Master of IT in Business

Master of IT in BusinessCourse Description

PAGECOURSE

FINANCIAL TECHNOLOGY (FINTECH)

Digital Banking & FinTech

Analytics in Financial Services

Corporate Banking & Blockchain

Digital Transformation in Retail Banking Technology

Financial Markets Systems & Technology

Digital Payments & Innovations

Table of Content

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4

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ANALYTICS TECHNOLOGY & APPLICATIONS (ANALYTICS)

Analytics Framework & Business Context

Data Management

Data Analytics Lab

Applied Statistical Analysis with R

Python for Data Science

Customer Analytics & Applications*

Operations Analytics & Applications

Big Data: Tools & Techniques

Visual Analytics & Applications

Text Analytics & Applications

Social Analytics & Applications

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ARTIFICIAL INTELLIGENCE & APPLICATIONS (AI)

Algorithm Design & Implementation

Introduction to Arti�cial Intelligence*

Applied Machine Learning*

Deep Learning for Visual Recognition*

Natural Language Processing for Smart Assistants*

AI Planning & Decision Making*

Recommender Systems*

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INFORMATION TECHNOLOGY MANAGEMENT (TECH)

Cybersecurity Technology & Applications

Spreadsheet Modelling for Tech & Ops Decisions

IT Project & Vendor Management

Global Sourcing of Technology & Processes

IoT: Technology & Applications

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10

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Process Analytics using Simulation 7

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Applied Machine Learning* 7

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9AI Translational Research Seminar (without credit)

Multi-Agent Systems*†

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Digital Transformation Strategy 11

www.smu.edu.sg/mitb [email protected] +(65) 6828 0921/ 6828 0878 /mitbsmu

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Master of IT in BusinessCourse Description

PAGECOURSE

PRACTICUM

Internship (2 CUs)

Capstone Project (2 CUs)

Table of Content

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Strategy & Organisation 12

Finance for Tech & Ops Managers 12

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A compulsory pre-requisite course is required.

These courses cannot be taken in students' �rst term of study. As a result, some full-time students may need to extend to their fourth term of study in order to read these courses. Only those with special exemptions can be allowed to read these courses in the �rst term of study.

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Financial Accounting (0.5 CU)

Management Accounting (0.5 CU)

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GENERAL MANAGEMENT FOR TECHNOLOGY & OPERATIONS (GENERALMGT)

FINANCIAL TECHNOLOGY (FINTECH)

Digital Banking & FinTech The �nancial services industry (FSI) has been undergoing transformational changes especially in the last decade. Drivers for these changes include competition, stringent regulations and digitization. FSI comprises of many types of �nancial players including banks, hedge funds and stock exchanges. Within banks we have many sub types ranging from consumer or retail banks to investment banks. This course will focus on the banks as they generate signi�cant jobs and are major contributors to the GDP.

Through this course, you will be able to:

Understand the foundational elements of the banking industry including the types of banks, products and services, delivery channels, risk and compliance.

Analyse banks using the Uni�ed Banking Process Framework (UBPF).

Apply digitisation to banking by di�erentiating how the di�erent technologies are used by the banks.

Understand FINTECH innovations and its transformational impacts in business, by studying settings in which recent innovations are making disruptive impacts.

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Analytics in Financial ServicesEvery bank is collecting ever-increasing large amounts of data every day. With such massive amounts of data collected and huge potential value that are found in these data for marketing, risk management or other purposes, the demand for analytics professionals to make sense of these data is increasing. Through this module, you will learn about:

Understand the process to undertake when given an Analytics project in Financial services.

Bridge the gap between analytics techniques and domain knowledge in Financial services during implementation of solutions.

Provide a more thorough view of the analytics value chain from collection of data to validation and how domain knowledge interacts with each of these stages.

Know the approaches to managing data and exploring data coupled with application of domain knowledge.

Be equipped with the necessary skills for them perform well as an Analytics Professional in the bank.

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Students will gain exposure to SAS Enterprise Guide and SAS Enterprise Miner, tools that are commonly used in the banks and highly sought after. Students will be exposed to the Analytics Process undertaken by the banks and be able to partake in these projects easily, being prepared for the next steps.

MITB Financial Technology & Analytics track students are given priority for enrolment into this course.

Corporate Banking & BlockchainThis module deals with speci�c corporate banking areas and the emerging area of blockchain and smart contracts which facilitate and enforce agreements between organisations through the use of decentralised technology. You will learn about:

Cash management & payments, the product areas of corporate lending, trade �nance, and corporate treasury.

The application of blockchain and smart contracts through the case study analysis of real-world situations.

