B.1 Analytics Framework & Business Context

While each service sector has its unique flavour of processes and operations, there are commonalities in most of the business processes of this sector. This course introduces the students to the service industry processes and stakeholders (both internal and external) that are working to meet customer needs. The concept of Unified Service Sector process framework, a framework to manage service sector industry operations, will be introduced and few industry specific frameworks will be examined and expanded as part of the course. Healthcare, Retail, Airport Operations and Telco are the service sector industries that are covered at varying level of detail.

The course will use lectures, class discussions, assignments and external speakers to cover the material and provide introduction to service industry business processes and data management.

NOTE: Blended Learning Delivery from Aug 2014. More Details

B.2 Data Analytics Lab

This course is about data analytics techniques and data-driven business 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 business data. In this course, students will be exposed to a collection of data analytics techniques and gain hands-on experiences in using these techniques. Student will focus their attention on the use and valuation of these techniques and solution to discover new knowledge from data and how to make data-driven decisions in an intelligent and informed manner. You will be also trained to understand the statistics rigour and data requirements of these techniques. Techniques discussed in this course form the foundation of the subsequent courses.

NOTE: Blended Learning Delivery from Jan 2015. More Details

B.3 Customer Analytics & Applications

The goals of this course are for students to (a) develop a strong understanding of the concepts and techniques of customer focused and data driven analytics, and supporting systems and (b) apply that understanding in creating cutting-edge business analytics applications and IT solutions for service industry companies to gain customer insights. This includes using these applications and solutions to analyse customers' attitudes, behaviour, profitability and risk. This involves topics such as i) customer focused analytical reasoning, ii) methods and applications to support analytic tasks and analytic challenges in specific industry contexts, iii) evaluating and improving the usefulness and usability of data analytics applications. Several case studies on customer focused data analytics and related IT projects are studied. Emerging "next-practices" of real time, adaptive data analytics in the context of customers are explored. Linkages across processes, data, operations, analytics, technology and architecture are highlighted.

B.4 Operations Analytics & Applications

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. This course is build on the processes that are learnt in “Analytics Framework & Business Context”.

In this course, students will be exposed to the Data and Decision Analytics Framework which helps the analyst identify the actual cause of business operation problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problems. Such a framework combines identification of the root causes by data analytics, and proposing solutions supported by decision analytics.

The objectives 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. Students 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 to include data analytics so as to exploit the linkages across processes, data, operations, analytics and technology, to offer businesses alternative solutions to operations problems.

B.5 Big Data: Tools and Techniques *^NEW

Big Data has become a key consideration when organizations today develop strategic outlook of the consumer and market trends. Big Data sets have become an enabler to organizations in developing strategies and plans to develop compelling product and services and differentiated customer experiences at low cost by optimizing operations and processes.

Business analytics today increasingly leverages not just the 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 provide richer and newer insights into questions analytics has been answering by modeling for 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 analyze 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 students with an appreciation and a working knowledge of 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). The course will also cover key Big Data architectures.

B.6 Visual Analytics & Applications

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 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 business data from various sources and formats, explore relationship, detect the expected and discover the unexpected without having to deal with complex statistical formulas and programming.

The course consists of three major components, namely: lecture, lab exercise and visual analytics project. The lecture component allows students to develop a comprehensive understanding of the principles, techniques and best practices of data visualisation and to expose students to various real world applications of visual analytics. In the lab session, students will gain hands-on experiences on using data visualisation toolkits and web-based data document programming libraries to design data visualisation. Finally, students will apply the understanding and skills learned in building cutting-edge visual analytics tools and systems using real world data. This component may involve: (i) advancing the theory of visually-enabled analytical reasoning, (ii) developing new methods to support analytic tasks in specific domains, (iii) applying existing methods and tools to analytic challenges in these domains, or (iv) evaluating and improving the usefulness and usability of visual analytics applications.

B.7 Text Analytics & Applications

Recent advances of technologies have enabled much easier and faster ways to generate and collect data, of which unstructured textual data account for a large proportion, especially on social media. Textual data contain much valuable information for businesses, such as consumer opinions, which can help improve products and services, and users’ personal interests, which can guide targeted advertising. However, textual data are inherently different from structured data. How to extract value out of the large amount of unstructured and oftentimes noisy textual data is a challenge many businesses face nowadays. This course will introduce to the students the fundamental principles behind text analytics algorithms and some of the latest emerging technologies for solving real-world text analytics problems. Students will acquire knowledge and skills in text analytics through lectures, class discussions, assignments and group projects using real-world datasets.

