o Interpret the outcome of the ABMS system.

With the advancement of systems like GPT, artificial intelligence (AI) techniques are anticipated to significantly impact various aspects of individuals' lives. While these AI techniques have demonstrated remarkable, occasionally superhuman, performance across numerous applications, there is a growing concern regarding their safety and security. It has been shown AI systems are subject to a range of attacks, ranging from adversarial attacks (i.e., perturbing an input slightly causes an AI to make completely wrong predictions), backdoor attacks (i.e., backdoors can be easily embedded in neural networks), and privacy-violating attacks such as membership inference attacks (i.e., an adversary may reliably infer whether a certain sample is used during training or not). In addition, AI systems can inherit or amplify biases present in their training data, potentially leading to unfair or discriminatory outcomes. Furthermore, many AI models, including GPT, are complex and not easily interpretable. It makes understanding how these models make decisions highly nontrivial, even though it is crucial for trust and accountability. This course aims to present a systematic view on the range of AI safety problems that have been identified, analyse their root causes, and study potential approaches to mitigate the safety and security risks. In particular, we will focus on answering two key questions. First, given an AI system, how do we systematically evaluate its safety risk? Second, given an AI system that potentially has safety issues, how do we systematically mitigate the risks? This course will feature real-life AI safety issues on popular AI systems such as ImageNet, GPT and so on.

The Foundations of Cybersecurity course provides fundamental knowledge and technical skills for protecting computing and networking systems against various cyber-attacks. Topics covered include cryptographic algorithms, public key infrastructure, network security, authentication, access control, web security basics, and malware basics. Classroom instructions will be integrated with hands-on exercises and group projects.

Built on the foundation of the Information Security and Trust course (IS302), this course draws on hard-won experience to explain the latest developments in security protocols, network security, web security, application security and industrial standards. Classroom instruction and discussion will closely integrate technical principles with real world applications such as secure e-banking, secure corporate networking, secure messaging in healthcare environment and multimedia system security. In addition, case studies will be used to demonstrate that security and trust are not only for protection of information assets, but also means of improving business operation or even starting new businesses. Besides the textbook knowledge, this course also brings to the classroom security practices in industries, e.g. Microsoft, and government agencies (e.g IRAS and CSIT).

This course introduces undergraduate students to fundamental access control techniques that are essential to ensure data security and privacy. The focus of this course is on (A) access control on mobile platforms, and (B) access control on cloud. The topics to be covered in the area of access control on mobile platforms include mobile platform security model, Android overview, SELinux, and SEAndroid. The topics to be covered in the area of access control on cloud include an overview from PKI to ABE, math foundation and IBE, fuzzy IBE, KP-ABE, and CP-ABE.

Software and systems security aims in equipping students with the fundamental concepts in software and systems security, as well as basic hands-on skills in understanding, analyzing, and protecting a software program and a computer system. Each lesson spends roughly 50% of the time on fundamental concepts (lecturing) and 50% of the time on hands-on exercises/assessments. Assessments focus on hands-on projects.

Cyber threat intelligence can potentially be a force multiplier for organizations looking to update their cyber posture, specifically their response and detection programs to deal with increasingly sophisticated advanced persistent threats. Adversaries are constantly developing new tools, techniques, tactics and procedures to bypass security mechanisms successfully. Cyber threat intelligence empowers cyber defenders on countering those flexible and persistent threats with a good understanding of the attacker’s behaviour, motivation and playbook. During an attack, an organization needs a top-notch and cutting-edge threat hunting or incident response team armed with the threat intelligence necessary to understand how adversaries operate and to counter the threat. Cyber Threat Intelligence course will equip students with a brief understanding of various aspects of cyber threat intelligence – in the tactical, operational, and strategic level cyber threat intelligence skills and tradecraft required to make security teams better, threat hunting more accurate, incident response more effective, and organizations more aware of the evolving threat landscape.

The Offensive Cybersecurity course aims to equip students with knowledge and technical skills in the fields of Penetration Testing and Offensive Security Tactics. The course will introduce the tools used in these fields and provide students hands-on experience to accomplish a mock engagement. Students will also learn the reporting structure and professional standards of a Security Engineer. The course also aims to equip students with skills that can be used and/or transferred to professional certifications exams/courses recognized by the industry. Upon completion of the course, students will be able to: ❖ Apply the key tools and methodologies used in Offensive Security ❖ Assess the ethical considerations to perform Offensive Security ❖ Analyse and evaluate Offensive Security test projects ❖ Satisfy the reporting requirements for Offensive Security

As digital technologies pervade every aspect of society, the prevalence of cybercrime and technology-enabled offences has surged. Digital forensics is the science of identifying, collecting, preserving, analysing, and presenting digital evidence in a legally sound and systematic manner. In this course, students will be introduced to the principles and practices that underpin modern digital forensics. They will learn how to investigate compromised systems, recover deleted or hidden information, trace cyberattacks, and handle evidence so that it remains admissible in court. Beyond technical tools, the course also addresses the ethical, legal, and organisational dimensions of digital investigations. Through a combination of lectures, case studies, and hands-on labs, students will acquire both conceptual understanding and practical experience in forensic analysis of filesystems, networks, and mobile/cloud environments.

Cyber-Physical Systems infuse sensing, computing, networking, and control capabilities into physical objects, breathing into them new life, new purpose, and new meaning. They are present in diverse application domains: social services, food, healthcare, transportation, environmental sustainability, and more. Cyber-Physical Systems empower global communities to create meaningful impact by addressing societal challenges, in areas such as social fabric, quality of life, and sustainability. In this foundational course, we embark on an adventure; sometimes challenging, always exciting. We learn vital theories, acquire skills, and work with tools for Cyber-Physical Systems. We discover their essential elements. We explore ways to unite the cyber and physical realms. We unleash our creative energies, our youthful idealism, and our capacity to dream, by creating visionary technology to conquer a real-world societal challenge. The adventure reaches its peak at the project showcase, where we witness the work of human hands come to fruition, leaving us inspired to reflect deeply and broadly about how we, as global citizens, can harness the power of Cyber-Physical Systems as a potent force in the service of humanity.

This course aims to introduce the concept of mobile and pervasive technologies to the students while providing hands-on experience in building such systems. The focus will be mainly on the concepts of (i) mobile/pervasive computing and application development, (ii) applications of mobile systems, (iii) context-awareness, and (iv) improving usability and efficiency of mobile applications.The course examines the outline provided above, from both technical and real-world applications, hence involves the concepts of machine learning. Students of this class will be using development tools and techniques to build mobile Applications (using Android OS) and the main focus will be on making mobile applications faster (lower-latency), more energy-efficient, smarter (context-aware) and highly usable (multimodal UIs).

This course will equip students with the state-of-the-art in Internet-of-Things technologies, to enable them to conceptualize practical IoT systems to realize useful applications. Students will learn how to:identify and translate real needs into system requirements and constraintsidentify suitable IoT technologies to realize a practical system; andbuild simple proof-of-concept applications, through real-world examplesStudents may also find some background/skills in interactive design and prototyping useful (e.g., IS211 Interaction Design and Prototyping).

This course will provide an introduction to the principles and techniques of computer graphics and virtual reality, with an emphasis on interactive 3D applications. The course will cover topics such as: • Introduction to computer graphics • The graphics pipeline • 3D modeling, rendering, and animation • Ray Tracing and Illumination • Interactive Graphics • Stereoscopic perception and display systems • Human perception and multimodal interaction • Virtual and augmented reality environments and applications The course will include lectures, labs, assignments, and final projects. The students will learn how to use various tools and frameworks for developing 3D graphics and VR applications. The students will also gain hands-on experience with building their own CG and VR applications. The course will also expose the students to current research and trends in computer graphics and virtual reality.

This course exposes students to full stack development. It is designed to equip students with the skills and knowledge to build robust full stack applications using modern technologies. From backend development in Golang, to frontend user interactions with HTMX, and managing data using both SQL and NoSQL, as well as object storage, to deploying applications on the cloud using containers. Students will gain hands-on development that spans the full spectrum of web development.

This course takes a step-by-step investigation of modern analytical databases. It starts with hardware-aware storage, then moves through execution flow, indexing, cost-based optimisation, and emerging analytic tasks. The course offers lectures and guided labs to let students practise ideas such as columnar layout, vector processing, joins, estimators, and approximate answers right away. It provides an in-depth exploration of modern database systems and data management techniques with a special focus on their application in data science and artificial intelligence. Students will gain practical experience designing, implementing, and optimizing analytical pipelines, preparing them to tackle real-world data analysis challenges.

This course introduces students to Web3 technology and development through the Aptos blockchain and the Move smart contract language. Aptos is a next-generation, high-performance blockchain designed for scalability, security, and low-latency transactions. Students will be introduced to the essential concepts of Web3 with its latest development and ecosystem. They will learn the core principles of Move, including its unique resource-oriented programming model, and explore key blockchain concepts such as accounts, digital assets, smart contracts, events, and scripts. The course emphasizes practical skills in decentralized application (DApp) development using Aptos SDKs (TypeScript, Python, Go), indexing tools, and deployment pipelines. By the end of the course, students will be equipped to design, build, and deploy robust DApps on the Aptos platform.

CS470 aims to introduce students to academic research in Computer Science. It allows students to experience first-hand the challenges and exhilaration of research, discovery and innovation, and enriches their academic experience by working at/near the frontiers of research in computer science.