Hands-on experience of banking systems.

Smart contract development using Ethereum.

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Digital Transformation in Retail Banking Technology

This course provides foundations retail banking products and services and digital transformational topics such as Open APIs; AI; Blockchain(DLT); Design Thinking and Agile Development. Through this module, you will learn about:

Core banking products, their process �ows and channels o�ered to customers.

Solution architectures supporting core banking products; challenges and criteria in evaluating solutions.

Linkages between business value and the processes and systems.

The increasing importance of FinTech, Mobile Banking, Risk Management, Security and Compliance (RiskTech & RegTech).

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Financial Markets Systems & TechnologyAs an introductory module to �nancial markets, products and services, markets and market participants, this module focuses on foreign exchange and equities products, and the processes that support the trading and settlement of these instruments. Topical coverage includes, amongst others:

Architecture and design of IT application systems that support �nancial market processes.

Deployment of STP to enable e�ciencies and eliminate risks by streamlining of end-to-end operations within and across �rms from trade initiation to settlement.

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Digital Payments & InnovationsBlockchain, cryptocurrency and other forms of innovation are fast surfacing to the forefront of digital payment. Through this module, you will learn about:

How blockchain impacts the design of payment design, its possibilities and limits to change.

The key components in the payment domain, as well as various 'domestic' and cross border payments.

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ANALYTICS TECHNOLOGY & APPLICATIONS (ANALYTICS)

Analytics Framework & Business ContextThis course tackles the business aspects of analytics and focuses on imparting sustainable skills that will be relevant regardless of changes to analytics technology landscape. It introduces a framework for Analytics that organizations can follow to ensure that they succeed in using analytics as a competitive di�erentiator. This is followed by setting the business context by way of introduction to select industries and common uses of analytics in them. The Plan-Do-See-Act framework for the data value chain is used to organize and explain how data is generated, manipulated, analysed and used across key functional areas. This is illustrated with case studies from banking, healthcare, retail & telco. Topics like blueprint de�nition, business case, change management, data privacy and ethics complete the broader aspects of analytics to make the course well rounded.


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Data Management

The proper design, implementation and management of data is critical in business processes. Through this module, you will learn about:

The fundamentals of relational database theory which include data management concepts such as data modelling, database design, and database implementation in current business information systems.

Applying data management knowledge to solve real-life business scenarios.

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Data Analytics Lab

Business decision-making is an art and a science. In this Information Age, the ability to uncover actionable insights from business data is an increasingly sought after skill-set. Through this module, you will learn about:

Data analytics techniques and data-driven business knowledge discovery anchored by principles, concepts, methods and best practices from both statistics and data mining.

Making data-driven decisions in an intelligent and informed manner.

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Applied Statistical Analysis with RApplied statistics enables the visualisation, analysis, interpretation and prediction of outcomes from massive amounts of data. Through this module, you will learn about:

Descriptive statistics which consists of basic concepts used to describe data.

Inferential statistics that make inferences and deductions about underlying populations from sample data.

Implementing both descriptive and inferential statistics within the R console.

Developing your own data application in RStudio, an integrated development environment for the R programming language.

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Python for Data ScienceThis course is aimed to provide both an overview and an in-depth exposition of some key topics of data science from the perspective of a data-driven problem-solving paradigm for business application and innovation, with Python as the tool.

This course introduces students to computational thinking, data-driven problem-solving techniques and basic programming. Students will learn the basic programming constructs, and apply programming techniques to solve real-world business problems.

Python will be o�ered as the programming tool in the �rst half of the course to prepare students for the data science tasks. Towards the end of the course, it will kickstart the learning of Python for Data Science by exposing them to various Python libraries (e.g., Numpy, Pandas and matplotlib) for the Data Science journey.

This course is intended for students with little or no programming experience.


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Operations Analytics & ApplicationsService sector businesses are typically faced with operations-management issues ranging from demand forecasting, inventory management, distribution management, capacity planning, resource allocation, work scheduling, and queue & cycle time management. Through this module, you will learn about:

The Data and Decision Analytics Framework, which aids in the identi�cation of the actual cause of business operation problems by collecting, preparing, and exploring data to gain business insights.

Capitalising on data analytics to exploit the linkages across processes, data, operations, analytics and technology, so as to o�er businesses alternative solutions to operations problems.

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Big Data: Tools & TechniquesBig Data has become a key asset when organisations develop their strategic understanding of consumer and market trends. Through this module, you will learn about:

The storage, management and analysis of Big Data streams along with traditional data sets arising from their customer relationship management and enterprise resource planning platforms.

