The financial services industry (FSI) has been undergoing transformations, especially in the last decade. Drivers for these changes include competition, stringent regulations and digitisation. FSI comprises many types of financial players including banks, hedge funds and Stock Exchanges. Within banks, we have many sub types ranging from consumer, retail to investment banks. This course will focus on the banks, as they generate significant jobs and are major contributors to a country’s GDP.

Banks offer digital banking business products, processes and services to institutional and individual customers to enable them to transact for their business or personal needs. They include savings, loans, financial transactions, international trade activities; and management of financial risk with options and derivatives for hedging. Customer assets held in bank accounts and the transactions involving these accounts require total and continuous security and protection.

This course is structured based on two inter-related modules that are built up sequentially:

1. Banking Foundation – The Essential Concepts
2. Digital Trends and Applications to Banking in Digital Transformation

Upon completion of this course, you will be able to:

• Describe and apply the foundational elements of the banking industry including the types of banks, products and services, delivery channels, risks and compliance
• Analyse banks using the Unified Banking Process Framework (UBPF)
• Apply digitisation to banking by differentiating how the different technologies are used by the banks
• Describe and analyse FINTECH trends in banking digitalisation and transformation

The financial services industry world-wide is facing more challenges than ever due to an increasingly competitive environment, with new challenger businesses re-writing whole sectors of the industry, and the increased regulatory scrutiny from both central banks and international bodies. To assist its players, the financial services industry is collecting ever-increasing amounts of data from their internal processes, customers and services, and applying state-of-the-art artificial intelligence algorithms to find value and service automation.

The knowledge and understanding that are needed for an artificial intelligence and data analytics professional in financial services includes, but is not limited to, data management, analysis, mathematics and statistics, machine learning and deep learning as well as an intimate knowledge of the specific financial services domain, including the regulations and compliance factors surrounding it.

This module aims to bring these skills and knowledge together to bridge the gap between artificial intelligence techniques and their applications in financial services. Using state-of-the-art artificial intelligence algorithms, coupled with class discussion, labs and guest speakers from the industry, you will be able to understand how domain knowledge (such as compliance and regulation) interacts with artificial intelligence solutions and value chains through a range of industry cases.

This module is also designed to take advantage of the diversity in students’ backgrounds to give varied points of view during each lab project and discussion. This closely emulates many financial services artificial intelligence environments.
Prerequisites are set to ensure that you have the required level of knowledge and skills.

Upon completion of this course, you will be able to:

• Understand the process to undertake when given an artificial intelligence and analytics project in financial services
• Understand and evaluate the range of challenges that artificial intelligence could be applied to
• Bridge the gap between artificial intelligence and domain knowledge in financial services during implementation of solutions
• Understand and value the process from collection of data to model validation and explainability, and how domain knowledge interacts with each of these stages
• Understand and apply state-of-the-art artificial intelligence techniques, including deep learning and natural language processing
• Be equipped with the necessary skills to perform well as an artificial intelligence professional in financial services
• Understand the legal and ethical implications of artificial intelligence solutions in financial services

You will gain exposure through lectures, labs, group project work and discussions of the various approaches to AI in financial services and will be able to articulate and evaluate potential AI solutions to drive insights and value. You will be exposed through labs, a group project and an individual project to the artificial intelligence process and be able to undertake a process from data collection to model validation and implementation in a financial services context. This will give you a significant edge in your financial services career.

Note: "Python for Data Science” or “Python Programming & Data Analysis" is a prerequisite for this course.

This course explores corporate banking and the use of smart contracts on blockchains with specific use cases shown for corporate banking. It begins by describing corporate customers and the financial services that they need in different corporate business contexts. Specifically, the product areas of corporate lending, cash management and payments, trade finance, and corporate treasury are covered.

Emphasis is placed on analysing real-world situations using case studies and gaining hands-on experience with blockchains as banking systems. Guest speakers from companies using blockchains and blockchain vendors will be invited to share their experiences.

The course comprises lectures, hands-on lab sessions and assignments:

• The lectures are on corporate banking, blockchains (also called DLTs) and smart contracts.
• The labs consist of a series of exercises to install, administrate and develop a blockchain application for a corporate banking business process.
• The assignments will explore uses of blockchains in corporate banking and then proceed through an implementation of a blockchain application.

Upon completion of this course, you will be able to:

• Understand corporate customers and the financial services that they need
• Become familiar with the basic technologies currently used in the major functional and business areas of corporate banking
• Gain a depth of understanding on corporate banking core products and services such as corporate lending, cash management, interbank payments, trade finance, supply chain financing, money market products and foreign currency exchange
• Explore Smart Contracts use cases in relevant areas of corporate banking
• Understand the future of blockchains and the role that smart contracts could play in future global financial crises

The Retail Banking industry is undergoing a major transformation. Digital technology forms the key back-bone in taking new financial solutions and services to the customers (individuals and SMEs). With rapid assimilation of technology and the pace at which business is done today, having robust and scalable IT solutions has become a necessity. Advance delivery channels, greater automation and finding new avenues to bring down costs have been some of the main stays of the solutions.

This course provides an essential foundation on retail banking products and services. Focus will be placed on the core banking systems, retail products such as Current, Savings, Deposits and Loans, and the processes and technology solutions supporting these products. The course then builds upon the foundation to elaborate on the business and operational needs to support these core banking products and offerings, and also the architecture and design of the IT application systems that support these processes. Other key digital areas include the use of technology for Retail Banking RiskTech, Compliance, Cyber Security and Regtech.

With the rise of FINTECH and Mobile Banking, this course will also guide you to take into account these phenomena. Hence contemporary topics include a FINTECH Lab and a FINTECH Project, aimed to impart the know-how in such developments and FINTECH trends. Digital transformational topics will include industry applications to retail banking such as Open APIs, AI, Blockchain(DLT), Design Thinking and Agile Development.

Digital innovation will be the key focus for the FINTECH project.

Upon completion of the course, you will be able to:

• Identify core banking products and their process flows
• Differentiate core banking services and channels offered to customers
• Develop solutions, architecture supporting core banking products; challenges, criteria in evaluating solutions
• Identify linkages between business value and the processes and systems
• Discern the increasing importance of FINTECH, Mobile Banking, Risk Management, Security and Compliance (RiskTech & RegTech)
• Develop solutions that address management needs in the realm of decision making and regulatory requirements

Financial Institutions are among the most intensive and innovative users of information technology. Floor- 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 has enabled performance of complex data analytics to advance the 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 settlement processing, resulting in shorter settlement cycles.

But amid the loss of colourful 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, you must understand these fundamentals.

This course introduces the roles within these types of markets, products and services, and how associated risks are harnessed and managed. Focus will be placed on 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. You will be placed in multiple simulations, taking on different roles from broker to trader to risk manager in order to gain insights into the practical application of what otherwise remains theory.

Upon completion of this course, you will be able to:

• Describe the linkages between business value and the processes and systems
• Recognise the various types of markets and market participants, and describe their revenue sources
• Differentiate the functions within a financial institution
• Explain the trade life cycle
• Contrast the characteristics of the various financial instruments
• Derive the theoretical prices of futures and options
• Analyse markets using technical analysis
• Create trading strategies and manage their risks
• Manage positions in news-driven markets
• Execute orders correctly within markets
• Discuss the rigours of trading from first-hand experience
• Build and deploy an automated market making price quotation system
• Apply options for hedging and speculation
• Calculate historic value-at-risk and apply it to portfolios
• Derive and apply margin requirements to portfolios
• Recognise market misconduct
• Describe the importance of market surveillance
• Sketch typical financial market system architecture and its core functionality

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 system that market participants can rely on is even more pronounced. A payment system (legal definition) is an arrangement that 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 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, organisations 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.

Upon completion of this course, you will be able to:

• Describe and explain the payment industry, especially the key and critical aspects of the payment infrastructure, the major functions and the roles and responsibilities of key stakeholders/participants
• Present the major payment systems, the payment networks and methods available in the market covering these key areas:
• Singapore’s local market e.g., clearing house, NETS, Fast And Secure Transfers (FAST), Global market e.g., PayPal, VISA, CLS, Standards and messaging format e.g. SWIFT, ISO, CEPAS, (also SEPA)
• Payment-related Innovations (e.g. Open API, telco-based mobile money, blockchain technology, cryptocurrencies, and non-bank FinTech alternative payment providers such as AliPay, Stripe, Square, and TransferWise)
• Identify appropriate sources and provide an update on developments and emerging trends including possible impact of political and economic climate in key jurisdictions, e.g., the payment services directive in the European Union
• Demonstrate awareness of key functions of payment networks and methods such as:
  • Optimised and secured integration links
  • Efficient operational batch processing e.g. awareness of cut-off times
• Articulate the major issues and problems associated with payment systems and Identify payment security threats, vulnerabilities, risks, and necessary controls/mitigation including (but not limited to):
  • Privacy safeguards
  • Resiliency, high availability
• Give examples of the anticipated benefits and other impacts associated with e-payment system implementation
• Provide examples of typical system requirements for e-payment systems
• Differentiate payment system objectives and technological characteristics (e.g., centralised vs distributive architecture, on-line vs off-line processing, wire and wireless communication features)

Fintech is the creative integration of emerging business models and digitalisation that results in advancing financial and social impact. The ultimate goal is to advance societal financial needs effectively, efficiently and safely.

The Fintech industry is one of the fastest-growing sectors with major impact and consequences on the banking industry. In 2018, US$32.6 billion was invested in Fintech (Accenture 2019 Fintech Report). Digitalisation is the key enabler for many of the innovations occurring in the financial services industry.

This course is divided into two main sections: Section 1 will include Fintechs and Innovation and Section 2 will include the concepts and characteristics of start-ups and key practices for successful start-ups.

The course will enable you to understand the fundamentals of Fintech, the nomenclature used in the industry, the ecosystem of Fintechs, the nature of innovation, the drivers for innovation in the financial industry, Fintech trends, the business impact of Fintech, digital banks, the methodologies for start-ups, and incubation best practices that lead to successful start-ups. It is actively supplemented by Fintech industry partners as guest speakers, FINTECH co- founders, visits to innovation centres, etc. so as to broaden the scope from classroom learning to practice-based learning.

Upon completion of this course, you will be able to:

• Analyse the characteristics of Fintechs & start-ups
• Identify Fintech and start-up eco-system stakeholders and their roles
• Differentiate the types of incubators and incubation best practices for successful start-ups
• Apply innovation frameworks and methodologies for start-ups
• Develop fund raising and investment valuation knowledge
• Develop financial innovations that positively impact customers
• Develop effective pitching and communication skills of successful start-ups
• Identify emerging form of Fintechs such as digital banking platforms providers, neo banking challengers and trends

Note: "Digital Banking & Trends” is a prerequisite for this course.

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 accurately. The overall objective of this course is to understand quantum computing, how it differs from classical computing and what the main applications are, now and in the future. Furthermore, you can experience programming real quantum computers and explore the quantum world.

Upon completion of the course, you will be able to:

• Explain the fundamentals of quantum computers
• Recognise the advantages and disadvantages of quantum computers
• Programme quantum computers
• Recommend quantum computers for the correct problem types
• Predict advancements in quantum computing

Note: "Digital Banking & Trends” and "Python for Data Science/Python Programming & Data Analysis" are prerequisites for this course.

Along with sales, risk and regulatory concerns determine the success or failure of financial institutions. When banks misprice risk associated with financial products or take on too much risk, they endanger their overall profitability. Likewise, when legal and regulatory compliance are mismanaged, banks can incur substantial fines, suffer reputational damage and become subject to ongoing regulatory scrutiny. Accordingly, efficient and effective management of risk and regulatory compliance is a core focus of banks. Because of its mathematical nature, risk calculation, extensively leveraged technology for several decades. On the other hand, a long-standing approach that banks have used to deal with gaps in regulatory compliance and increasing regulation has been to "throw more bodies" at the problem. This approach has been costly, inefficient and, in some cases, ineffective. As a result, Regtech solutions have been developed that help banks use technology to address compliance-related challenges.

This course begins by providing an introduction to Risktech, technology that is used to support banks' risk management activities. It reviews the main types of risks that banks encounter: market risk, credit risk and operational risk, and the processes and techniques used to measure those risks. Challenges related to managing risk data and performing risk calculations are reviewed along with related technology approaches. The course then goes on to review the purpose and application of bank regulation and common causes of regulatory compliance failure. With an understanding of relevant regulatory-related problems, different types of Regtech solutions are examined.