B.8 Social Analytics & Applications

This course covers necessary skills for analytics related to social medias and social networks. Students trained by this course are expected to have developed the abilities to (i) process and analyze data from various social networks; (ii) discover knowledge from social media and social networks; and (iii) apply the knowledge they have discovered on business problems.

This course first introduces Python programming, as Python is the preliminary programming language used in this course. With enough skills on the programming language, students are taught on building crawlers to get data from social networks. Such data include both social media data and social network data. However, social network data may not be as explicit as social media data due to the fact that different social networks structure their data about their customers and relationships among customers including customers’ behaviors in different ways. Students need to understand such differences, and extract social networks from various aspects that they deem important and relevant to some business applications in mind.

There are two categories of data mining techniques to be introduced for social media data and social network data respectively, namely, text mining techniques to analyze topics and sentiments of social media, and graph mining techniques to analyze structures of social networks. The main focus of this course is on graph mining techniques over text mining techniques since there is already an elective for text mining – text analytics and application (B.7). Since graph theory is not taught for undergraduate programs in most universities, this course will spend some time to get students familiarized with graph theory before introducing graph mining techniques.

Plenty of social analytics methods originate from graph mining techniques, students should first understand these techniques in order to apply these methods appropriately on social networks. In general, students will develop the ability to abstract social networks into graph models, and to extend graph algorithms to applications on social networks. Besides graph mining techniques, this course will also discuss social influence and social links which are more unique to social networks than graphs. There are also three advanced topics including ego networks and community detection catered for modern social networks analytics.

Last but not least, students are expected to apply a mixture between text mining algorithms and graph mining algorithms on social media and social network data, with some business applications they have already in mind when extracting social networks, as their course projects to demonstrate a set of full-stack abilities from developments to analytics, knowledge discovery, and even further to business applications.

B.9 Applied Machine Learning

This course is meant for students who wish to develop their knowledge in machine learning techniques and applications. It aims to convey the principles, concepts, techniques and algorithms in the fields of machine learning. Students are not expected to develop complex machine learning algorithm, but will be expected to create applications using existing machine learning algorithms to solve business problems.

B.10 Process Analytics using Simulation

Historically, analytics has referred to the use of statistical or data mining techniques to analyze data in order to determine what happened and why. Predictive analytics focus on what could happen in the future and typically uses statistical techniques, regression analysis, or machine learning algorithms to train a model to predict future outcomes. Predictive analytics attempts to make sense of data by finding patterns or fitting statistical models while simulation modeling attempts to mimic reality. Simulation can be used to analyze systems that have not yet existed. “What if” analysis concerning the system can be posed to assess the impact of various alterations to the system. Simulation modeling provides the ability to look into the future, predicting what can or will happen. As such, simulation can be used for modeling systems that are too expensive to build or too dangerous to experiment with in practice.

Simulation modeling can help to maximize the effectiveness of decision making by examining alternative outcomes and scenarios, before, during and after process implementation or execution. Powerful simulation software that allows realistic 3D models to be built with ease and support data analysis without needing sophisticated knowledge of statistics are available and increasingly more organizations are embracing such technology.

This course covers the modeling and simulation of the structure and behavior of real-world systems using discrete-event simulation techniques. You will be introduced to the application and theoretical background of simulation modeling. Topics includes modeling of manufacturing and materials handling systems and service systems through simulation. Theoretical topics include random variable generation, queueing theory, model verification and validation, design of experiments and statistical analysis of input and output data. An advance simulation package Flexsim will be utilized for modeling complex problems.

B.11 Marketing Analytics & Applications

Marketing is “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large” (American Marketing Association, 2013). Especially, business environments are now more dynamic and rapidly changing than ever. Even if we are now able to access and utilize various types of marketing data assets thanks to the recent technological development, such ‘data abundance’ has made it further difficult and challenging to design and articulate effective marketing strategies. For marketers in all kinds of industry, it’s important to get 1) an overall landscape picture of data analytics for marketing decision making, and 2) a framework to integrate various data analytics works, and 2) a capability to translate data analysis into actionable insights and strategies. This course aims at offering students these key assets for marketing analytics by focusing on three key phases of marketing activities: 1) Analytics for marketing ‘planning’, 2) Analytics for marketing ‘execution’, and 3) Analytics for marketing ‘measurement’. This course also discusses cutting-edge practices of digital marketing analytics.