CS471 is to allow students to continue with the research project done during CS470 OR to embark on a new research project (and there should not be any significant overlap with your CS470 research project).

CS472 aims to train students to conduct scientific research in a specific area and prepare students for postgraduate study. The students will be guided to formulate research problems, propose research methods, develop experiment design for research validation and write scientific publication (in the form of a conference proceeding paper or journal article). Only students who are undertaking the Computing Research Programme are eligible to take this course.

This course will provide opportunity for BSc (CS) students to:- Develop a proof of concept software application or system that satisfies a list of functional and quality requirements. Students will work with a team and practice their requirement analysis, design, implementation, testing, deployment and project management skills. Build the professional knowledge depth required to become a software developer or research engineer. This includes building up competence in the student's selected track, applying their learning domain knowledge and technology to the relevant industry sectors or research areas. A CS project does not have to be tied to a specific track, i.e., it can be multidisciplinary. The project's nature and scope are set by the project sponsor. Students can work on an application development or a research project. It is advisable that for research projects, students should have GPA > 3.4.

The Computer Science Work-Study Elective is a six-month internship attachment program. During this work-study internship course, students will alternate between working at least four days at a company and studying on campus for up to one day each week (splitting into two-half days is allowed), for a period of 20 weeks or more. Students can only take at most two additional courses at school while on the internship. This elective is a pass/fail course, and is not DROPPABLE. During the internship, students will pay the full tuition fees. IMPORTANT: Students on tuition fees grant/subsidy should plan their study plan carefully so that they do not exceed the normal candidature period (i.e. 8 terms).

This course introduces Natural Language Processing (NLP) technologies, which cover the shallow bag-of-word models as well as richer structural representations of how words interact with each other to create meaning. At each level, traditional methods as well as modern techniques will be introduced and discussed, which include the most successful computational models. Along the way, learning-based methods, non-learning-based methods, and hybrid methods for realizing natural language processing will be covered. During the course, the students will select at least 1 course project, in which they will practise how to apply what they learn from this course about NLP technologies to solve real-world problems.

This course introduces students to the fundamentals of digital business, technologies and the principles and practices that lead to successful digital transformation. With the exploitation of digital technologies such as artificial intelligence, cloud, analytics, mobile networks, social media, and the Internet of Things, organizations can develop a competitive edge that can boost efficiency and drive new business models that lead to an increase in the top and bottom lines. The course focuses on digital strategies using four components namely reimagining the business, re-evaluating value chain, reconnecting with customers and rebuilding the organisation. Challenges such as data security and governance, regulatory constraints, and future directions of digital business will be discussed. Besides helping students to understand the key concepts, tools and API services are introduced to implement the digital and analytics solutions. Real world examples and case studies of how organizations innovate and drive digital transformation will also be covered.

his course prepares the students to translate their business innovations into a technology start-up company. Students will get an opportunity to learn from startups in the city of the study mission and apply the skills learnt in this course to their own start-up company. This course will help student: - Get introduced to the entrepreneurship scene. - Develop an understanding of the factors and ecosystem leading to a start-up. - Aspire to be entrepreneurs and apply their findings to their own venture. - Understand how to start a technology company. - Build relationships with the organisations we visit.

This TESM course will focus on appreciation of the broader aspects of Technopreneurship by visiting high tech start-ups in a particular world-renowned high tech centre. Some possible countries / cities for the visit are Silicon Valley, Europe. Objectives Before the trip - Understanding the culture, start up scene of the entrepreneurship center and the start ups selected for the visit During the trip - A number of factors including motivation of the founders, key innovation, key challenges faced by the founders, managing investors, learning from market feedback After the trip - How to apply the learning for the creation of a technology enabled start up. Upon completion of this course, students are expected to: - Develop deep understanding of the challenges involved in starting high tech companies, and of the best practices of managing these challenges - Learn about the differences in business environment and culture between Singapore and the trip destination, in particular in terms of creating an environment conducive for high-tech start-ups. - Gain experience in pitching an idea - Expand the potential for students’ personal network by facilitating the pursuit of internship in the companies visited or other companies in the trip destination.

Supply Chain management (SCM) is about how suppliers, producing firms and logistics service providers collaborate together to serve the final customer. Recent rapid developments in information and communications technology (ICT) have led to exciting changes in SCM and brought it from the backroom to the forefront of businesses and is recognized as a key aspect of competitive strategy. In this context, the course introduces students to large-scale, real-world supply chain processes and their key operational choices and managerial decisions. Taking business-centered rather than technology-centered perspectives, students are introduced to SCM software applications and relevant technologies. We explore strategic opportunities and practical challenges of their applications in global operations. The main focus, taking a supply chain IT user’s perspective, remains on discovering business requirements and operational constraints, and understanding how to lever the applicable technologies surveyed. Beyond the typical shipper’s logistics and supply chain integration project management concerns, selected special industry topics will be examined: third-party logistics, continental trucking, global freight shipping, logistic depot and seaport terminal operations. Objectives Upon completing the course, students will • Understand key logistics and supply chain concepts and strategies, and the role IT plays to help reduce cost and provide strategic advantages. • Know the range of information and communication technologies that supports SCM and understand how they may be employed. • Understand the complexity in the integration of supply chain activities, as well as the integration of disparate technologies deployed to support the challenges in physical coordination. Pre-requisite: None (For 3rd or 4th year students only)

This course addresses the business aspect of IT and students will learn how to apply IT to gain business value. We shall discuss key IT management issues faced by CIOs and Business leaders and how to become effective change agents– effecting changes in the organization, processes and people. We shall also cover aspects of IS management & best practices, and apply them to solve real life case problems, as well as to propose IT strategy to address the specific business challenge given by a sponsoring organization. Topics will vary from year to year, enabling the instructor to include a combination of the latest IT trends (eg. Mobile & Social Media) and emerging issues at the intersection of IT and management. along with some of the important fundamentals. Students will have the opportunity to learn from leaders (CxO) in the industry on their views on specific IT management issue, best practices, and how to be successful in their career( not limited to IT career). This course will benefit all students who aspire to be in an executive or management position, be it in IT or business functions such as finance, marketing, HR, etc. Note to SIS students: IS425 revisits in more depth similar themes that were introduced in Seminar on IS Management (IS101) in your first year –such as how IT is used in the context of complex business processes; how IT can enable value generation; and other topics related to the management of information technology and systems in business settings. There will be greater emphasis on putting what you have learnt into practice by solving real life challenges holistically. Note to non-SIS students: Final year students are very welcome to attend this course to learn how you can best leverage IT to be more successful in your professional life. This is not a technical course and there is no coding/computing skill required. However, it is important that you have keen interest in IT and are prepared to read up on some of the topics (Reference Readings will be given) to help you gain most benefit from this course.

Data analysis and communications can be fun! With visual analytics techniques and tools, everyday data analysts from various disciplines such business, economic, sociology, political science and public policy can now synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data without having to deal with complex statistical formulas and programming. Many companies and organization took notice when Gartner cited visual analytics as one of the top five trends transforming business intelligence. In this course, students learn how to use data visualization and interactive analytic tools and techniques to interact with data of different formats from various sources, explore the expected relationships and discover unexpected correlations and patterns.

This course explores the corporate and institutional banking functional domain and technology, as well as the emerging non-bank FinTech alternatives. This course module begins by reviewing banking solution delivery processes and identifying the various corporate banking customer segments and their needs. These considerations are then examined in different corporate banking business contexts. In the context of corporate customer needs, the traditional product areas of corporate lending, cash management & payments, trade finance, and corporate treasury are covered. In the later part of the course, we cover institutional banking customers and products, and their role in the credit crisis of 2008 to understand what happened and the implications going forward. Finally, we will cover cybersecurity, operational and compliance risk. Emphasis is placed throughout the course on analysing real-world situations using case studies and gaining hands-on experience with banking systems.

The better any organisation can manage the relationship with its customers, the more successful it will become. IT systems targeting the problems of dealing with customers are growing in popularity. Customer Relationship Management (CRM) is not just the use of a technology, or a hardcoded business process, it is a management strategy to help organisations understand and learn about customer behaviours, needs, preferences and expectations in order to improve and maintain a strong relationship with them.Managing Customer Relationship with Analytics: Asian Insights presents the concepts and methodologies required to execute a methodical approach to apply analytics and CRM principles into a business. The course will cover the customer-centric business culture, and the customer relationship process to attract, convert, retain and amaze customers with the help of IT tools.This course provides a comprehensive understanding of advanced business management practices, focusing on four key areas: Lead Management (Attract)Opportunity Management (Convert)IT Service Management (ITSM) using ITIL (Retain)Operations Management with a Customer-Centric Focus (Amaze)Students will explore each area from three perspectives: Customer Journey, Business Processes, and Managerial Roles. The course aims to equip students with the skills needed to excel in real-world business environments, emphasizing the importance of customer-centric strategies.

The annual expenditure on healthcare, by both individuals and governments, is expected to continue increasing within the next five years. There is thus an imminent need to stretch the effectiveness of the expenditure, and explore new innovations, which can subsequently serve the needs of the rapidly ageing population, tackle the issues associated with shortage of healthcare professionals, and achieve improved clinical outcomes. This course will explore the changing needs and trends of the healthcare industry, and how innovation can address the rising costs and inefficiencies in the healthcare systems, with a particular focus on Singapore and Asia. Students can expect to be equipped with knowledge of state-of-the-art smart healthcare technologies, as well as examine the multi-faceted impact of technology on this multi-million dollar industry, through various lenses.