Upon completion of the course, you will have an understanding of:

1. The following aspects of risk management:
   • basic concepts related of market, credit, and operational risk
   • the principle behind and ways of calculating value at risk (VaR)
   • technologies that banks use to support risk management activities
2. The following aspects related to Regtech:
   • purpose and concerns of bank regulation
   • challenges banks face related to regulatory compliance
   • ypes of Regtech solutions available and the benefits that they provide

In the digital age, data is considered a very valuable resource and one of the most important assets of any organisation. It forms the basis on which an organisation makes decisions. Consequently, data needs to be accurate, complete, consistent and well organised.

This course focuses on relational databases, one of the most common approaches adopted by the industry to manage structured data. It covers the fundamentals of relational database theory, important data management concepts such as data modelling, database design, implementation and data access, as well as practical data-related issues in current business information systems.

A series of in-class exercises, tests, pop quizzes and a course project will help you understand the covered topics. You are expected to apply knowledge learnt in the classroom to solve problems based on real-life business scenarios while gaining hands-on experience in designing, implementing, and managing database systems.

Upon completion of this course, you will be able to:

• Understand the role of databases in integrating various business functions in an organisation
• Understand data modelling, conceptual, logical and physical database design
• Apply the fundamental techniques of data modelling to a real project
• Query a database using Structured Query Language (SQL)
• Use commercial database tools such as MySQL

This course is about data analytics techniques and data-driven knowledge discovery. It aims to convey the principles, concepts, methods and best practices from both statistics and data mining, with the goal of discovering knowledge and actionable insights from real world data.

In this course, you will be exposed to a collection of data analytics techniques and gain hands-on experiences on using a powerful and industry standard data analytics software. However, you are not required to formulate or devise complex algorithm, nor to be a master of any particular data analytics software. You will focus your attention on the use and value of the techniques and solutions taught to discover new knowledge from data and how to make data-driven decisions in an intelligent and informed way. You will be also trained to understand the statistics rigour and data requirements of these techniques.

Upon completion of this course, you will be able to:

• Discover and communicate business understanding from real-world data using data analytics approaches
• Extract, integrate, clean, transform and prepare analytics datasets
• Perform Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA)
• Calibrate and interpret explanatory models
• Build and evaluate predictive models
• Visualise, analyse and build forecasting models with time-series data
• Perform the above data analysis tasks by using SAS JMP Pro and/or SAS Enterprise Miner

Recent advances of technologies have enabled more seamless ways of generating and collecting a larger volume and variety of data. Applied Statistics is hence the relevant branch of Mathematics that is used to visualise, analyse, interpret and predict outcomes from these data. Descriptive Statistics will equip us with the basic concepts used for describing data while Inferential Statistics allows us to make inferences and deductions about underlying populations from sample data.

This course spans a semester. You will acquire knowledge in applying statistical theory for analysing data as well as the skillsets in statistical computing for developing applications with the R programming language. The first half of each lesson will be dedicated to equipping you with statistical concepts in descriptive and inferential statistics while the second half will focus on the practical aspects of implementing them within the R console. The course aims to progressively prepare you to eventually develop your very own data application in RStudio, an integrated development environment built for the R programming language.

Upon completion of this course, you will be able to:

• Understand concepts in probability theory and its computation
• Apply techniques for describing data
• Apply and evaluate statistical methods for making statistically sound inferences from sample data
• Apply R programming for describing data, making statistical inferences and perform rigorous statistical analysis.
• Analyse, interpret and communicate statistical results
• Create RStudio integrated development environment to develop an interactive and insightful web application in the business context

Many real-world businesses require data analysts, data engineers and data scientists to build applications in programming language Python, together with several off-the-shelf libraries. This course is designed for you if you wish to master Python as a programming language and build data analysis solutions with Python, along with several widely used libraries. This course teaches both the Python programming language itself and how to carry out descriptive and diagnostic data analysis in Python. In the Python programming part, basic topics, including data types, containers and control flow, will first be introduced. As advanced topics in Python programming, lambda expressions, functions, modules and regular expressions will also be explained and elaborated in great details. The second part of this course will teach functions in the three important libraries: NumPy, Pandas and Matplotlib. With these three libraries, you will then be ready to perform descriptive and diagnostic data analysis with data visualisation on sample datasets provided by the course instructors. Upon the completion of the course, you should be able to carry out data analysis with Python and related libraries at a highly proficient level.

Upon the completion of this course, you will be able to:

• Program in the Python programming language
• Analyse data with Python and Python libraries
• Create a set of analysis tasks to be carried out
• Apply functions in Python libraries in data analysis
• Evaluate datasets and business applications from the results of data analysis

Most organisations have tons of data collected over the years and often don’t know how to make the best use of it to increase the profitability and overall health of the company. Out of all the assets that a company has, customers are the most critical as they are the key drivers of the business and are the building blocks of profit.

In this course, we focus on ways of leveraging customer data in order to drive the company’s strategy in all key functional areas including marketing, sales, ops and finance. In order to achieve this objective, we need to understand customers holistically from acquisition to the time they continue to buy the goods and services from the company.

The goals of this course are to:

• Develop a customer-centric business solution by understanding the available customer data and learning to apply advanced analytics techniques including segmentation, prediction and scoring for increasing profitability of the firm
• Develop thorough insights into applications of analytics in various industries and build understanding about the use cases in multiple industries where analytics can make an impact
• Gain deep understanding of the SAS data mining tools for implementing the devised solutions
• Learn to communicate and present complex analytical solutions intuitively and articulately

Upon completion of this course, you will be able to:

• Build data-driven applications that help to drive customer strategy in the organisation
• Build a customer-focused data and analytics framework for developing customer lifetime value
• Understand the basic concepts and principles of customer relationship management such as customer lifetime value, recency-monetary-frequency (RMF) and next best offer (NBO)
• Analyse customer data with appropriate data analytics techniques
• Discover actionable insights from the analysis results for supporting marketing and sales strategies such as customers acquisition, cross-selling, customers retention and recommendations
• Articulate the analysis results for management communication
• Plan, design, deploy and manage a customer analytics project or system

Note: "Data Analytics Lab" is a prerequisite for this course.