Using Big Data insights to design products, services and di�erentiated and richer customer experiences at low cost by optimising operations and processes.

Key Big Data architectures.

The Hadoop framework (HDFS, MapReduce) along with Hadoop ecosystem components (Pig, Hive, Spark and Kafka).

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Visual Analytics & ApplicationsA “must-have” in the business practitioner’s toolkit is the ability to interact with the visual representation of business data and to detect meaningful patterns, trends and exceptions. With visual analytics, the unexpected can be discovered without having to deal with complex statistical formulas and programming. Through this module, you will learn about:

Principles, techniques and best practices of data visualisation and various real world applications of visual analytics.

Building and improving cutting-edge visual analytics tools, systems and applications using real world data and visually-enabled analytical reasoning.

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Text Analytics & Applications

Textual data streaming especially on social media account for a huge proportion of information that businesses can draw customer insights from to improve products and services, and guide targeted advertising, amongst others. Through this module, you will learn about:

Fundamental principles behind text analytics algorithms and some of the latest emerging technologies for solving real-world text analytics problems.

How you can extract value from unstructured and noisy textual data.

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Customer Analytics & Applications*Businesses are constantly on a lookout to bridge the gap between what customers want and how they can improve their product/service o�ering. To do so, they turn to data from and about their customers gathered through various means. Through this module, you will learn about:

Concepts and techniques of customer focused and data driven analytics, and supporting systems.

Creating cutting-edge business analytics applications and IT solutions for service industry companies to gain customer insights, including customer attitudes, behaviour, pro�tability and risk.

Emerging "next-practices" of real time, adaptive data analytics in the context of customers.

Customer focused analytical reasoning, methods and applications to support analytic tasks and challenges in speci�c industry contexts.

Evaluating and improving the usefulness and usability of data analytics applications.

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Note: The pre-requisite course for this is “Data Analytics Lab”.


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Process Analytics using Simulation

Simulation modelling allows organisations to peek into the future, predict what can or will happen, and maximise decision making e�ectiveness after examining alternative outcomes and scenarios. It is typically used to model systems that are too expensive or dangerous to build or experiment with in practice. Through this module, you will learn:

Modelling and simulation of the structure and behaviour of real-world systems using discrete-event simulation techniques. Examples may include processes from manufacturing, logistics and health services.

Theoretical topics like random variable generation, queueing theory, model veri�cation and validation, design of experiments and statistical analysis of input and output data.

Con�guration of a simulation software for modelling complex business processes

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Applied Machine Learning*

Machine learning forms the backbone of a great deal of personalised technology, including speech recognition and product recommendations. Through this module, you will learn about:

Principles, concepts, techniques and algorithms in the �elds of machine learning.

Creating applications using existing machine learning algorithms to solve business problems.

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Note: The pre-requisite for this course is “Python for Data Science”. However, students from Jan 2019 or older intakes are allowed to replace the pre-requisite course with “Data Analytics Lab”.

Social Analytics & Applications

The methods for the analysis of social networks originate from graph mining techniques. Through this module, you will learn about:

Python programming and build crawlers to get data from social networks. Such data include both social media data and social network data.

Processing and analysing data from various social networks. This includes abstracting social networks into graph models, and extending graph algorithms to applications on social networks. We will familiarise you with graph theory before introducing graph mining techniques.

Social in�uence, social links, ego networks and community detection catered for modern social networks analytics. You will apply both text mining and graph mining algorithms on social media and social network data.

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ARTIFICIAL INTELLIGENCE & APPLICATIONS (AI)

Introduction to Artificial Intelligence*AI aims to augment or substitute human intelligence in solving complex real world decision making problems. This course explores the breadth of the topic that will equip students with core concepts and practical know‐how to build basic AI applications that impact business and society. Through this module, you will learn about:

Cover searching (for e.g., scheduling meetings between di�erent people with di�erent preferences),

Probabilistic graphical models (e.g. building an AI bot that evaluates whether a credit card fraud has taken place based on transactions),

Planning and learning under uncertainty (e.g., building AI systems that guide doctors in recommending medicines for patients or taxi drivers to optimise their routes),

Multi‐agent systems (e.g., building next generation patrolling systems for critical infrastructure security),

Image processing (e.g. building systems that track and/or recognise suspicious people),

Natural language processing (e.g., building chat bots that can automatically and intelligently interact with customers across di�erent service industries).

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Algorithm Design & ImplementationMany AI solutions require data scientists to have deep knowledge in algorithm design and implementation. Through this module, you will learn about:

Algorithmic analysis in various applications and appreciate the e�ciency achieved by well‐designed data structures and algorithms.