B.12 IoT Technology & Applications

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 (IoT). 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, homes, transport and healthcare.

In line with worldwide efforts to realize smart cities through IoT technologies, this course is intended to equip students with the state-of-the-art in IoT technologies, to enable them to conceptualize practical IoT systems to realize citizen-centric applications.

B.13 Data Management

This course will cover fundamentals of relational database theory, important data management concepts such as data modelling, database design, and database implementation in current business information systems.

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

B.14 Applied Statistical Analysis with R

Recent advances of technologies have enabled more seamless ways of generating and collecting larger volume and variety of data. Applied Statistics is hence the relevant branch of Mathematics that is used to visualize, analyze, 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 across a semester and students will acquire knowledge in applying statistical theory for analyzing 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 students with statistical concepts in descriptive and inferential statistics while the second half will be focused on the practical aspects of implementing them within the R console. The course aims to progressively prepare students to eventually develop their very own data application in RStudio, an integrated development environment built for the R programming language.

C.1 Cybersecurity Technology and Applications

This course provides an introduction to cybersecurity. The focus is on fundamental concepts, system design principles, basic cryptographic techniques, protocols, access control models, firewalls and intrusion detection systems. While the emphasis is on foundations, case studies will be given that examine how these ideas can be used to protect existing and emerging applications, such as secure electronic transactions, secure e-banking, and data confidentiality and privacy in cloud computing.

C.2 Spreadsheet Modelling for Technology & Operations Decisions

Very often, managers need to quickly make important decisions related to managing IT investments, assets, operations and projects. Understanding how to analyze trade-offs between alternatives is difficult to do without a good model. This course focuses on using Microsoft Excel as a spreadsheet tool to build such decision models and to perform business analysis. Students will be able to analyze trade-offs and understand the sensitivity impact of uncertainties and risk. The key emphasis of this course is on developing the art and intuition of modeling, more so than just learning about the long list of available models, in the context of managing IT resources and operations.

C.3 IT Project & Vendor Management

IT projects never go according to plan and project managers must exercise good judgment and effective management skill to ensure success. Students will learn of the many decisions and tradeoffs that project managers make daily, namely managing schedules, team dynamics, resource scheduling, quality issues, schedule delays, cost overruns, to name a few. Students will be introduced to best practices promoted by Project Management Institute (PMI) and documented in "A Guide to the Project Management Body of Knowledge" (PMBOK® Guide).

Case studies of real world IT projects, computer-based simulation games and role playing negotiation exercises will be used in class to help students understand and practice project management skills of managing conflicts, responding to unexpected project problems and negotiating a win-win contract agreements between client and vendor.

IT projects inevitably involve vendors, and increasingly IT operations and IT infrastructures are outsourced to third-party service providers. The ability to manage vendors and negotiate favorable contract terms are essential skills for project managers. Determining when to use and how to select a vendor effectively can make or break even the most carefully planned projects. Students will learn and practise activities that are essential to effective vendor management.

C.4 Global Sourcing of Technology & Processes

Standardising business processes, advances 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 impetus to "strategise" IT sourcing. In this course we will investigate how enterprise IT services are (out/in) sourced in the financial services industry. We will also draw relevant examples and lessons learnt from other industries.

Students will develop an understanding of the core issues involved in a variety of 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 other side of sourcing will be analysed, that is, the vendor's perspectives on managing sourcing relationships and how they deliver their promise of low-cost and high-quality services. Students will have the opportunity to practise forming sourcing decisions - writing Request For Proposals (RFPs), bidding on RFPs, designing contracts, and the like.

D.1A Financial Accounting

The course aims to equip students with basic knowledge of accounting basics and the ability to better understand a set of financial statements. The course covers issues such as accrual versus cash systems of accounting, measurement difficulties, and timing judgment involved with assets, liabilities, revenues and expenses amongst others. Equipped with the language of business and students will hence enhance their communications with the finance people and will be able to justify project implementation on the basis of cost-benefits analysis as well as discounting the cash flows.