Every service sector business is faced with operations-related problems including demand forecasting, inventory management, distribution management, capacity planning, resource allocation, work scheduling and queue & cycle time management.

Very often, the business owner knows that problems exist but has no idea what caused the problems, and, therefore, does not know what to do to solve them. In this course, you will be exposed to the Data and Decision Analytics Framework, which helps the analyst identify the actual cause of business problems by collecting, preparing and exploring data to gain business insights before proposing the objectives and solutions to solve the problems. Such a framework combines identification of the root causes by data analytics and proposing solutions supported by decision analytics.

The goals of this course are for students to (a) develop a strong understanding of the theory, concepts and techniques of operations management and data driven analytics, and (b) apply that understanding in creating cutting-edge business analytics applications and IT solutions for service industry companies to gain operation insights and business improvements. You will apply the Data and Decision Analytics Framework to solve several operations-focused case studies. This framework is an expansion of a typical operations management solution methodology that includes data analytics so as to exploit the linkages across processes, data, operations, analytics and technology to offer businesses alternative solutions to operations problems.

Upon completion of this course, you will be able to:

• Explain the theory and concepts of several operations management areas
• Apply a Decision Analytics framework for solving operations-related problems
• Explain the data and apply the theory and concepts, and Data and Decision Analytics framework into solving operations-related problems
• Relate the key data and processes to operations problems in several business domains
• Acquire knowledge and skills in several data analytics tools including SAS Enterprise Guide, SAS O/R, SAS Simulation Studio, SAS Viya
• Build analytics models to perform data analysis and obtain insights

Big Data sets have become an enabler to organisations in developing strategies and plans to create products and services and differentiated customer experiences at low cost by optimising operations and processes.

Business analytics today increasingly leverages not just traditional structured data sets to answer business questions, but also the newer forms of Big Data that can help answer new questions or even answer old questions in newer ways. Big Data is helping to provide richer and newer insights into the questions that analytics has been answering, and does so by modelling a richer customer and operations scenario.

As such, it is incumbent on practitioners of advance analytics to be intimately familiar with technologies that help store, manage and analyse these Big Data streams (sensor data, text data, image data, etc.) in an integrated way along with more traditional data sets (e.g. CRM, ERP, etc.) This course is intended to equip you with an appreciation and a working knowledge of the Big Data technologies that are prevalent in the market today, along with how and when to use Big Data technologies for specific scenarios.

This course will provide a foundation to the Hadoop framework (HDFS, MapReduce) along with Hadoop ecosystem components (Pig, Hive, Spark and Kafka). It will also cover key Big Data architectures from the point of view of both on-premise environments and public cloud deployments.

Upon completion of this course, you will be able to:

• Explain the application of Big Data technologies and their usage in common business scenarios
• Compare, contrast and select Hadoop stack components based on business needs and existing architectures
• Analyse and explore data sets using Big Data technologies
• Design high-level solution architecture for different business needs both on premise and on the cloud

In this competitive global environment, the ability to explore visual representation of business data interactively and to detect meaningful patterns, trends and exceptions from these data are increasingly becoming an important skill for data analysts and business practitioners. Drawing from research and practice on Data Visualisation, Human-Computer Interaction, Data Analytics, Data Mining and Usability Engineering, this course aims to share with you how visual analytics techniques can be used to interact with data from various sources and formats, explore relationships, detect the expected and discover the unexpected without having to deal with complex statistical formulas and programming.

The goals of this course are to:

• Train you on the basic principles, best practices and methods of interactive data visualisations
• Provide you hands-on experiences in using commercial off-the-shelf visual analytics software and programming tools to design interactive data visualisations

Upon completion of this course, you will be able to:

• Understand the basic concepts, theories and methodologies of visual analytics
• Analyse data using appropriate visual thinking and visual analytics techniques
• Present data using appropriate visual communication and graphical methods
• Design and implement cutting-edge web-based visual analytics application for supporting decision making

This course focuses on data analytics in the context of social media. People interact with each other on social media on a daily basis, which generates a huge amount of social data. We are primarily interested in two types of social data: social relationship networks, such as friendship networks and professional networks; and social text data, such as user reviews and social status updates. Thus, this course integrates both network (formerly known as graph) mining and text analytics, with more emphasis on the network portion.

We will explore the fundamental data science and programming skills needed to process and analyse social data in order to reveal valuable insights and discover knowledge for making better business decisions. You will not only learn the different theories and algorithms for social data analytics, but also have a chance to apply them to real-world problem solving through in-class lab sessions and a course project.

The main programming language used in these lab sessions is Python. Throughout the course, more advanced tools and algorithms for social analytics will progressively be introduced. You are expected to complete a group project to demonstrate a set of full-stack abilities from development to analytics, knowledge discovery and business applications.

Upon completion of this course, you will be able to:

• Crawl and process social network and text data
• Perform analytics on social text data
• Perform graph mining algorithms on social network data
• Discover knowledge and insights gained from analysing social data
• Apply social analytic techniques on business problems

Many real-world business processes are highly complex, dynamic (evolving over time) and uncertain (subject to random events and behaviour). Such processes are easily found in various industries such as manufacturing, retail, healthcare and many more. It is challenging to model these processes mathematically using typical data models due to the complexity of the systems and the uncertain nature of the environment. Simulation becomes the feasible tool to model and study the design and operations of these real-world processes by examining its alternatives before, during and after process implementation or execution. Process simulation, when coupled with software which provides 3D models, can help decision-makers to visualise and arrive at evidence-based conclusions to improve their business operations.

This course covers the predictive and prescriptive analytics by modelling and simulating the behaviour of real-world processes using discrete-event simulation. You will be introduced to the application and theoretical background of simulation modelling. Topics covered include abstracting real-world process as a digital replica, statistical input modelling, verification and validation, design of experiments, statistical analysis of output data, simulation optimisation and digital twin. A simulation software will be used for modelling the complex business processes and designing data-driven solutions to achieve desired business outcomes.

Upon completion of this course, you will be able to:

• Model the dynamic and stochastic behaviour of a process as a system
• Explain how simulation can be used to model complex processes and solve related decision problems
• Design and construct discrete event simulation models using simulation software
• Analyse, interpret and communicate simulation results for evidence-based decision-making
• Apply statistical methods used in simulation analysis, experimental design, input and output analysis
• Evaluate the consequences of different operational business decisions and select the most desired to-be process using simulation

This course teaches machine learning methods and how to apply machine learning models in business applications. Through this course, you will develop the abilities to (i) process and analyse data from business domains; (ii) understand various machine learning methods, algorithms and their use cases; (iii) combine machine learning methods and algorithms to build machine learning models for specific business problems, and (iv) compare, justify, choose and explain machine learning models in the designated business scenarios.

We will cover both unsupervised learning algorithms, including principal component analysis, k-means, expectation-maximisation, spectral clustering and topic models; and supervised learning methods, including regression, logistic regression, Naïve Bayes classifiers, support vector machines, decision trees, ensemble learning, neural networks, deep learning models, convolutional neural networks and recurrent neural networks.

Upon completion of this course, you will be able to:

• Explain machine learning methods and algorithms, and their use cases
• Apply machine learning models in business applications
• Analyse the applicability of machine learning models
• Evaluate machine learning models by considering their effectiveness, efficiencies and the business use cases
• Create machine learning models by combining several basic machine learning methods and algorithms

Note: "Python for Data Science" or "Python Programming & Data Analysis” is a prerequisite for this course.

This course aims to provide both an overview and an in-depth exposition of key topics of data science from the perspective of a data-driven, technology-enabled paradigm for business application and innovation.

In this age of big data and machine intelligence, almost all aspects of business are bound to be profoundly impacted by this new wave of data and technology explosion. Moreover, disruptive innovation nowadays springs more often from the engine of big data and the intelligence extracted from them. It is our aim to help you gain a deeper look into data and computation on them, such that you:

1. learn the state-of-the-art of the data technology at the current frontier, as well as the possibilities to explore future innovations.
2. learn the pitfalls and limitations of what data and computations can do to gain a technologically savvy mindset and decision system.
3. understand and learn to evaluate the relevant key factors that interplay in data science from a business perspective.

Upon completion of this course, you will be able to:

• Analyse business problems from a computational perspective and translate them into corresponding data science tasks
• Identify data in the business ecosystem and perform data inventorisation and mapping for the business problem of interest
• Design and integrate data science concepts and notions to customise for the business problem
• Design and construct corresponding models and algorithms to derive a computational solution
• Identify appropriate metric and criteria to evaluate the computation results
• Interpret the computational solution in the business setting and translate back into actionable intelligence
• Propose action plans for the closed-loop data ecosystem to complete the analytical journey for iterative model improvement and result optimisation
• Evaluate non-technical aspect of the solution in terms of business, social and ethical aspect, including bias, fairness, cost, privacy, and so on

Note: “Python for Data Science” or “Python Programming & Data Analysis” is a prerequisite for this course.

Artificial Intelligence (AI) aims to augment or substitute human intelligence in solving complex real-world, decision-making problems. This is a breadth course that will equip you with core concepts and practical know-hows to build basic AI applications that impact business and the 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), image processing (e.g., predict labels for images), and natural language processing (e.g., predict sentiments from textual data).

Upon finishing the course, you are expected to understand basic concepts, models and methods for addressing key AI problems of:

• Representing and reasoning with knowledge
• Perception
• Communication
• Decision Making

Note: “Algorithm Design & Implementation” is a prerequisite for this course.

This course is designed for students who wish to develop their algorithmic skills and prepare themselves for deeper courses in artificial intelligence. It aims to train students in their algorithmic thinking, algorithm design, algorithm implementation and the analysis of algorithms. This course covers a wide range of topics, including data structures, searching, divide-and-conquer, dynamic programming, greedy algorithms, graph algorithms, intractable problems, NP-completeness and approximate algorithms. You are expected to design and implement efficient algorithms to solve problems in assignments, which require you to reiterate and continuously improve your solutions. At the end of the course, you should have the mindset to achieve more efficient algorithmic solutions as much as possible for business problems, and be inspired to learn more after this course by taking our electives from the Artificial Intelligence track.

Upon completion of the course, you will be able to:

• Explain important algorithms and their use cases
• Apply algorithms in business applications
• Analyse algorithms in terms of time and space efficiency
• Evaluate algorithms based on their applicability and efficiency
• Create algorithms in some new or unique business applications

Note: "Python for Data Science" or "Python Programming & Data Analysis” must be taken either prior to or at the same time as this course.

This course teaches machine learning methods and how to apply machine learning models in business applications. Through this course, you will develop the abilities to (i) process and analyse data from business domains; (ii) understand various machine learning methods, algorithms and their use cases; (iii) combine machine learning methods and algorithms to build machine learning models for specific business problems, and (iv) compare, justify, choose and explain machine learning models in the designated business scenarios.

We will cover both unsupervised learning algorithms, including principal component analysis, k-means, expectation-maximisation, spectral clustering, topic models; and supervised learning methods, including regression, logistic regression, Naïve Bayes classifiers, support vector machines, decision trees, ensemble learning, neural networks, deep learning models, convolutional neural networks and recurrent neural networks.

Upon completion of the course, you will be able to:

• Explain machine learning methods, algorithms, and their use cases
• Apply machine learning models in business applications
• Analyse the applicability of machine learning models
• Evaluate machine learning models by considering their effectiveness, efficiencies and the business use cases
• Create machine learning models by combining several basic machine learning methods and algorithms

Note: "Python for Data Science" or "Python Programming

Unless you have special exemption, you cannot take this course in your first term of study. As a result, you may need to extend into your fourth term of study in order to read this course.

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 realising natural language processing will be covered. During the course, you will select at least one course project, in which you will practise how to apply what you have learnt from this course about NLP technologies to solve real-world problems.

Upon completion of this course, you will be able to:

• Explain the basic concepts of human languages and the difficulties in understanding human languages
• Acquire the fundamental linguistic concepts and algorithmic concepts that are relevant to NLP technologies
• Analyse and understand state-of-the-art methods, statistical techniques and deep learning-based techniques relevant to NLP technologies, such as RNN, LSTM and Attention
• Obtain the ability or skill to leverage the exiting methods or enhance them to solve NLP problems
• Implement state-of-the-art algorithms and statistical techniques for specific NLP tasks and apply state-of-the-art language technology to new problems and settings

^*Note: "Python for Data Science" or "Python Programming & Data Analysis" is a prerequisite for this course.

"Applied Machine Learning" must be taken either prior to or at the same time as this course.

Automated planning and scheduling is a branch of Artificial Intelligence that concerns the realisation of strategies or action sequences, typically for execution by intelligent agents, robots and unmanned vehicles. In this course, we discuss the inner working and application of planning and scheduling models and algorithms embedded in systems that provide optimised planning and decision support. You will acquire skills in AI and Operations Research for thinking about, understanding, modelling and solving such problems.

Upon completion of this course, you will be able to:

• Understand problems and mathematical models for planning and scheduling
• Design efficient algorithms for specific planning and scheduling tasks
• Implement efficient algorithms for specific planning and scheduling tasks

^*Note: "Algorithm Design & Implementation" is a prerequisite for this course.

Unless you have special exemption, you cannot take this course in your first term of study. As a result, you may need to extend into your fourth term of study in order to read this course.

This course provides an introduction to systems with multiple “agents”, where system and individual performances depend on all agents' behaviours. We will cover theory and practice for strategic interactions among both selfish and collaborative agents. The most important foundation of the course is game theory and its direct application in modelling agent interactions, but we will also introduce how multi-agent systems can be applied to other fields in AI, such as machine learning, planning and control, and simulation.

The course should equip you with skills to model, analyse and implement complex multi-agent systems.

Upon completion of this course, you will be able to:

• Recognise different classes of multi-agent systems
• Identify and define agents in a distributed environment
• Design and use appropriate framework for agent communication and information sharing
• Design and implement multi-agent learning processes
• Model and solve distributed optimisation problems
• Understand game theory and use it in modelling and solutioning
• Apply game theory in mechanism design and social choice problems
• Model complex systems as agent-based models and simulations

Note : "Algorithm Design & Implementation" is a prerequisite for this course.

Unless you have special exemption, you cannot take this course in your first term of study. As a result, you may need to extend into your fourth term of study in order to read this course.

With pervasive digitisation of our everyday lives, we face an increasing number of options, be it in which product to purchase, which movie to watch, which article to read, which applicant to interview, etc. As it is impossible to investigate every possible option, driven by necessity, product and service providers rely on recommender systems to help narrow down the options to those most likely of interest to a target user.

A major part of this course will focus on the development of fundamental and practical skills to understand and apply recommendation algorithms based on the following frameworks:

• Neighborhood-based collaborative filtering
• Matrix factorisation for explicit and implicit feedback
• Context-sensitive recommender systems
• Multimodal recommender systems
• Deep learning for recommendations

Another important part of the course covers various aspects that impact the effectiveness of a recommender system. This includes how it is evaluated, how explainability is appreciated, how recommendations can be delivered efficiently, etc.

In addition to covering the technical fundamental of various recommender systems techniques, there will also be a series of hands-on exercises based on Cornac (https://cornac.preferred.ai), which is a Python recommender systems library that supports most of the algorithms covered in the course.

Upon completion of this course, you will be able to:

• Understand the application of recommender systems to businesses
• Formulate a recommendation problem appropriately for a particular scenario
• Understand various forms of recommendation algorithms
• Apply these methods or algorithms on various datasets
• Identify issues that may affect the effectiveness of a recommender system

Note : "Algorithm Design & Implementation" is a prerequisite for this course. "Applied Machine Learning" must be taken either prior to or at the same time as this course.

This series of 10 seminars will be conducted by various SCIS faculty members who will share their innovative translational projects related to AI that take place in their respective centres/labs. Through these seminars, you will learn about:

• Translating artificial intelligence to your business. Industry practitioners will be invited to share their experiences.
• A wide spectrum of the different application areas. You will be encouraged to ask the right questions and think out of the box.

This module is a graduation requirement (without credit) for AI Track students.

For the past several years, we have seen many industries (including government) transformed by digital technology. Yet, businesses are concerned about technology disruptions, with boards and CEOs of large organisations looking for business/IT leaders who can help them navigate through this disruption and at the same time, gain competitive advantage and business values by leveraging these technologies.

This is an SMU-X course focusing on IT trends and Digital Transformation Strategy. It aims to help you understand and leverage the latest IT trends to transform businesses, implementing the following learning:

• Key technology trends, their use cases and best practices
• Business value of IT and why it is important
• Business strategy and digital strategy frameworks – including digital ambition and digital KPIs

The aim of this course is to equip you with a framework in which you can build digital transformation strategy for organisations and help implement this strategy, not just from a technology perspective but from business and organisational change perspectives. This will in turn help you gain a competitive advantage when you are seeking a new job, or improve on your effectiveness by delivering strategic value to your organisation.

Upon completion of this course, you will be able to:

• Gain better understanding of IT management principles and best practices, which include IT Strategy to deliver business value, IT Governance, IT-enabled innovations, IT capabilities management, etc.
• Apply the knowledge gained to propose a Digital Business Transformation Strategy that enables organisations to better exploit Cloud Computing, Mobile Computing, Social Computing, Advanced Analytics, Internet of Things, AI, etc. in delivering business value
• Understand the challenges relating to management of change in a business setting

Organisations are led, managed and run by people, and people are also the key and fundamental factor for any organisational change to occur. To successfully transition into a new digital model, people need to be empowered and align with the organisation’s digital strategy. In this module, you will learn about digital talent management, principles of effective organisational change management, vision and case for change, key stakeholder management, communication and training management and sustaining culture change.

Upon completion of this course, you will be able to:

• Understand the importance of digital talent management, future workplace and fundamentals of change management
• Apply change management methodologies, techniques and tools
• Analyse gaps in the people side of change
• Develop a change management plan as part of an organisation’s digitalisation journey

The traditional waterfall approach to software development is not flexible enough to support digital strategies to deliver business results fast. Organisations need to become more agile in systems analysis and design beyond a linear sequential flow. Adopting DevSecOps delivers business value by increasing the speed of application releases to production, thereby, shortening the time to market. In this module, you will learn about the Agile model and its principles, DevSecOps practices and a large-scale experimentation (A/B-testing) approach

Upon completion of this course, you will be able to:

• Understand Agile principles and DevSecOps concepts
• Apply Agile principles and best practices
• Select appropriate Agile practices for different scenarios
• Develop a plan to implement Agile practices in a digital enterprise

Enterprises are increasingly turning to digital innovation and investments to drive business growth. A key aspect involves digital product management playing a crucial role in orchestrating different stakeholders to drive digital business success. However, shifting from a project-centric to a product-centric model requires major changes to the existing enterprise. In this module, you will learn the fundamentals of product management, business model canvas, pricing and segmentation, digital product life cycle and managing a product development team.

Upon completion of this course, you will be able to:

• Understand key digital product management concepts
• Apply digital product management best practices
• Implement processes to support the business and digital product development teams
• Develop a digital product plan as a part of digital transformation

Human-centred design is critical in the digital world. The digital systems developed must address the fundamental needs and requirements of the user. Design thinking can be used to bring about digital innovations. Through empathy, ideation, prototyping and testing, new solutions can be rapidly co-created, experimented with and enhanced in an iterative process. In this module, you will learn about business experimentation, the design thinking process, ethnographic methods, customer journey mapping, systems thinking and user experience design (UX). An external industry speaker will be invited to share real-world cases and examples whenever possible.

Upon completion of this course, you will be able to:

• Understand the importance of human-centred design and key design thinking concepts
• Apply design thinking methodologies, techniques and tools
• Interpret user needs and requirements
• Design a prototype to improve user experience

Digital governance is a subset of corporate governance that balances conformance and performance in objective setting and decision making for the digital enterprise. To achieve this outcome, management requires an enterprise-wide view of IT risks to articulate the potential risk impact on business outcomes. Information security incidents generate a high level of anxiety associated with a fear of the unknown. In this module, you will learn about information security, digital governance styles and mechanisms, data policies and procedures and risk management concepts and frameworks.

Upon completion of the course, you will be able to:

• Understand information security, digital governance and risk management concepts
• Apply digital governance and risk management frameworks
• Analyse the key governance issues and risks associated with a digital enterprise
• Formulate a digital governance and risk management plan for execution

Delivering business outcomes requires strong collaboration among different individuals and teams across the organisation. An enterprise architecture roadmap is sometimes used to illustrate the milestones, deliverables and investments required to manage change to a future state from the current state over a specific period for such outcomes. In this module, you will learn architecture principles and lifecycle methodology, different types of architecture viz. business, data and information, application and new technologies (e.g., cloud, analytics, IoT).

Upon completion of this course, you will be able to:

• Understand key enterprise architecture principles and concepts
• Apply enterprise architecture methodologies, techniques and tools
• Analyse existing gaps in the enterprise architecture
• Formulate a plan to integrate enterprise architecture with business

This course provides an introduction to cybersecurity. The focus is on basic cryptographic techniques, user authentications, software security and various network security topics. The course emphasises the applications of such technology in real-world business scenarios with case studies that examine how these ideas can be used to protect existing and emerging applications. Examples include secure email communications, secure electronic transactions over the Internet, secure e-banking, data confidentiality and privacy in cloud computing and secure protocols in realistic networking setups. Although the course covers fundamentals of cryptography, our emphasis is not on its mathematical background and security proofs, but rather on how such building blocks could be applied to satisfy business, communication and networking needs.

Upon completion of this course, you will be able to:

• Understand basic security concepts, models, algorithms and protocols
• Conduct basic software vulnerability analysis and construct corresponding exploits
• Design and implement secure user authentication on Internet-facing servers
• Formulate security requirements for real-world computing applications
• Analyse the latest security mechanisms in use

Managers often need to make important decisions related to different business challenges. Understanding how to build models to represent the business situation, analyse data, perform computations to obtain the desired outputs and analyse the trade-offs between alternatives will support good decision making.

This course focuses on using Microsoft Excel as a spreadsheet tool to build such decision models and to perform business analysis. You will be able to analyse trade-offs and understand the sensitivity impact of uncertainties and risks. The key emphasis of this course is on developing the art of modelling, rather than just learning about the available models, in the context of managing IT and operations decisions.

The primary focus is on using personal computers as platforms to solicit, consolidate and present information (data, assumptions and relationships) as a model for a variety of business settings; use the model to drive understanding and consensus towards generating possible actions; and finally, select a final course of action and assurance of execution success.

Upon completion of this course, you will be able to:

• Formulate business problems and integrate business analysis skills (statistics, mathematics, business processes, and quantitative methods) to model and appraise broad business problems
• Acquire computer skills to become motivated to self-learn problem analysis and know where to get such information and system resources
• Associate with a variety of software solutions (e.g., add-ins) and acquire competency in using Excel as an effective tool for analysis, model verification, simulation and management reporting, for possible use in other courses in your study programme and professional career

The aim of this course is to equip you with the essential knowledge to lead and direct IT projects for successful implementation. The module will introduce you to key elements of project management and provide an understanding of project management attributes across multiple dimensions of scope, time, cost, people, process, technology and organisation. You will be taught the process activities, tools and techniques required, and case studies will be used to illustrate practical situational issues and challenges in project management. Class sessions will include lectures, discussions, case studies and group work.

As projects invariably provide for the engagement of vendors for products or service, the course will teach you vendor engagement and management processes, which are significant responsibilities for a project manager. You will develop an understanding of vendor selection, contracts dealing, vendor performance and relationship handling to enable good collaboration with external partners for successful project closure.

Upon completion of this course, you will be able to:

• Describe and analyse the business and organisational imperatives for projects, the success factors and the pitfalls
• Analyse the IT project characteristics, challenges and requirements
• Apply the project management process methodology and best practices
• Apply the key elements of the process including knowledge areas and stakeholders
• Evaluate the tools and techniques for effective project management
• Apply personal skills attributes for project management leadership
• Apply the key elements of the vendor management process, including contracts, delivery, performance and relationship management

Standardisation of business processes, advancements in information and communication technologies and the continuous improvement of the capabilities of IT service providers around the world, among other factors, have led to an intense movement to "strategise" IT sourcing. In this course, we will investigate how enterprise IT services are (out/in/back) sourced in the financial and other services industries. We will also draw relevant examples and lessons learnt from a variety of industry sectors and leading companies. You will be exposed to the core issues involved in a variety of sourcing strategies (out/in/co-sourcing/captive), the industry best practices in managing IT sourcing and the emerging governance schemes for IT sourcing. In addition, we will analyse the supply side of sourcing – i.e., the vendor’s perspectives on managing sourcing relationships and how they deliver their promise of low-cost and high-quality services.