Building AI solutions and implement them.

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Note: The pre-requisite course for this is “Algorithm Design & Implementation”.

Applied Machine Learning*Machine learning forms the backbone of a great deal of personalised technology, including speech recognition and product recommendations. Through this module, you will learn about:

Principles, concepts, techniques and algorithms in the �elds of machine learning.

Creating applications using existing machine learning algorithms to solve business problems.

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Note: The pre-requisite for this course is “Python for Data Science”. However, students from Jan 2019 or older intakes are allowed to replace the pre-requisite course with “Data Analytics Lab”.

Deep Learning for Visual Recognition*Recent advances in Deep Learning approaches have signi�cantly expanded the frontiers of visual recognition systems. Visual recognition tasks, such as image classi�cation, localisation, detection and segmentation, are at the core of various real-world computer vision applications in areas that include autonomous vehicles, face recognition and medical imaging, amongst others. Through this module, you will learn about:

Deep learning architectures with an emphasis on end-to-end models for image recognition and other tasks.

Implementing and training your own deep learning models for visual recognition tasks.

Cutting-edge techniques in computer vision.

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Note: The pre-requisite course for this is “Python for Data Science” OR “Applied Machine Learning”.

Recommender Systems*

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 a�ect the e�ectiveness of a recommender system.

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Natural Language Processing for Smart Assistants*Natural language processing helps machines understand and communicate with us in human language. It comprises applications such as conversational agents, machine translation, text summarisation and question answering. Through this module, you will learn about:

Key technologies behind a number of natural language processing applications with deep learning that have big impact in our daily lives.

Implementing and applying such systems.

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AI Translational Research Seminar (without credit)

This seminar will be conducted by various SIS faculty members who will share their innovative translational projects related to AI that take place in their respective centres/labs. Through this seminar, you will learn about:

Translating arti�cial intelligence to your business. Industry practitioners will be invited to share their experiences.

A wide spectrum of the di�erent application areas and will be encouraged to ask the right questions and think out of the box.

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

Note: There are two pre-requisite courses for this course unit: "Python for Data Science" AND “Algorithm Design and Implementation”.

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AI Planning & Decision Making*Building upon the pre-requisite algorithmic design and implementation module, this module deepens the framework for inner working and application of AI models and algorithms embedded in enterprise systems that provide optimised planning and decision support. Through this module, you will learn about:

Skills for thinking about, understanding, modeling and solving problems pertaining to enterprise systems.

Heuristics and advanced search techniques, graph algorithms, AI planning and scheduling, and optimisation under uncertainty and big data.

Numerous real‐world applications drawn from a diverse domain in transportation, logistics, travel, healthcare, safety and security.

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Multi-Agent Systems*

Multi-Agent Systems (MAS) is employed as an approach in this module to study complex business and social phenomena, particularly when there is a need to describe a system that is populated by many independent and heterogeneous decision makers. MAS can also be used to perform policy evaluations and generate decision supports by computing parameter changes at di�erent levels to observe its e�ects on various performance indicators. Through this module, you will learn about:

Both the theoretical and application frameworks of MAS.

NetLogo, an intuitive yet powerful modeling language for building MAS.

Utilising MAS in policy/strategy evaluations.

The importance of considering uncertainty and opponent modeling when designing strategic, tactical, and operational policies.

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Note: The pre-requisite course for this is “Python for Data Science” OR “Applied Machine Learning”.

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, and which applicant to interview, etc. As it is nigh impossible to investigate every possible option driven by necessity, product and service providers rely on recommender systems to help narrow down the options that are most likely of interest to a target user.

This course teaches the various aspects of recommender systems. By the end of the course, students would be expected to have developed the following abilities:

INFORMATION TECHNOLOGY MANAGEMENT (TECH)

Cybersecurity Technology & Applications

Cybersecurity has become a fundamental area of focus of private entities and governments alike. Through this module, you will learn about:

Fundamental concepts of cybersecurity, system design principles, basic cryptographic techniques, protocols, access control models, �rewalls and intrusion detection systems.

Applying these concepts to protect existing and emerging applications, such as secure electronic transactions, secure e-banking, and data con�dentiality and privacy in cloud computing.

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Spreadsheet Modelling for T&OManagers constantly use decision-making tools and models to aid them in their business decisions. Through this module, you will learn about:

Employing Microsoft Excel as a spreadsheet tool to build decision models and to perform business analysis.

Analysing trade-o�s and understanding the sensitivity impact of uncertainties and risks.

Developing the art and intuition of modelling in the context of managing IT resources and operations.

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IT Project & Vendor Management

While IT projects typically tend not to stick to schedule, managers need to exercise good judgment and e�ective management skill to ensure the success of their projects. Through this module, you will learn about:

Decisions and tradeo�s faced by project managers in managing schedules, team dynamics, resource scheduling, quality issues, schedule delays, cost overruns, con�ict management, negotiation and vendor management, amongst others.

Best practices promoted by the Project Management Institute (PMI) and documented in "A Guide to the Project Management Body of Knowl-edge" (PMBOK® Guide).

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Global Sourcing of Technology & Processes

Corporations are constantly strategising their IT sourcing to enhance their processes. Through this module, you will learn about:

How enterprise IT services are (out/in) sourced in the �nancial services industry.

Sourcing strategies (out/in/co-sourcing), the industry best practices in managing IT sourcing (through case-studies and guest lectures), and the emerging governance schemes for IT sourcing.

The vendor's perspectives on managing sourcing relationships and delivering on the promise of low-cost and high-quality services.

Sourcing decisions that include writing Request For Proposals (RFPs), bidding on RFPs, and designing contracts, amongst others.

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IoT Technology & Applications

The ubiquity derived from the Internet of Things (IoT) is rapidly changing and improving our quality of life and applications into domains like smart homes, cities, transport and even healthcare, amongst others. Through this module, you will learn about:

State-of-the-art IoT technologies.

Conceptualising practical IoT systems to realise citizen-centric applications.

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Digital Transformation StrategyDigital Transformation Strategy is an SMU-X course focusing on IT trends, Digital Transformation Strategy and Organization Change Management. It aims to help students understand and leverage on the latest IT trends to transform businesses. Students will work on real life business problems in the course term projects.

For this course, you will learn a digital transformation strategy framework and work with real life organizations (private or public sector) in proposing such a strategy for them. You will learn the following:

Key technology trends, their use cases and best practices

The business value of IT and why it is important

Business strategy and digital strategy frameworks – including digital ambition & digital KPIs

Organization change management best practices

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

Note: MITB students with at least 2 years of working experience are preferred.

GENERAL MANAGEMENT FOR TECHNOLOGY & OPERATIONS (GENERALMGT)

Financial Accounting (0.5 CU)

Management Accounting (0.5 CU)

Finance for Technology & Operations Managers

Financial accounting remains a foundational course in general management programmes. Through this half-module delivered once a week over 7 weeks, you will learn about:

Accounting basics and gain a better understanding of �nancial statements.

Accrual versus cash system of accounting, measurement di�culties, and timing judgment involved with assets, liabilities, revenues and expenses, amongst others.

Justi�cation of projects on the basis of a cost-bene�t analysis as well as discounting of cash �ows.

Fixed vs variable costs and conduct a Cost-Volume-Pro�t Analysis based on this distinction.

Employing accounting tools like costing systems, performance measures, cost allocation, planning and performance budgets, in order to enhance organisational e�ectiveness.

A core course in most general management programmes, management accounting is typically used by managers to plan, evaluate and control organisational operations. Through this half-module delivered once a week over 7 weeks, you will learn about:

Managers typically employ �nancial tools and concepts to make informed and sound �nancial decisions. Through this module, you will learn about:

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Applying management accounting into the IT and operations ambits of the �nancial and service sectors.

Management accounting practices and their potential impact on performance at an individual as well as organisational level.

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Ratio analysis, time value of money, risk-return trade-o� analysis, capital budgeting, stocks, bonds, and option pricing.

The terminologies, characteristics and features of various �nancial instruments transacted in the �nancial services industry.

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Strategy & OrganisationOrganisational success is experienced when strategy is followed through with precise execution. Through this module, you will learn about:

How you can execute your strategies through organisation, management and change leadership.

Theoretical and practical approaches in strategic analysis, formulation and execution.

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PRACTICUM

Internship (2 CUs)

Capstone Project (2 CUs)

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

Providing you the opportunity to gain real-world working experience. The internship may involve an allowance, to be agreed between you and the company.

Internship work during the day and attendance at SMU classes in the evenings on days when classes are scheduled.

Competitive selection by the respective internship companies, which may include interviews and assessments.

The programme’s capstone project is where you will integrate and apply all that you have learnt in an in-depth manner on one or more topics covered during the programme. Our capstone projects function on the basis of:

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Guidance by faculty supervisors who will be assigned to you.

A 6-month timeframe for full-time students and 10-month timeframe for part-time students.

A capstone project sponsor’s selection process. In some cases, this may include compensation to students in either an allowance or a scholarship.

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Master of IT in Business

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