This course prepares the students to translate their business innovations into a technology start-up company. Students will get an opportunity to learn from startups in the city of the study mission and apply the skills learnt in this course to their own start-up company.

This course will cover the fundamentals of relational database theory, important data management concepts such as data modelling, database design, database implementation and searches in unstructured data (i.e., text) in current business information systems for non-SCIS students. Students are expected to apply knowledge learned in the classroom to solve many problems based upon real-life business scenarios, while gaining hands-on experiences in designing, implementing, and managing database systems.

By precisely documenting, consistently updating, and efficiently tracking data, organizations can tackle challenges and harness the vast potential offered by this sector. Database management systems play a vital and essential role in both the creation and administration of data, serving as critical components for the effective operation and governance of data. This course will cover the fundamentals of relational database theory, important data management concepts such as data modelling, database design, and database implementation in current business information systems. Students are expected to apply knowledge learned in the classroom to solve many problems based upon real-life business scenarios, while gaining hands-on experiences in designing, implementing, and managing database systems. The students will be given hands-on class activities to enable a problem-based learning environment.

In this course, you will get an overview of fundamental business concepts with an emphasis on the challenges and opportunities that arise from technology and how information systems can be used to create business value and innovations.

The Advanced Data Management course exposes students to exciting data management topics and provides them with hands-on experience with widely used database tools. The course includes a technology component and an advanced topics component. The technology component comprises lab sessions, where students use a commercial database (e.g., Oracle) to solve exercises based on real-life scenarios. The advanced topics component is delivered through interactive lectures and discussions on emerging database fields, such as location-based services, preference-based query processing, etc. Objectives Upon completion of the course, students will be able to: • Understand concepts of database design, extending further than common/ standard application domains • Identify the trade-offs of alternative solutions to a particular problem, and decide the best one, depending on the particular requirements of the targeted application and the intrinsic characteristics of the underlying data and queries • Use techniques that are very useful when deploying/using/maintaining a real-world database system • Use data management tools that are widely spread in the industry • Use the acquired background to solve problems and use tools other than the ones presented in class

This course is intended for any student who wishes to gain some programming fundamentals, also known as the building blocks of Information Systems. The course introduces students to fundamental programming concepts and constructs, explains the process of developing a basic software application, and explains the basic concepts of object orientation. The student will experience the implementation of a basic software application. Python, a widely-used, high-level, general-purpose and interactive programming language, is used as the vehicle of exploration in this course.

This course will cover fundamentals of relational database theory, important data management concepts such as data modelling, database design, database implementation in current business information systems, and some basic concepts related to unstructured data. A series of in-class exercises, tests, pop quizzes and a course project will help students understand the covered topics. Students are expected to apply knowledge learned in the classroom to solve many problems based on real-life business scenarios, while gaining hands-on experience in designing, implementing, and managing database systems. Students are also expected to understand the differences between structured data and unstructured data. This course is applicable to students declaring a major from SIS.

This module requires basic programming and coding skills as covered in COR-IS1704 Computational Thinking and Programming. If you lack this foundation, please reconsider enrolling in this course.Web applications are commonly used today by governments, enterprises, and even individuals to provide information, market products, etc. Ability to create web applications is thus a crucial skill for graduates in Information Systems. This course is designed to equip students with the knowledge and skill to develop well-styled database-driven web applications. In this course, students will learn how to build dynamic, data-driven web applications, focusing on backend development logic, database integration, and security features. The hands-on learning environment will provide learning opportunities to design and build their own interactive web pages. By the end of the course, students will have the skills to develop functional web applications and understand practices in backend development.

This module requires basic programming and coding skills as covered in COR-IS1704 Computational Thinking and Programming. If you lack this foundation, please reconsider enrolling in this course. We begin our adventure by exploring the essential elements of Computing Systems — hardware, software, and network technologies — through a series of challenging yet fun learning activities. We grow our coding skills by building a networked, distributed computing system: starting with physical computing devices that unite the cyber and physical realms, we add fundamental networking capabilities for devices to exchange information and form networks, and finally integrate the system into the cloud. Equipped with newfound skills and knowledge, we unleash our creative energies, our youthful idealism, and our capacity to dream, by designing visionary technology to conquer a real-world societal challenge. This is a journey into the unknown. Yet, a still, small voice deep within us compels us: Courage! Do not be afraid! Put out into the deepest oceans and brave the stormiest seas! Let down your nets for an awesome catch! The adventure reaches its peak at the project showcase, where we witness the work of human hands come to fruition, leaving us inspired to reflect deeply and broadly about how we, as global citizens, can harness the power of Computing Systems as a potent force in the service of humanity.

A&P can be viewed as a first course in algorithms. Students will be trained to compute the time complexity of algorithms and compare algorithms using their Big-O time complexity. Besides coming up with the pseudocode of an algorithm to solve a given problem, students are expected to implement the pseudocode in Python for the project assignments. Common data structures such as stacks, queues, trees, graphs, heaps and hash maps will be covered. After students have gained a strong foundation in complexity and data structures, students will be introduced to heuristic approaches – specifically greedy and local search algorithms – that can be used to tackle computationally intractable problems.

In practice, a management decision to invest in business process modeling is often motivated by the need to document requirements for an information technology project. So this course aims to help students: - Understand and apply BPM project needs and life-cycle stages - Gain knowledge of business process and its role in an industry. - Understand business models and create models for the as-is business process. - Understand analysis techniques static and dynamic and analyse the business process - Apply dynamic analysis techniques using tools and analyse the simulation results - Identify business needs in the process and convert them to the IT needs - Understand techniques for solutioning and design solution models for the to-be process - Understand enterprises and they are implemented in an organization. - Gain understanding of the process innovation and identify the needs for process innovation. - Analyze and review innovated business processes in industry cases where digital transformation is applied.

This course introduces fundamental human-computer interaction principles and techniques for designing usable interactive systems. Topics include common methods for gathering user requirements, basic UI and graphics programming techniques, and common evaluation techniques. Hands-on experience with UI prototyping tools will be provided and students will complete a UI design and prototyping project as part of this course.

In IS212 (Software Project Management), students will learn about modern frameworks and tools for software project management. In particular, students will gain hands-on experience with ‘scrum’ and several agile techniques (e.g. test-driven development, AI-based pair programming, continuous integration) as they design and build the first release of a software system. Students will gain an appreciation for how these methods help to manage the inherent uncertainty of software projects, as well as how they ensure that developers work towards a common goal at a sustainable pace.

With the emergence of new technologies and evolution of existing ones, organizations are changing the way they build enterprise solutions. Rather than build monolithic applications, the current emphasis is on building solutions by leveraging existing functionality exposed as services. This approach to composing solutions using services follows the Service Oriented Architecture (SOA) paradigm, where applications are structured as a collection of loosely coupled services. In this course students will learn how to design and implement enterprise solutions using SOA using suitable tools. The course will cover topics such as service-oriented architecture (SOA), microservices architecture (MSA), web services, JSON/XML, cloud computing, and Enterprise Service Bus (ESB).

The Enterprise Business Solutions Course is an SMU-X course delivered in collaboration with OutSystems, offering students experiential learning opportunities by bridging classroom theory with real-world practice. Students will work collaboratively with OutSystems industry experts to address genuine business challenges, creating innovative, low-code application solutions. Guided by faculty and industry mentors, teams will progress from problem definition to a practical solution demonstration for real clients.

This course explores the elements in the IT ecosystem that is required to support enterprise systems. It is divided into three main areas: maintenance, change and disaster prevention and recovery. Using common tools in the industry for ticketing, automated testing and DevOps, students are given hands-on experience as well as the understanding for robust delivery, efficient change and deep resilience. Teams will be given their own system environment to maintain and protect. Real world use cases and examples are given to highlight the importance and complexity of managing applications in the enterprise.

This course is designed to equip students with knowledge and skills to develop well-styled and responsive web applications that provide rich user experiences. Combining with the skills learnt in IS113 course, which focuses on developing database-driven web applications with basic web designs, after this course, the students will be equipped with full stack web development skills, who can build both front-end and back-end software. In the introductory weeks of the course, the students will revisit HTML and server-side programming (PHP) concepts learnt in IS113. Then, the students will learn the concept of “Styling” the web pages. The students will learn a style sheet language called cascading style sheets (CSS) and learn how to separate the content and presentation of web pages, how to control web page layout, colors and fonts, how to bring multiple styles into a web page, how to control the layout of multiple web pages efficiently, etc. Next, the students will learn the concept of adding responsive behaviors to web pages to enhance the user experience. The students will learn a client-side programming language called JavaScript to make ordinary web elements like input boxes, buttons, forms, tables, menus interactive and animated. Furthermore, they will learn how to connect to API gateways and process data from external sources like RESTful web services so that they can build practical applications. In the latter weeks of the course, the students will be introduced to programming with frameworks. The students will learn how to use frameworks to build complex web applications in an efficient, scalable manner. More specifically, the students will be introduced to Bootstrap, a popular CSS framework for developing responsive website and introduced to Vue, a progressive JavaScript framework for building rich user interfaces.

The Application of AI in Financial Services is an SMU-X experiential course delivered in partnership with Union Bank of Switzerland (UBS), providing students with immersive learning opportunities that bridge academic theory with industry practice. Students collaborate directly with UBS professionals to tackle authentic business challenges in financial services, developing innovative AI-driven solutions across domains such as trading, risk management, and banking operations. Generative AI will be the enabling technology for the course.Working in multidisciplinary teams under faculty mentorship, students engage in a comprehensive project lifecycle—from initial problem scoping and stakeholder analysis through solution design, development, and final client presentation. This industry-embedded approach ensures students gain practical experience with cutting-edge AI technologies, i.e. large language models (LLMs), while understanding their real-world application constraints and business impact within the financial services sector.

In this globalising and competitive business environment, the value of location as a business measure is fast becoming an important consideration for organisation. GIS with its capability to capture, manage, display, and analyse business information spatially is emerging as a location intelligence tool. Today, many kinds of industries are employing GIS as an integral part of their business processes. Examples of the use of GIS in business are: • Market Analysis Which is the company’s market share in different geographical areas? Where are the customers located? What are the characteristics of customers in different geographical locations? Are there potential customers and where do they live? • Site Selection Where are the shops or branches of the company located today? Where are the competitors located? What would be the surrounding market area for a new location? What are the socio-economic characteristics of the people living in this market area? Will the establishment of a new shop interfere with the existing shop owned by the company? Are the land prices in different areas suitable for building a new shop? • Sales territories Which the company’s present division into sales territories? What kind of customers live in these districts and how much is sold? Is there a need for more salesmen in any district? Could redistricting lead to more efficient sales organisation and less travelling time for the salesmen? • Distribution and travel costs How can the distribution system be made more efficient? In what way does the transport system influence the distribution and how does it restrict or facilitate expansion in the region? Can the number of journeys made by company staff be reduced by applying route planning software? Are there any alternatives to our present distribution system? • Analysis of the global environment and new export markets What is the general economic and spatial pattern in the region? How good is the infrastructure? How is the population distributed geographically? Which other companies are present in the region and where are they located? Is this region a potential export market for the company’s products? This course provides students with an introduction to practical applications of GIS in business operations. Emphasis will be placed on (i) locating, acquiring and integrating business data into GIS, (ii) understand the principles and methodologies of the geocoding process, (iii) become familiar with geovisualisation, spatial analysis and location modelling techniques, and (iv) explore the technologies and possibilities of server-based and/or web-based GIS analysis for Business Intelligence.

Data warehousing has recently gained a considerable momentum as a paradigm for driving daily business analytics operations. This course provides an introduction to fundamental issues and novel techniques of data warehouse. Issues covered include data warehouse planning; business analytics modeling, design, and implementation. In particular, the role of data warehouse in supporting business intelligence and effective decision making is emphasized through labs, projects and case studies. The course is designed to expose students to concepts, enabling methods and hands-on usage and problem solving in an integrated way. The participants will explore applications and have great opportunity for hands-on experimentation with data warehousing using advanced software packages from leading industrial vendors.

The Financial Services Industry was among the early adopters of IT in delivering banking products and services, and in achieving operation efficiencies and increased revenues through new opportunities enabled by IT. This course introduces the retail banking environment and architecture. It delves into some of the key processes that span the front and back office of a bank and the associated banking products that require IT solutions to enable these processes. The curriculum also includes core banking as well as delivery channels such as ATM, internet and IVRS. Emphasis will be placed on Mobile Technology due to the FINTECH focus of the upcoming revamped course for AY 2017-2018 Topics such as FINTECH, banking security, customer and credit analytics, emerging technologies and industry trends will also be covered.

In a fast-paced business world, enterprise systems must adapt dynamically to environmental uncertainties. Existing plans and schedules need to be constantly updated to accommodate new requests and events under severe time constraints. Such requirements are becoming increasingly common in the service industry (transport and logistics, health-care, hospitality, to name a few). In this course, we discuss the inner working of decision analytics embedded in enterprise systems for managerial decision making and decision support. Students will acquire skills for understanding, modeling and solving such decision problems. We will cover both foundational as well as emerging methodologies. This course, together with other “enterprise” courses (such as EI, EIS and EBS) provides a holistic picture on the functioning of information systems in intelligent enterprises today and into the future. It is preferred that students are familiar with Excel (particularly the use of solver and sensitivity analysis) and have background on Analytics (eg. have taken ANLY104).”

Financial Institutions are among the most intensive and innovative users of information technology. Voice- and paper-based trading have been replaced with electronic channels linking up market participants globally. Technology has equipped traders with real-time price and market information, and enables performance of complex data analytics to advance competitive edge. Open outcry trading floors at exchanges have been replaced by automated trade matching and straight-thru-processing (STP) has replaced error-prone paper-based settlements processing resulting in shorter settlement cycles. But amid the loss of colorful trading jackets and the hype around technological advances, the fundamentals of markets, trading and risk management have not changed. And in order to provide products and services salient to the financial market community, one must understand these fundamentals. This course introduces the roles within the types of markets, products and services, and how associated risks are harnessed and managed. Focus will be placed on the foreign exchange and equities products and the processes that support the trading and settlement of these instruments. The course will include the schematic architecture and design of the systems that support these processes. Learners will be placed in multiple simulations, taking on different roles from broker, to trader to risk manager, allowing them to gain insights to the practical application of what otherwise remains theory.

Quantum computing is now being realised at an ever-increasing pace. “Quantum advantage” has been demonstrated and the underlying technology continues to advance weekly. While everyone talks about the speed of quantum computers, the power of this technology is not just in how fast calculations can be performed but also how accurate. The overall objective of the course is to understand quantum computing, how it differs from classical computing and what the main applications are, now and in the future. Emphasis is placed on FinTech/Banking applications, e.g., trade, investments. Furthermore, you can experience programming real quantum computers and explore the quantum world.

Data mining consists of a wide range of data analysis techniques that can be applied to large datasets to discover patterns, trends and other forms of knowledge embedded in the data. In the commercial world, data mining is often conducted on enterprise data stored in relational databases to help managers make informed decisions so as to keep businesses competitive and attuned to changing market conditions. With the recent advances in data generation and collection, new data types such as text, web, spatial, and temporal data have emerged creating new opportunities for mining knowledge from data for business intelligence.This course provides an introduction to the fundamental issues and basic techniques of data mining. The topics covered include data mining process, data preprocessing, data mining techniques and data mining evaluation. In particular, the use of data mining in support to business intelligence and decision making will be covered through labs, projects and case studies.Students are expected to learn data mining and its use in business intelligence through acquiring the basic data mining concepts and techniques, using them to explore data, and deriving useful knowledge patterns from the data through hands-on programming and experimentation that involve some industry strength data mining software packages.

This course will introduce the foundations required for designing and implementing solutions “in the cloud”. The course will outline a taxonomy for categorizing various cloud offerings and introduce popular examples of each. Students will then have an opportunity to deploy small solutions to various 3rd party services to gain a better understanding of the intricacies of working with different offerings. In addition, students will work on a team throughout the course to integrate multiple cloud-based services together to develop a reasonably sized modern web application that integrates specialized services in a similar fashion to currently popular web sites. Throughout the course, students will also have the opportunity to consider and analyze how building solutions on shared public platforms and integrating 3rd party services impacts security, privacy, and accountability. By the end of the course, students should be comfortable implementing information systems entirely with cloud-based services and be able to describe the advantages, disadvantages, and risks associated with their approach.

A payment is a transfer of monetary value. Under the hood of payment transactions are the products, the companies, the legal framework, the technology, and the financial institutions we rely on to facilitate the timely and uninterrupted exchange of value from one entity to another. In times of crisis, the importance of having a robust, efficient, and secure national and even global payment systems that market participants can rely on is even more pronounced. A payment system (legal definition) is an arrangement which supports the transfer of value in fulfilment of a monetary obligation. Simply put, a payment system consists of the mechanisms - including the institutions, people, rules and technologies - that make the exchange of monetary value possible. This course “Digital Payments & innovations” takes an overall look at the payment landscape viewing consumer, business and wholesale payments. It presents a depiction of the changing environment and delineates the dynamic payment ecosystem, helping us understand the possibilities as well as the limits to change. It covers payments for individuals, organizations and banks, and all of their possible permutations. The course is aimed at students who are interested in both domestic’ and cross border payment systems, particularly those who aspire to; a) work in a bank’s T&O (technology and operations) as an architect, business analyst or project manager, or b) work in a non-bank FinTech provider of alternative payment services.

In today’s globally connected, online and mobile world, social media platforms are fast becoming the dominant means of communication and it is revolutionizing the way businesses communicate with their customers. Many popular social media platforms such as Facebook and Twitter allow for instant, real-time multi-way communication. Collecting and analysing data from multiple online sources require an Information Technology infrastructure. The data collected from online sources create a gold mine for businesses that want to understand and predict consumer and market behaviour. By leveraging sophisticated computing technologies, big data analytics can produce actionable insights valuable to the core operations of the business. This course will explore emerging methods and applications for understanding online user behaviour on popular social media platforms. Students will be exposed to a variety of real-world business cases, a collection of data analytics tools, best practices and hands-on exercises. Students will learn how to 1) identify analytics problems, 2) use data analytics tools and identify types of analysis to be performed, and 3) close the loop (the process of taking the analysis results and interpreting it contextually).

Given the dominance of text information over the Internet, mining high-quality information from text becomes increasingly critical. The actionable knowledge extracted from text data facilitates our life in a broad spectrum of areas, including business intelligence, information acquisition, behavior analysis and decision making process. In this course, we will cover important topics in ext mining including: document representation, text categorization and clustering, sentiment analysis, probabilistic topic models and text visualization. Text mining techniques adopt the models from research areas such as Statistics, NLP and Linguistics. We will also focus on basic natural language processing techniques, language parsing and analysis and evaluation techniques.

The Digital Analytics Technology (DAT) is a SMU course that will be delivered in collaboration with Google. This course is designed to equip students with essential knowledge and skillsets in digital analytics so that students can apply the skillsets into practical solutions for a real organization. Besides concepts related to digital marketing and analytics, relevant measurements and development of a strategy will be covered. The course will focus on helping students gain a good understanding of different aspects of digital analytics, such as effective dashboard visualisation, campaign and conversion tracking, by leveraging the Google Analytics Platform. Students will acquire techniques in text analytics and advanced analytics and make use of available AI services for developing feasible analytics solution for real-world problems.

This course explores blockchains and smart contracts in the context of financial services. The fundamentals of blockchains and smart contracts are first explained and then the similarities and differences of public and private blockchains are shown. Various blockchain platforms are considered as well as the end-to-end implementation of a range of services, for example supply chain financing. The course has hands-on development, deployment and execution of smart contracts using Solidity for Ethereum and the FISCO BCOS blockchain platform. Emphasis is placed throughout the course on analysing real-world situations using case studies and gaining hands-on experience with financial systems.

Financial analytics helps to identify insights from vast amounts of data that financial institutions have to help them to answer specific business questions and to forecast possible future scenarios. Financial firms are investing significant sums in data analytics to facilitate market trading activities and to improve business processes that range from customer acquisition to fraud detection. With large amounts of data available and great potential value for providing competitive edge to businesses, the demand for analytics professionals is also increasing. Analytics professionals in the banks require a broad array of skills such as data management, analysis, statistics, and an understanding of the financial services domain.The module helps students understand how analytics techniques are applied in financial institutions. Through hands-on application of analytics techniques in financial services contexts, together with class discussion and labs, students will learn how business objectives and constraints can be supported by data analytics.

Successful companies realised the power of data driven decision making a few decades back when analytics became a lever to succeed. Over the years the landscape evolved and became much more complex, with large volumes of complex data streaming in and being stored, waiting to be analysed. In order to analyse this new era data, companies need newer technologies and algorithms in order to extract the insights needed to make business impact. This is where Machine Learning comes in handy. Machine Leaning helps to solve business challenges with the help of data. Today's business challenges start with large volumes of complex data. Effective decision-making requires state-of-the-art techniques for predictive modeling. In this course, you learn about the three main requirements for moving rapidly from data to decisions: 1) state-of-the-art techniques for predictive modeling: machine learning; 2) powerful and easy-to-use software that can help you wrangle your data into shape and quickly create many accurate predictive models: SAS Viya and related tools; 3) and an integrated process to manage your analytical models for optimal performance throughout their lifespan.

This course provides students with an overview of the many concepts, techniques and algorithms in data analytics and machine learning. Students will acquire knowledge on classification and regression models such as support vector machines and linear regression e.t.c. The emphasis in this course will be on the application side of data analytics which includes not only creating the predictive model but also deploying and visualizing the output from the models. More importantly, this course allows students to apply cross-disciplinary and project management approaches while learning and applying machine learning techniques to help Thai companies to effectively and efficiently apply data analytics and machine learning to improve their competitiveness and business efficiency. It hones students’ problem-solving skills and prepares them for the complex regional business environment today. Thailand, as part of ASEAN, is rich in natural resources eg. gems and precious metals and is considered to be one of the vital exporters of resources globally. Other than the 2 traditional sectors of agriculture and tourism, it is also a prime manufacturing hub for investors in vehicles, electronics and medical equipment. In recent years, Covid-19 has become a key accelerator for digital transformation globally and the Thai government has also been relentlessly encouraging its businesses to adopt digital technologies and the use of data analytics. This course is not biddable. Students will be shortlisted for interviews and selected students will be enrolled via offline enrolment (e$20 will be deducted).

We interact with a variety of services and systems in our daily lives. While manual labors still take some part in those systems, some other parts become more and more automated by artificial intelligence (AI). In general, we might expect that those systems treat users fairly. If the system uses AI that is built on big data and complex algorithms, such expectation would be strengthened. Compared to human labor that might involve subjective decision-making, algorithmic systems are expected to objectively work and treat users fairly. However, in recent years, there are raising concerns about the potential harms of those systems, which are rooted in biases embedded in socio-technical systems. The inherent opaque nature of AI systems makes the problem worse. For example, YouTube recommends next videos when a video is finished playing. Those recommendations, on the one hand, are helpful to find interesting videos from a tremendous number of YouTube videos, but on the other hand, it is often unclear how or why the video is recommended. What happens if some biases exist in the recommendation algorithm, such as favoring videos with a specific (political) view? No matter whether those biases are intentional or unintentional, users would be exposed to a certain set of videos and are likely to be influenced by them. YouTube is only one out of many examples because AI systems are becoming pervasive these days. In various areas, including healthcare, hiring, financial service, ads, policymaking, and internet services, AI systems are actively used. Thus, it is crucial to ensure that those systems are working fairly without any potential biases. It might be overlooked that the biases are embedded not only in the AI systems but also in established processes or human operators within the systems. The goal of this course is to provide students with an extensive understanding of diverse concepts of fairness and bias in socio-technical systems through examples across diverse domains, from healthcare to internet search. Then, students will learn how to audit practical systems in terms of fairness and bias through recent case studies. The course also aims to understand public concerns related to AI systems and help students to deeply think about ethical AI within multiple social contexts.

Cloud computing gain fast development within the past 15 years and continue penetrating to many aspects of our daily lives. It provides strong and reliable support for all kinds of information systems in various industries. It also greatly reduces the go-to-market time for high-tech start-up companies. Although many people are using cloud in their daily work, but few of them have structural understanding on how cloud functions. With more and more fancy features incorporated in Cloud, it seems difficult for beginners to understand their relations and apply them to real world problems. This course provides an introduction to the fundamental concept and basic structure of cloud computing. The topics covered include basic cloud concept, typical business and technical requirements on cloud infrastructure, basic building blocks for an cloud solution, and how to manage reliability and incident using cloud tools. All topics are designed based on actual commercial usage pattern to support real business requirements. Computing theory and mathematical analysis are included at a minimally needed level. Exercising and integration of knowledge learnt will be covered through assignments, labs, and a demonstrative project for real world problems. After this course, students are expected to gain a structural concept of how cloud operates and how to manipulate cloud resources to support information system and business needs. The principles and techniques learnt in this course will help students understand the common core structure of various commercial cloud platforms and how to go deeper to a specific function. With this foundation, students can easily build up their knowledge of a chosen commercial cloud on a systematic way using online resources.

Data Architecture is a vast area of expertise covering multiple types of data usage. Data Architecture applies to Operational use cases, Decision making processes as well as Analytical use cases. With the advent of Data Warehouses, Data Marts and Data Lakes, Data Architecture has become an important and imminent process in the Data Analytics and Business Intelligence world. The success of a Data Analytics and Business Intelligence initiative depends heavily on the Data Architecture put in place. At the same time Data Architecture can also be ambiguous, subjective and open to interpretations, depending on circumstances. This course will provide a “Guided path to Data Architecture, in the field of BIG DATA”. That means, this course will address the architectural needs of Data Analytics and Business Intelligence. It will provide a prescriptive, unambiguous and objective methodology to arrive at the Data Architecture for any business/entity that wishes to perform Data Analytics or indulge in Business Intelligence. Students are expected to learn how to methodically, step by step derive Data Architecture. This will be a practical course with heavy emphasis on project work involving hands-on architectural work. The course will involve a lot of group discussions, scenario analysis and mini-workshops.

Machine Learning is one of the fundamental subjects in the field of Artificial Intelligence. Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., learning to recognize images or speech, classifying text documents, detecting credit card frauds, or driving autonomous vehicles). This course covers both fundamental theory, practical algorithms and the applications for machine learning from a variety of perspectives. It includes a range of topics, from supervised learning (such as Naïve Bayes Classifier, Linear Regression, Logistic Regression, and Neural Networks) and their applications, to unsupervised learning (such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)) and their applications, and from traditional (shallow) learning (such as Support Vector Machine (SVM)) to recent state-of-the-art deep learning methods (such as Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN)). The course is intended to prepare students for basic understanding of machine learning fundamentals and equip students with the capability to apply machine learning techniques through real world business applications (to solve real world problems). NOTE: This is an algorithm and technical course, and it is highly recommended that students are proficient in programming, probabilities, statistics, linear algebra and calculus. Solid math background will be very useful and helpful for your learning journey. It is highly recommended that students have taken IS424 Data Mining and Business Analytics first if you do not have such math background knowledge. Having taken IS424 first will make this difficult course, Machine Learning & Applications, easier.

Building deployable AI systems for high-stakes domains—healthcare, finance, public services—requires engineers to navigate institutional, regulatory, and human constraints from the start. This course teaches AI Governance as a technical discipline, focusing on the engineering, evaluation, and operational requirements that make AI systems safe, auditable, and trustable at scale.Students learn how policies and standards—such as the EU AI Act, ISO/IEC 42001, NIST AI RMF, and Singapore's Model AI Governance Framework—translate into concrete system requirements, architectural choices, testing protocols, documentation artefacts, and monitoring workflows.Grounded in real-world constraints from multilateral governance and public-sector deployments, the course covers:* Governance-as-Engineering: Developing practical artefacts such as risk registers, model /system cards, and evaluation and monitoring plans.Infrastructure-Scale Data Governance: Architecting for secure data access, privacy-enhancing technologies (PETs), and compliance in multi-stakeholder environments—as exemplified by Digital Public Infrastructure (DPI).* Behavioural Trust Design: Engineering for “felt governance”: designing explanations, recourse pathways, and user-facing signals that shape perceived safety, fairness, and control.* Cross-Functional Translation: Bridging engineering, product, operations, legal, and compliance requirements into coherent system design.The course provides students with exposure to practical methods used in AI governance and offers opportunities to create selected governance artefacts for learning purposes. These outputs are intended to support understanding of industry practices, without implying professional qualification or job preparation.

Technology continues to transform businesses around the world in the 21st century. Digital platforms are revolutionizing the way businesses communicate with their customers and offer products and services. In recent years, there has been a rising interest in XR (extended reality – including virtual, augmented, and mixed reality) technology and applications in consumer, commercial, and industrial markets. In the coming years, advanced 5G network technology and edge computing will enable the rapid progress of XR towards mainstream adoption in many areas of our work and life. With many new developments happening in XR technology, now is the perfect time for businesses to consider incorporating XR with their enterprise solutions.The objective of this course is to cultivate a broad and comprehensive understanding of XR and prepare students for participating in the ideation, development, and integration of highly integrative and immersive applications in businesses. In particular, through the SMU-X course project, students will learn how to develop a Virtual Reality application for e-commerce businesses. The course curriculum covers a wide range of business cases (from Industrial, Manufacturing, Medical, R&D, Entertainment, Educatio, etc.), literature, and practice including: Computer Science Human-Computer Interaction (HCI) concepts, evolution of visual displays for VR, motion tracking, interactive 3D graphics, multi-modal sensory integration, user interfaces, experience design, psychology of VR, and challenges of VR as a communication and collaboration medium. This course will include lectures, readings, case discussions, guest speakers, hands-on labs, and exploration of VR platforms. The course project will require students to render e-commerce business scenarios in a VR environment.

The efficient management of our shared resources and the way we dispose of waste and pollutants are crucial to achieving responsible consumption and production. Encouraging industries, businesses and consumers to recycle and reduce waste is necessary, as is supporting consumers to move towards a more sustainable pattern of consumption. This forms the basis of SDG12: Responsible consumption and production, which includes the following targets: 1. Substantially reducing waste generation through prevention, reduction, recycling and reuse 2. Reduce food waste along the supply chains, retail and consumer levels 3. Ensure that people have the relevant information and awareness for sustainable development. Digital Technologies for Environmental Sustainability (in the Singapore context) is a hands on module which allows students to employ problem solving and prototyping skills using digital technologies to address the above targets. In addition to case studies of how the issue of Responsible consumption and production is tackled by various Singapore organizations, communities and businesses, they will also be exposed to design thinking, hardware and software prototyping, prototyping tools and technologes (IoT, Microcontrollers, App development, Artificial Intelligence, AR/VR, Metaverse) and will gain first hand experience in using these tools to prototype a solution to address a challenge statement around environmental sustainability.\\

This course is structured as a guided-by-instructor course with content provided by Google through the Google Career Certificates (hosted on online learning platform, Coursera) together with guidance and assessments by SMU. It incorporates industry or career certification in the undergraduate study to equip students with professional and technical skills. Data Analytics and Technology Guided External Course is part of the SMU-KPMG Analytics and Cloud Technology Work Study Program in partnership with Google. Students need to clear this course and its corresponding Google Career Certificate before embarking on a six-month work-study with KPMG. There are two certificates: 1) Google Data Analytics Professional Certificate aims to give students the skillsets on collecting, transforming, organizing, and visualizing data in order to draw conclusions and drive informed decision making; 2) Google IT Automation with Python Professional Certificate focuses on equipping students to use Python in automating common system administration tasks, troubleshoot, debug complex problems, and apply automation at scale using configuration management and the Cloud. NOTE! This course is only open to students accepted into the SMU-KPMG Analytics and Cloud Technology Work Study Program in partnership with Google and enrollment will be done offline with e$20 deduction.

In a world where digital technologies are not just tools but extensions of our daily lives, their societal impacts are far-reaching yet still emerging. Past slogans like \"move fast and break things\" led tech companies to overlook harms, as evidenced by scandals involving user data misuse and workplace culture issues. There is now broader recognition that responsible innovation requires understanding potential risks and aligning with human values. But how can we ensure that technology advances in a manner that's ethical, sustainable, and beneficial for all? This timely course explores ethics in computing. Through case studies and frameworks, students will develop skills in critical and nuanced thinking about complex issues at the intersection of technology and society. What you will find in this course: - Embracing Complexity: We won't seek single \"right\" answers. Instead, we'll foster a space for exploring multidimensional problems with interconnected tradeoffs, encouraging students to understand, appreciate, and engage with differing viewpoints. - Skill Development: Students will practise not just theory but systematic analysis of real-world impacts, preparing them for the consequential decisions they may face in their future careers. - Structured Exploration: The course is divided into two engaging parts. The first introduces fundamental concepts, while the second dives into contemporary dilemmas, exposing the tensions between responsible development and disruptive innovation. - Respectful Discourse: A commitment to respectful dialogue is central to this course. We recognise the intricate and \"messy\" nature of real-world problems, with ramifications extending far into a dynamic and uncertain technological future. - Distinct Focus on Technology: While courses like \"COR3301 Ethics and Social Responsibility\" provide a broad understanding of ethical conduct across various professions, this course specialises in the intersection of technology and ethics. This course targets the unique challenges of the digital age, equipping tech professionals with the nuanced understanding needed to navigate ethical dilemmas specific to computing, AI, and digital human rights. It's a tailored exploration for those aiming to align technological advancements with responsible decision-making. - Empowering Future Leaders: By attuning students to ethical imperatives and responsible decision-making processes, this course prepares the next generation of tech professionals to navigate and guide technological progress in a manner that honours human values and serves the greater good.

Low Code Application Platforms (LCAP) are trending in industry. Government agencies in Singapore now require LCAP in their software outsource requirements, including for public facing applications. Both major Telco’s in Singapore are using LCAP. Banks are ramping up their adoption of LCAP. This course exposes students to LCAP using OutSystems, a leading LCAP provider. The course starts out covering architecture and design best practices, followed by weekly hands-on lab exercises covering data modeling, processing logic, API development, and user interface development. Student teams will develop a complete application for their term project. By the end of the course, students will be able to develop commercial-grade full-stack applications without writing any code.

This course will equip students with core knowledge of appreciating what it takes to plan, design, build and sustain digital (‘smart’) cities that are innovative, inclusive, safe, resilient and sustainable. By the end of this course, students will be able to appreciate the following 4 areas: Urban Challenges, Urban Sustainability, Design of ‘Smart’ Digital Cities and Commercialisation of selected digital city projects. There will be an introduction to how software can be used to provide solutions for smart city challenges.

This course on Generative AI is designed for students who want a deep yet accessible understanding of how large language models (LLMs) work and how they are applied, without requiring a mathematical background. No prior knowledge of linear algebra, calculus, or machine learning is needed. The first part of the course focuses on the foundations of LLMs—covering embeddings, transformer architectures, attention mechanisms, training and inference dynamics, and model behaviour—to give students a clear and strong conceptual grasp of the technology powering LLMs. The rest of the course covers practical applications, including retrieval-augmented generation (RAG), prompt design, and agentic workflows that enable dynamic, multi-step problem solving. With a mix of theory, hands-on labs, and projects, students will leave the course equipped to critically evaluate and effectively use and develop Gen AI-based systems in both technical and strategic contexts.

This module aims to introduce students to academic research in Information Systems and/or Computer Science. It allows students to experience first hand the challenges and exhilaration of research, discovery and innovation, and enriches their academic experience by working at/near the frontiers of research in IS technology or management. Each student will work on an independent research project under a mentoring supervision of a faculty. The supervisor will guide the student through one or more phases of research, such as problem/hypothesis formulation, literature survey, case study, solution design and implementation, experimentation and validation, technical writing & presentation. This model enables students to interact and foster closer ties with SIS faculty and their research groups. Objectives Upon completion of the module, students will: • Have participated in an academic research project in Information Systems. • Have acquired special communication and presentation skills. • Have acquired an appreciation for the intellectual process of inquiry and creative thinking. • Have acquired background knowledge and experience in the process of research. • Have surveyed and analyzed relevant literature in the research area.

This module aims to introduce students to academic research in Information Systems. It allows students to experience first hand the challenges and exhilaration of research, discovery and innovation, and enriches their academic experience by working at/near the frontiers of research in IS technology or management. Each student will work on an independent research project under a mentoring supervision of a faculty. The supervisor will guide the student through one or more phases of research, such as problem/hypothesis formulation, literature survey, case study, solution design and implementation, experimentation and validation, technical writing & presentation. This model enables students to interact and foster closer ties with SIS faculty and their research groups. The module qualifies as an IS Technology or Management elective, as determined by the supervisor in consultation with the course coordinator. Students opting to continue with the SIS MSc (Information Systems) Fast Track Program may use this research project as groundwork for the Master’s thesis. Students who perform excellently (getting at least A-) in this course may apply to extend their research as an IS480 project. This is subject to approval by the course coordinator.

IS472 aims to train students to conduct scientific research in a specific area and prepare students for postgraduate study. The students will be guided to formulate research problems, propose research methods, develop experiment design for research validation and write scientific publication (in the form of a conference proceeding paper or journal article). Eligibility to take this course: - students who are undertaking the UResearch in Computing Programme are eligible to take this course. - students who have/currently are taking a PhD course.

The application project enables the SMU Information Systems Management student to • Develop the additional depth and experience required to become a Business IT professional. • Use the project to realize their own version of our “3 Pillars Strategy” by concentrating on how to apply IT solutions to problems within a particular industry sector concentration. • Use the project to build up competence in one of the Information Technology & System Areas. Please refer to http://www.sis.smu.edu.sg/programme/overview.asp for description of the “3 Pillars Strategy”. Objectives Upon completion of the course, students will be able to: • Showcase expertise in executing a project using the knowledge acquired from the courses taken from the BSc(ISM) curriculum. • Experience developing some technology deliverables required in all IS480 projects. • Experience working in a team environment with a sponsored project (if sponsored) using project management skills experience throughout the courses taken in BSc(ISM). • Learn about an industry or technology not otherwise available in the course curriculum. • Work on a complex project.

This course will provide opportunity for BSc(IS) and BSc (C&L) student to: - Develop an IT system or proof of concept application that satisfies a list of functional and quality requirements. Student will work with a team and practice their requirements gatherings, analysis, design, implementation, testing, deployment and project management skills. - Build the additional depth required to become a Business IT professional. This includes building up competence in the student's selected track, applying their learning domain knowledge and technology to the relevant track domain industry sector. Upon completion of the course, student will be able to: - Showcase expertise in executing a project using knowledge acquired from the courses taken from the IS curriculum - Experience developing of some technology deliverable for an IT system or proof of concept - Experience working in a team environment with a sponsored project (internal, external or self-proposed) using project management skills experience throughout the courses taken in IS - Learn about an industry or technology that is related to his selected track not otherwise available in the course curriculum. - Work on complex and real project used by the project sponsor https://wiki.smu.edu.sg/ISProjectExperience/ DO NOT BID unless your team has been approved. (Biddable at BOSS 2 or 2A)

Note: This course counts as a direct replacement for the IS483 Project Experience course. This is an SMU-X course designed in collaboration with Citibank, and has since been extended to include other banking/fintech sponsors such as OCBC, UBS, and NETS. Sponsors will each supply 3-5 projects ideas to select from. Students will form teams of 5 or 6, and select one of the sponsors’ project ideas to work on. Project selections do not need to be unique, meaning multiple teams can select the same project idea. Each student project team will be assigned to a sponsor and an SMU faculty supervisor. Sponsors will provide project scope and management for student teams to have practical industry learning experiences. Student teams will have weekly check in meetings, either virtually or physically, with their sponsor. Student project teams will be expected to develop a working software application prototype, to be delivered to the sponsor at the end of the course. Sponsors will specify the technologies to be used, including; development tools/languages, OS, database, 3rd party libraries, target deployment environment e.g. cloud environment.

IS485 aims to introduce students to academic and industry research in Information systems. it allows students to experience first hand, the challenges of research, discovery and innovation, and allows students to work near the frontiers of research in the field. - IS485 Research projects can be either done as an industry research project (SMU-X) or an academic research project (non SMU-X). - Students who do the IS485 (Academic Research Project) will have to source their own faculty supervisor - Students who do the IS485 (Industry Research Project) will have to source both an industry mentor and a faculty supervisor to mentor the team For students who take up the IS485 project and perform an industry research project, this module will be considered an SMU-X module. https://wiki.smu.edu.sg/ISProjectExperience/ Please do not bid for this course if you have not been granted prior approval.

The Google Analytics Work-Study Elective is a six-month internship attachment program under the SMU-Google Squared Data & Analytics Programme. During the attachment period, students work five days a week at an assigned Google Partner company. While undertaking the work-study elective, students may also enroll in 1-2 CUs of courses at SMU during the same term and are allowed to be back in campus up to one day each week. Students will be assigned to a SMU Faculty Advisor and an Internship Supervisor (from the Google Partner company). This elective provides students with an opportunity to do an extended internship with a Google Partner company and applies the digital analytics skillsets that they learnt to contribute to the data and analytics sector in Singapore.

The Analytics and Cloud Technology Work-Study Elective is a six-month internship attachment program under the Google-SMU-KPMG Analytics and Cloud Technology Work-Study Program. During the vacation period, students work five days a week at KPMG. While undertaking the work-study elective, students may also enrol in 1-2 CUs of courses at SMU during the same term and are allowed to be back in campus up to one day each week. Students will be assigned to a SMU Faculty Advisor and an Internship Supervisor (from KPMG). This elective provides students with an opportunity to do an extended internship with KPMG and applies the data analytics or IT automation skillsets that they learnt to contribute to the analytics or cloud technology sector respectively in Singapore. NOTE! - This course is only open to students accepted into the SMU-KPMG Analytics and Cloud Technology Work Study Program in partnership with Google and enrollment will be done offline with e$20 deduction. - For students from 2019 intake and later, please ensure that you have submitted the Internship in OnTrac so that you will be granted the 1 CU Internship requirement after you have successfully completed the WSE internship. For assistance, you may contact your Career Coach from DKHMCC. - This is a Pass/Fail course and if F grade is awarded, it will be counted towards your GPA.

The Cloud Solution Development Work-Study Elective is a six-month internship attachment program under the SMU-AWS-Accenture Cloud Solution Development Work-Study Program. During the vacation period, students work five days a week at Accenture. While undertaking the work-study elective, students may also enrol in 1-2 CUs of courses at SMU during the same term and are allowed to be back in campus up to one day each week. Students will be assigned to a SMU Faculty Advisor and an Internship Supervisor (from Accenture). This elective provides students with an opportunity to do an extended internship with Accenture and applies the solution development skillsets that they learnt to contribute to the industry sector in Singapore.

This course provides Information Systems (IS) students with expanded opportunities for work-study experiences that integrate theory and practice, allowing them to pursue self-sourced work-study opportunities and earn academic credit. This approach allows students to develop industry-relevant competencies and contributes to the development of industry-ready graduates who are adaptable, flexible, and innovative.This course comprises of a minimum 20-week work-study internship program. Students will develop and hone specific relevant skillsets through on-the-job training alongside classroom learnings. Students are expected to work full time before the term starts and alternate between working at least four days at a company and studying on campus for up to one day for up to 2 courses each week during term time.

This is a hybrid course that aims to equip students with foundational knowledge on operating systems and computer networking. Operating Systems: knowledge of the mainstream operating systems, Microsoft Windows and Linux, security and access control strategies, shell commands and scripting, as well as enterprise-level features of various OSes. Students should be able to demonstrate ability to compare and contrast these two OSes. Students should also be comfortable with using the command line interface, including skills to troubleshoot issues pertaining to environment variables and misbehaving applications. Students should also get experience with installing Windows and Linux from scratch either on a real laptop/PC or a virtual machine. Networking: foundational networking concepts useful for programming, including TCP/IP concepts, ports, routing concepts, basic network-related security issues. Students are expected to work on real switches and routers as well as set up a simple LAN, configure the network settings for Windows, Linux, and optionally MacOS machines, to join this network, and perform basic network-related troubleshooting. Students should also be able to appreciate how the Internet works, including knowledge on DNS, gateways, the client/server architecture of Web servers and how a packet is routed from origin to destination. Students should also be able to write simple high-level programs in Python that communicate across the network. This course is specially designed to be industry-focused with hands-on practice.

This course on Generative AI will provide software engineering students with a deep yet accessible understanding of how large language models (LLMs) work, their applications, and how to use them as a tool to design and deploy software efficiently. No prior knowledge of linear algebra, calculus, or machine learning is required.The first part of the course focuses on the foundations of LLMs, covering embeddings, transformer architectures, attention mechanisms, training and inference dynamics, and model behaviour. We will discuss retrieval-augmented generation (RAG) and advanced LLM architectures, as well as the associated use cases that students are likely to encounter in practice.The second part of the course focuses on the responsible use of LLMs as a tool for software engineering. We will investigate the use of AI in eliciting user requirements, generating design documents, and code generation. As part of the DevSecOps pipeline, we will explore how AI can automate documentation tasks, code reviews, unit and integration testing, security auditing, risk assessment, and threat monitoring. Finally, we will investigate autonomous coding agents for code maintenance and optimization. Case studies, practical exercises, and an emphasis on responsible and ethical use of AI accompany these topics.Throughout the course, students will learn the practical impact of cluster and GPU architectures on the design of efficient systems. Students will explore both cloud and self-managed environments, containerization, and modern development tools and frameworks like Triton, TensorFlow, and PyTorch, along with APIs for pre-trained models from various sources.With a mix of theory, hands-on labs, and projects, students will leave the course equipped to critically evaluate and effectively use Gen AI as both a tool and a product of software engineering.

Advanced Programming and Design is an intensive course aimed at equipping students with the skills and knowledge required to become proficient and efficient software developers. Building on a solid foundation in object-oriented programming and Java, students will explore advanced language features not covered in earlier courses, including generics, memory management, and JVM performance tuning. The course introduces functional programming paradigms, with a focus on Streams, functional interfaces, and lambda expressions. Principles of multi-threaded programming will be examined to prepare students for concurrent application development. Emphasis is placed on professional software engineering practices, including clean code, continuous refactoring, test-driven development, object-oriented and architectural design patterns, and adherence to SOLID principles. Throughout the course, students will engage in extensive hands-on programming exercises to reinforce theoretical concepts and cultivate best practices in software design and development.

The Software Engineering (SE) Apprenticeship is a mandatory 52-week work attachment programme. During the apprenticeship, students will be working full-time at the sponsoring company in a suitable software-engineering role. Students will have the opportunity to apply skills they learn from the SE Degree programme. The focus of the apprenticeship is to provide on-job-training for students, as well as exposure to working culture and professional practices. The apprenticeship will be closely monitored by the school and formally assessed by SCIS’s practicum manager and the sponsoring companies’ mentor(s).

Effective planning and management of smart cities require urban planners to integrate and share data from multiple sources to the urban users so that they can be active participants in the smart city planning process. GIS with its capability to capture, manage, display, and analyse information spatially is emerging as one of the important enabling tool in smart city planning. This course provides students with an introduction to practical applications of GIS in smart city management and planning. Emphasis will be placed on - locating, acquiring and integrating multi-sources of data into GIS, - understanding the principles and methodologies of geocoding and geo referencing, - become familiar with geovisualisation and GIS analysis techniques, and - exploring the technologies and possibilities of GIS-enabled Planning Support Systems for smart city management.

This course provides technology-savvy students, preferably at senior undergraduate level, with a set of strategy-innovation-entrepreneurship concepts from social science and management (including fundamentals of corporate strategy, social context, design thinking, blue ocean strategy) to support idea generation for Social Technopreneurship: application of technology to serve social outcomes and in an entrepreneurial manner (proposed startup can be tech, non tech business, social enterprises or even non-profit enterprise). This course has an Asian focus for learning of best practices from Asia (with emphasis on Indonesia, Vietnam and Thailand) on how they applied technology towards social outcomes yet practically relevant to apply and adapt overseas practices to Singapore’s context in social technopreneurship. Student will be empowered to work on their choice problem statements in a coached environment. The final pitch will be in Singapore and could be floated to relevant organisations for possible funding after the course.

This course is for SMT students to learn the programming fundamentals in the context of Smart Cities. The course introduces students to fundamental programming concepts and constructs, explains the process of developing a basic software application, and explains the basic concepts of object orientation. The student will experience the implementation of a basic software application. Python, a widely-used, high-level, general-purpose and interactive programming language, is used as the vehicle of exploration in this course.

The Internet of Things (IoT) brings us a vision of a world in which trillions of devices can sense, communicate, and collaborate over the Internet, in the same way that humans interact and collaborate with one another over the World Wide Web. In this course, we explore the infinite possibilities of using IoT to build Smart Cities Applications. We begin our adventure by exploring the three key components of IoT - things, connectivity, sense-making. What sensing modalities that we can equip \"things\" with? How do we connect these \"things\" to the Internet, and to each other? What data-driven, actionable insights could we provide to bring incredible benefits to the dwellers of smart cities? Drawing inspiration from case studies such as SmartBFA , we reflect deeply and broadly on the broader challenges facing Smart Cities, in particular exploring the various ways in which we could create social impact by building inclusive Smart Cities.

This course explores how analytics and applied Artificial Intelligence (AI) can transform smart buildings into sustainable, efficient, and adaptive environments. Students will learn how to apply machine learning, digital twins, and IoT data to optimise energy use, reduce emissions, and improve occupant well-being. Through real-world case studies, hands-on labs, and a project, students will develop the skills needed for the green buildings of the future.

We use mobile devices any time to access the internet, read the news, watch videos, search for nearby restaurants, chat with friends, and leave posts on social networking sites. These online interactions leave massive digital footprints which enable us to understand, and ultimately influence human behavior and social dynamics: what and why we like, hate, believe, behave, and engage. Computational Social Science is an exciting and emerging field that sits at the intersection of computer science, statistics, and social science. This course provides a hands-on introduction to the ideas and methods of Computational Social Science. We will discuss questions and problems across various domains of social science including politics, economics, and health and will learn how new online data sources and computational methods are being used to tackle those problems. Through exploring computational social science methods and their use in social sciences today, this course helps students to engage with questions on research design. Also, students will have the opportunity to try their hand at analyzing big data from various sources such as #covid-19 Tweets, Data.gov.sg, etc.

This course provides technology-savvy students, preferably at senior undergraduate level, with a set of strategy-innovation-entrepreneurship concepts from social science and management (including fundamentals of corporate strategy, social context, design thinking, blue ocean strategy) to support idea generation for Social Technopreneurship: application of technology to serve social outcomes and in an entrepreneurial manner. SMU-X office will provide up to 2 sponsors for this course (tech, non tech business, social enterprises or even non-profit enterprise) of which students will function as ‘consultants’ to understand the problem, collect relevant primary data (through surveys, interviews or tech means), and propose solutions to the sponsors. This course has an Asian focus for learning of best practices from Asia (with emphasis on Indonesia, Vietnam and Thailand) on how they applied technology towards social outcomes yet practically relevant to apply and to adapt these overseas practices to Singapore’s context in social technopreneurship and to the sponsors’ problems. Student will be empowered and coached to apply relevant strategy-innovation tools on a real problem. The final pitch will be in Singapore to SMU-X office arranged sponsors.

This course delves into the quantitative methods of operations research, with a strong focus on urban operations, efficiency, and sustainability. It explores how these techniques can enhance efficiency and sustainability in smart city domains such as transportation, logistics, delivery, e-commerce, and healthcare systems. The curriculum emphasizes the planning, analysis, and eco-friendly operations of urban services, integrating a comprehensive study of probability models and optimization techniques. It aims to equip students with the skills to implement practical, diverse, and sustainable solutions in the context of smart city infrastructure.

This course will enable Year 3 or 4 SMT students to apply what they have learnt at SIS to (i) design and implement an application as a team comprising 4 to 6 students (Application); (ii) conduct academic research individually (Research) or (iii) conduct evidence-based analysis of smart city blueprints or data-driven analysis as a team comprising 2 to 3 students (Analysis) to address a real-world urbanization or societal challenge towards realizing smart cities. Students who have confirmed their enrolment with the Course Coordinator should bid for SMT483 section(s) of the respective supervisor (where applicable) during BOSS 2A and onwards. e$10 bid amount will suffice. If students do not secure their bid by BOSS 2A Win 3, they can request to be enrolled offline and e$20 will be deducted.

This course examines the role of Enterprise Architecture in implementing a bank’s digital business strategy while minimizing the overall technology cost for the bank. In today’s market where the speed of doing business is rapidly increasing, and customers are becoming increasingly more sophisticated, banks are challenged to provide faster and better digital services, anytime, anywhere. Technology, as a business enabler, has become a key consideration of any bank’s strategy. The adoption of enterprise platforms such as Service-Oriented Architecture (SOA), Business Process Management (BPM), Business Rules Management System (BRMS), Master Data Management (MDM), and Enterprise Data Warehouse (EDW) will improve a bank’s competitive advantage with measurable results; increased revenue, speed to market, product & service innovation, improved agility, and reduced cost. Emphasis is placed throughout this course on analysing real-world situations using case studies, in particular large-scale change scenarios such as; core banking system replacements, and bank mergers whereby multiple vendor products need to coexist. Hands-on lab exercises and project assignments will include the assembly of prototype banking solutions which invoke the API of SMU Teaching Bank (SMU tBank).

The term “Analytics” has been around in the business settings for a while now, where past results have been used to guide and improve future performance of business. More recently, enhancements in technology have enabled the business world to produce and store very large amounts of data which needs to be processed, managed and analysed in order to uncover its hidden value. There is a real dearth of analytical talent needed to perform this task. This course aims to introduce students to the fundamental skills needed to get started with analytics. This course will help them build a foundation needed for advanced analytics by introducing them to data exploration techniques, data preparation methodologies, applying key analytics techniques and use them in formulating a business problem and identifying the correct analytical approach to solve it.

This course focuses on fundamental concepts of developing programs using an object orientated approach. There will be an emphasis on writing clean and efficient code, and the ability to use an appropriate data structure or algorithm to solve problems. The Java programming language will be taught in depth. Students are expected to have a strong foundation in programming. This is a compulsory course for IS (Software Development Track) for 2017 intake and onwards. Upon completion of the course, students will be able to: 1. Practice problem solving skills 2. Read UML sequence and class diagrams 3. Apply basic concepts of Object Orientation to a given scenario/context 4. Apply good programming practices and design concepts to develop software 5. Appreciate the role of algorithms and in problem solving

This course prepares students to design, deploy, and manage the interconnection of networking devices, including cyber-physical systems. It covers fundamental computer communication concepts, including switching, signalling, encoding and transmission, modern network technology, protocols (TCP, UDP, IP), and wireless (cellular and wireless LAN). Besides helping students to understand the key technologies conceptually, the course is structured to develop students’ skills in building, analyzing, and evaluation simple communication networks.