The format of the class will be seminar presentation, case studies discussion and role plays to simulate real-life situations (persuasion, building client trust and engagement in sourcing disputes, negotiations, board presentations, etc.)

Upon completion of this course, you will be able to:

• Understand the key factors influencing IT sourcing decisions
• Investigate the risks and tradeoffs involved in various forms of IT sourcing
• Analyse sourcing partner’s strength and weaknesses
• Understand key aspects of IT sourcing contracts
• Recognise the importance of inter-organisational relationship management and performance monitoring in global sourcing relationships
• Understand the impacts of outsourcing (both economic and social)
• Analyse sourcing trends and topical areas that the industry is trending towards

In the near future, we can envision a world in which billions of devices can sense, communicate and collaborate over the Internet in the same way that humans have interacted and collaborated with one another over the World Wide Web. This vision is now known as the Internet of Things. The knowledge created from these interconnected objects can potentially offer new anticipatory services to improve our quality of lives, and can be applied to various application domains, such as smart cities, smart homes, logistics and healthcare. In line with worldwide efforts to realise smart cities through IoT technologies, this course is intended to equip you with state-of-the-art in IoT technologies to enable you to conceptualise practical IoT systems to realise citizen-centric applications.

Upon completion of this course, you will be able to:

• Define and understand IoT
• Describe the impact of IoT on society
• Evaluate the potential and feasibility of IoT applications for large-scale smart city applications
• Acquire knowledge and gain hands-on experience in state-of-the-art IoT component technologies such as things, network connectivity and sense-making
• Conceptualise a sustainable and scalable end-to-end IoT system that generates actionable insights for stakeholders to solve real-world problems

Technologies play an important enabling role in digital transformation by improving efficiency and increasing productivity. As new disruptive know-hows continue to be developed, it is vital to keep up to date on the state-of-the-art knowledge in advanced science and digital technology. In this module, you will learn about use cases and best practices in enabling technologies such as data science, artificial intelligence, mobile and wearables, blockchain, 5G and communication technologies, cloud computing, IoT, social computing and APIs/microservices.

Upon completion of the course, you will be able to:

• Understand the fundamentals of enabling digital technologies and their trends
• Apply digital technology drivers and use cases
• Select relevant digital technology for different business scenarios
• Assemble a suite of appropriate digital technologies to enable digital transformation

The MITB Internship programme is an experiential learning experience for students to apply knowledge acquired in the MITB programme within a professional setting. The internships are aligned with the aims of the MITB programme and students’ respective tracks. It will provide you with career-related work experience and help you understand how your skills and knowledge can be used in the industry. You will be able to demonstrate functioning knowledge and identify areas of further development for your future career. The internship programme also provides a chance for you to establish a professional network within the profession.

Students may choose to do an internship for 6 months to apply and integrate what they have learnt. Our internships function on the basis of:

• Providing students with the opportunity to gain real-world working experience. The internship may involve an allowance, to be agreed between the internship company and student
• Internship work during the day, and SMU MITB classes in the evenings, on days when classes are scheduled
• Competitive selection by the respective internship companies, which may include interviews and assessments

Upon completion of the internship, you will be able to:

• Identify your own strengths, interests, skills and career goals
• Discover the wide range of companies and functions available and the skills needed for job success
• Develop communication, interpersonal and other critical soft skills required on the job
• Develop the work ethic and skills required for success in the internship
• Build a record of internship experiences
• Identify, document and carry out performance objectives related to the internship
• Build professional relationships with internship supervisors, mentors, learning buddies and other colleagues
• Prepare an engaging, organised and logical presentation summarising the internship
• Receive guidance and professional support throughout the internship
• Demonstrate independence, responsibility and time management

The MITB capstone project is an extensive, applied practice research project that is undertaken by students, supervised by SMU faculty members who have specific expertise and interest in the topic, and sometimes sponsored by external companies. It provides you with an individualised learning experience in which to integrate and synthesise the skills, theories and frameworks you have learned in the MITB programme. The project gives you an opportunity to delve in greater depth into business challenges or topics in financial technologies, analytics or the AI field. You will identify a problem, develop the approach and methods needed to address the problem, conduct the research and present the findings in both oral and written formats.

The capstone project experience aims to provide an authentic and practical interdisciplinary learning experience to take the knowledge and theory you have learned in MITB and apply it in a real-world setting. Upon completion of the capstone project, you will have gained theoretical and practice insight, including background and new information on topics within your tracks, and will be able to:

• Locate, collect and/or generate information and data relevant to the project
• Apply critical thinking through reading, research, and hands-on analysis of the problem and dataset
• Evaluate the strengths and weaknesses of current research findings, techniques and methodologies
• Select, defend and apply methodological approaches to answer the project’s questions
• Analyse the information and data, and synthesise them to generate new knowledge and understanding
• Manage the research project, monitor its progress, refine and pivot the approaches as needed
• Contribute to the development of academic or professional skills, techniques, tools, practices, ideas, theories or approaches
• Describe the limitations of the work, the complexity of knowledge and the potential contributions of interpretations and methods
• Present the project and its findings in an engaging, organised, and logical manner, summarising the entire capstone project
• Receive guidance and professional support throughout the project
• Demonstrate independence, responsibility and time management

The capstone project should be completed within 6 months and 10 months for full time and part time student respectively. The total number of hours committed to the project must be at least 182 hours for the entire duration of the project.

Capstone Projects are available on a competitive basis. To successfully clinch a Capstone Project, students are required to undergo the Capstone Project sponsor’s selection process which may include interviews and assessments. In some cases, Capstone Projects may include an allowance to the student either in the form of stipend or a scholarship for their work on the project, but this will be at the discretion of the company.

Mode of Work

• Students are to work on their Capstone Projects individually, in collaboration with a sponsoring company, under the supervision of an SMU appointed supervisor. In cases where the project scope is large enough to allow for the involvement of more than one student, a maximum of two may work on the project provided each student makes a distinct contribution towards the project.
• Students may be expected to work on-site at the sponsoring company's premises if necessary. This may help them in understanding the business domain, problem definition and even in gaining access to information systems, documents and resources available within the company. The work arrangement may assume the form of an internship with the sponsoring company.
• Strictest confidentiality is maintained between the sponsoring company, the student and SMU supervisor. Prior permission will be sought from the company before the use of any information, in any way, such as for presentation and report purposes.