The course also provide students with the essential skills needed to improve analytical skills through problem solving using accounting information such as fixed versus variable costs and the use of this distinction to conduct a Cost-Volume-Profit (CVP) Analysis. Students are also required to understand relevant costs for decisions such as make versus buy and segment analysis. The course will also introduce students to costing systems, performance measures, cost allocation, planning and performance budgets, and how these accounting tools help organisations to be more effective.

This is a half module delivered once a week over 7 weeks.

D.1C Management Accounting for Technology & Operations Managers

This course helps students understand why management accounting is important for daily business decision, and how managers can use management accounting to plan, evaluate and control operations of organisations. It also aims to provide appropriate management accounting knowledge for IT and operations specialists in the financial and service sectors.

Students are required to link class learning to their work experience and share it with their cohort. Through interactive contributions from all participants, all students should increase their understanding and exposure of management accounting practices and their potential impacts on individual and company performances.

This is a half module delivered once a week over 7 weeks.

D.2 Strategy & Organisation

This course is designed to help students understand how the development of strategies and the organizational changes and leadership necessary to create competitive success. A key aspect of strategic success is execution, without which ideas remain only ideas and not vehicles for value creation. The topics of execution by way of the organization, management and change leadership will be discussed in detail. Lectures, case studies, readings, simulations, tools and their practical use, as well as class participation will be the primary vehicle to create the appropriate learning environment. The outcome of this course will be an effective theoretical and practical understanding by the students of strategic analysis, formulation, execution, and the role and practice of leadership, management and organization.

D.3 Finance for Technology & Operations Managers

Students will be exposed to key financial concepts and tools commonly used by managers in making sound financial decisions. These include ratio analysis, time value of money, risk-return trade-off analysis, capital budgeting, stocks, bonds, and option pricing. In addition, the course also covers the terminology, characteristics and features of various financial instruments transacted in the financial services industry. This helps the students interact effectively with the finance professionals in the different product areas.

D.4 HRM for Technology & Operations Managers

The aim of this module is to allow students to understand the importance of soft skills that must go hand in hand with technical competencies. Given the reality of globalization, this course attempts to highlight key areas that embraces the dynamics of self-management and working with others. Hence, the coverage spans over a broad range of topics designed to meet the HRM needs at the workplace. The course aims to enhance the building of knowledge and relevant soft skills through seminars and the use of case studies, articles and self-assessment tools in order to develop personal and professional skills in working with others. The course is also intended to be interactive so as to foster a learning environment that facilitates creative thinking in the process of peer learning.

This is a half module delivered once a week over 7 weeks.

E.1 Internship

Students may do a Financial Services Operations & Technology related internship in a company for 4 to 6 months. The internship will enable the students to apply and integrate what they have learnt and give them the opportunity to gain real-world working experience in Financial Services Operations & Technology. Internship is done during the school term. Students will do their internships during the day and attend classes in SMU in the evenings on days when classes are scheduled. Internship may involve payment from company, and the stipend amount is to be agreed between the student and the company.

Internships are available on a competitive basis. To successfully clinch an internship, students are required to undergo the company’s selection process which may include interviews and assessments.

E.2 Capstone Project

The general effort required for the Capstone Project is minimum 182 hours per student. The capstone project will enable the students to apply and integrate what they have learnt and give them an opportunity to delve in greater depth, into one or more of the topics covered in the courses. Faculty supervisors will be assigned to guide the students. Desirable length of project would be around 6-10 months, 6 months (Full Time), 10 months (Part Time).

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 compensation to the student either in the form of an allowance or a scholarship.

SMU Postgraduate Professional Development Series

The complete SMU postgraduate experience helps develop students into the business, community and government leaders of the future. While coursework and academic studies are the major components of a student’s development, another component integral to the SMU postgraduate experience is the Professional Development Series.

The SMU postgraduate Professional Development Series is designed to complement classroom studies through a series of seminars, workshops, competitions, excursions and social activities. Students from different postgraduate programmes will have the opportunity to interact through these activities, further enhancing the SMU postgraduate experience by expanding your network.

The Professional Development Series is a compulsory graduation requirement with activities scheduled on three pre-scheduled Saturdays, one in each term of the academic year. The following dates are subjected to changes: