Programme Overview
The MSc Business Analytics at Nanyang Business School equips you to excel in business analytics.
Our curriculum imparts a strong commercial sense through our teaching of business strategy and the tools to gain insights from data analysis, through courses such as AI and Big Data in Business, and Data Management and Visualisation.
We adopt a very hands-on approach, allowing you to apply classroom knowledge to real-world business situations. You will be able to lead analytics projects, both as the head of a business unit and analytics business lead, or as a professional
consultant with business domain expertise.
Core Modules |
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AI and Big Data in Business |
Advanced Database Management |
Advanced Programming |
Analytics and Machine Learning in Business |
Analytics Strategy |
Applications of Statistics |
Data Management and Visualisation |
Design Thinking & Technology Management |
Programming Essentials |
Strategies for Digital Transformation in Business |
Electives |
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AI with Advanced Predictive Techniques in Finance |
Beyond Algorithms: The Future of AI and GenAI Innovations |
Casual Inference |
Data Analytics for Credit and Related Risk |
Data Analytics Practicum |
Information Systems & ERP (SAP) |
Introduction to Cyber Security |
Machine Learning Methodology |
Operations Analytics |
Robotic Process Automation (RPA) for Analytics |
Storytelling Through Data Visualization |
Time Series for Business Analytics |
All Core Modules and Electives might be subject to changes.
Programme Calendar
Conducted in English, Nanyang Business School’s MSc Business Analytics classes and lectures are held in the evening on weekdays and/or on Saturdays at Nanyang Technological University’s vibrant main campus.
Full-Time Programme (1 Year)
The programme undergoes continuous improvement. As such, modules might be subject to changes.
Curriculum
Core Modules
The aim of this course is to provide a broad understanding and appreciation of the key challenges in AI and key drivers of AI. This course will equip you with the ability to build complete understanding on AI basics and the ability to apply AI in Business. You will be expose to AI techniques and strategies used to predict and automate business processes, which are relevant in Data Analyst, Data Scientist and Business Analyst roles.
Database management in the changing business landscape requires more than just an SQL Server installation on a server. This course aims to give you the understanding on types of data store and architecture designs available for today’s different business needs in both operational and analytical systems. Throughout the weeks you will learn to identify the organisation data asset, characteristics, and usage. Including, identifying the challenges and propose possible solutions to these problems. After this course you should be able to give valuable insights on making organisation wide decisions on technology design and implementation.
The aim of this course is to provide a broad understanding on data structure and explore the various programming paradigms in greater depth. This course will equip you with the ability to optimise codes and write libraries for data analysis. You will be provided with individual hands-on practices to enhance the coding skillset and opportunity to appreciate the application of advance data structure in business solutions.
This course is designed for post-graduate business analytics student like you who had basic structured programming background and is interested to learn how to program effectively with data structures and algorithms. It is oriented to enhance the student technical skillset.
The leaners proficiency in Data Structure and Algorithms plays an important role during interviews in any organisations which are data driven.
Technological advancement renders both opportunity and shortfall for business organisation. The difference lies on the business stakeholders’ perspectives and understanding on technologies and discovering the insights from data. Appreciation of analytics and machine learning can often turn the shortfall into opportunity and transforms a business. In this age of technology, the speed for human to catch up with technology is often the key for a business to stay relevant.
This course introduces the concept and technology behind analytics and how can business embed and embrace them in its operation. This course will walk participants through the fundamentals of analytics and machine learning deployed in business operations. This course condenses the main concepts in developing analytics skill and machine learning understanding. Students will utilise programming language and other tools to perform analysis. They will learn to process different types of data to perform suitable analysis in accordance to problem statement.
Analytics, Data Science and Artificial Intelligence are transforming business, social and government’s decision making. This course will show how important ideas and concepts were applied in real world applications to make better Data driven decisions.
This course aims to introduce statistics and probability theories as conceptual tools used to solve problems and make better decisions. The main focus hinges on the applications of the theories rather than developing the theories themselves. The course uses Python Programming to develop a flair for using concrete programming codes to gain statistical insights from large datasets.
As data has become valuable asset and even lifeline to businesses in almost all industries, future managers, business owners, or professionals from various disciplines would benefit from this course by being able to make data-justified decisions from statistical insights. The focus of the course on applications rather than theoretical mastery allows students with no or limited statistics background to continue to benefit from the course.
This course presents fundamental concepts and techniques in managing and presenting data for effective data-driven decision making. Topics in data management and design include data design approaches for performance and availability, such as data storage and indexing strategies; and data warehousing, such as requirement analysis, dimensional modelling, and ETL (extract, transform, load) processing. Topics in data visualisation include understanding data types, data dimensionalities, such as time-series and geospatial data; forms of data visualisation to include heat maps; and best practices for usable, consumable, and actionable data/analytic presentation.
The expected attendance should be comfortable to communicate and present complex concepts in English. Relevant working experiences (e.g., data management, database design and administrations, and visualization design) can be helpful to fully comprehend the course content and make rich contributions to classes.
Individuals who are interested to develop a career in the areas of analytics, visualisations, as well as business modelling and analysis should read this course.
The aim of this course is to provide a broad understanding of key innovation skills necessary for leading firms including design thinking, AI and blockchain. This course could lead you to high level management and is relevant for Data Analyst, Data Scientist, Business Analyst and Management trainee/manager roles.
The aim of this course is to provide a broad understanding on programming paradigms, coding techniques, managing data variables, preparing data for analysis, fundamentals of analytics, and the means to communicate analytics outcome. This course will equip you with the ability to write customized solutions to inform business decision, integrate statistical libraries for data analysis, and construct visuals or reports for business understanding. This module will provide you with hands-on practices to hone the coding skillset and opportunity to develop coding solution in a team.
This course is designed for post-graduate business analytics student like you to acquire the necessary skills to manage data and conduct business analytics programmatically. It is oriented to enhance your technical skillset.
A common set of questions asked of many analytics and technology professionals are these: I know it is possible to implement this analytics or tech solution, but why should we do it? What is the value and how will it benefit the firm? How can you explain this value using a logical framework?
This course is designed to help you answer these questions, using the appropriate managerial vocabulary. Using frameworks and models from strategy and economics, the objective of this course is to understand the role and impact of technology in managing strategic transformation in organizations. Technology enables firms to offer new products, create new customer channels, and dramatically improve the efficiency of their supply chains. The purpose of this course is to introduce the key issues in managing the firm’s technology resources; and to stress management’s role in creating the “technology-friendly” firm. While technically trained professionals are able to understand and manage the technical infrastructure in a firm, understanding how the technology elements interact and complement the core business remains important. In addition, the course will provide an overview of key information technologies in use today and how they support a variety of operational and strategic decisions within firms, designed to create value for the firm.
The course will focus on two specific aspects. First, in class, we will spend time understanding the common elements of the technology infrastructure in today’s organizations. As a manager, you will be engaged with making some of these crucial investment decisions, and you need to understand what the capabilities of the analytics technology are, as well as the trade-offs involved in selecting one versus another. Second, in order to implement analytical and technical solutions that can transform the firm, we need some frameworks and models to guide our thinking. The material in the course draws in equal measure from descriptions of technology and value, strategic frameworks, and casework.
Specific course objectives for this course are:
- Understand the interplay between corporate strategy and digital technology
- Understand techniques for evaluating investments in digital technology through financial and economic models
- Explore the key enablers of digitization and analytics strategy – digital goods, platforms, the sharing economy, and infrastructure
- Study the role of social networks as applied to social media, user-generated content, and social technologies in creating value
- Explore issues related to technology governance in the organization and successful adoption and implementation of technology and analytics solutions
- Study the importance of privacy, ethics, and fairness in the use of analytics and technology
- Explore models of disruption within the technology sector
Electives
For the finance analytic component, the aim is to provide a broad understanding on how to manage data, the process of preparing data for analysis, basics of analytics, using AI to automate financial analysis process and generate accounting reports. This course will equip you with the ability to write customized solutions to make informed business decisions, integrate statistical libraries for data analysis, create AI models to automate accounting and financial process. This module will provide you with individual hands-on practices to hone coding skills and creates opportunities to develop coding solutions in a team. They will utilize R and Python language as the medium of learning because it is one of the most in-demand coding language and its user-friendly syntax is well suited for the beginners. They will utilise modern development tools to turn information into insights including Keras’ Deep Learning model, Google Brain TensorFlow, and Cloud Platform.
This course will also include Generative AI and Web3 which will help participants in their future career path such as business analytics, data analytics or data science.
Purpose: Comprehensive Understanding of Generative AI Models: Equip students with a thorough understanding of the principles, architecture, and algorithms behind generative AI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models like GPT and BERT.
Ethical and Social Implications: Educate students about the ethical considerations and social implications of generative AI, including issues related to bias, privacy, and the potential impacts on various industries and societal norms.
Industry Relevance: Ensure course content is aligned with current industry practices and future trends in AI, preparing students for careers in tech companies, research institutions, and entrepreneurial ventures.
Audience: Graduates with a Background in Computer Science or Related Fields: Students who have completed an undergraduate degree in computer science, engineering, mathematics, or a related field, and have a strong foundation in programming, algorithms, and basic machine learning concepts.
Professionals Seeking Advanced Knowledge: Working professionals in the field of technology, data science, or related areas who are looking to deepen their understanding of AI and expand their skill set in generative AI models for career advancement or a shift in their professional focus.
AI Enthusiasts with a Strong Quantitative Background: Individuals with a keen interest in AI and strong analytical skills, possibly with backgrounds in statistics, physics, or other quantitative sciences, looking to transition into the field of AI.
Correlation is not causation, but there are ways to justify causation from observational data (without experiments) and estimate the magnitude of causal effect. Causal Inference provide the principles and methods to infer cause and effect from observational data, incorporate human domain knowledge and lay the foundation for higher level intelligence in AI, Machine Learning and Data Science to solve real world problems.
A selection of real-world applications will be discussed depending on the background or interests of the audience majority (if any) e.g. government, healthcare, finance, banking, medicine, HR, CXOs and etc. Hence, this course can be customized to the audience.
Financial and credit-related risk are essential areas of risk management in business and finance, and they leverage on the application of machine learning and data mining tools to large customer databases. This course provides fundamental tools for financial modeling and risk management by data analytic techniques. This includes stochastic modeling, decision trees, statistical approaches to data mining, logistic regression and neural networks. Course concepts are illustrated by R and Python codes applied to the evaluation of risk measures and to credit rating and scoring.
The Data Analytics Practicum (DAP) aims to tap on industry and business environments as teaching platforms for students to both acquire real-life working experience on applications of data analytics, and to apply theory and skills they have learned from classroom settings to actual project environments. It is the course where theory meets practice. The integral benefits from such real-life environment include extending students’ classroom learnings and serving as a transitioning bridge for MSBA students into business environments involving data science, big data, statistics, and machine learning.
Students will work full-time for at least 12 weeks (Trimester) on relevant data analytics/ AI projects in one of the following tracks:
- Work Track: each student will apply to preferred Singapore-registered companies, (for-pro t or non-pro t) organizations, and agencies for internship practicum positions, possibly with the help of GSCDO (Graduate Studies Career Development Office). The practicum positions may be paid, or unpaid, but must be full-time covering the 12-week trimester duration for this course and endorsed by a company’s signed contract offer. Under guidance primarily by company’s supervisor, students are expected to be driven to deliver set goals, agreed project scope, and/or related project activities, depending on company’s requirements and job description. Each student will also be assigned to a NBS supervisor for milestone guidance and general academic supervision.
- Project Track: for students who are unable to find suitable practicum position in time for the trimester or do not prefer Work Track, they will be assigned to faculty supervisors for supervised work on data-analytics-related projects. The supervisor will provide a “company” context and will act both as day-to-day project supervisor as well as NBS supervisor for academic supervision.
Students would work in a group of up to 4 (depending on supervisor’s final allocation) on projects delegated by supervisor. These projects may include:
(a) supervisor’s consulting project sponsored by industry partners, or
(b) supervisor’s on-going research project, or
(c) a capstone project proposed primarily by students.
Each student is expected to be self-driven in the following: reading up plenty of research materials and papers, researching on latest developments, performing detailed analyses of collected data, creating analytics or AI model, coding program development and deriving realistic recommendations in the final project paper. While the group of students will deliver a joint Final Report (different from project paper) for submission, separate grades are assigned by supervisor to individual student depending on performance, contribution, attitude, and outcomes.
This course introduces candidates to the concepts of business processes, business process redesign and using and managing information systems. The module also acquaints candidates to new technologies like the blockchain and robotic process automation. A firm conceptual foundation in information systems, a necessary pre-requisite for effective performance of business professionals in the information age and digital era, will enable candidates to better understand, evaluate and use information systems in their roles as business managers, professionals, analysts and consultants.
The course takes a practical approach with demonstrations in class and with candidates setting up master data, processing transactions, and generating financial reports and analyses using an industry-leading Enterprise Resource Planning or ERP system, SAP. The module will also acquaint candidates with new and emerging technologies like Artificial Intelligence (AI), the blockchain, cryptocurrency, cloud computing and robotic process automation.
After the completion of this course, candidates can apply their knowledge and skills to effectively assess business processes, and to use enterprise information systems in their future roles as business managers, professionals, and consultants.
With the proliferation of information and communication technology products and services, it is of paramount importance for everyone to understand the role of security and privacy in practice. Financial technology, data analytics and digital transformation of businesses have immensely amplified the value of data, thereby demanding the appreciation of cybersecurity threats and privacy issues, in every sphere of business.
This course aims to equip students with practical security skills which can be applied in various business systems at risk. Students will get an in-depth understanding of common security concepts such as security goals, vulnerabilities, malware, threat models, access control and authentication. Students will also learn common practical issues and attack patterns including side-channel attacks, integer overflow, and code injection attacks. After finishing the course, students will also develop the ability to draft and strategize a cyber risk mitigation plan.
This course provides essential concepts of machine learning and various supervised learning and unsupervised learning algorithms, such as SVM, K-NN classifiers, decision tree, K-means clustering, hierarchical clustering to business students so that they can apply them to solve real-world problems. It also discusses their applications and weaknesses.
This course seeks to provide business analytics graduates with a rigorous appreciation of the issues and methodologies necessary for ensuring the competitiveness of the operations function in a firm. The course will be taken by MSBA students as an elective. The course takes an analytics-based “process management’ viewpoint while addressing a range of strategic and tactical issues. After completing this course, you will be able to understand the key trade-offs required for designing, managing, and improving operations and processes in both manufacturing and service industries. This course will give you a sound analytical background and prepare you for a future business career where you will be responsible for the interface of the operations function with other business functions such as strategy, marketing, finance, accounting, and information technology.
The operations function in an organization is responsible for the design and management of systems concerned with the production of goods and services. Operations accounts for a significant percentage of the value added and cost in any business. With increased competition, firms face constant pressure to deliver timely and high-quality products and services. Leading edge companies have met these challenges by enhancing their competitiveness in business operations. Even a small advantage in operations can make the difference between winners and losers in a competitive marketplace. This course will provide MSBA students a strong analytical background related to the operations function and its interface with other business functions.
The main objectives of this broad-spectrum course are
i) to gain an appreciation of the Operations function and
ii) to understand the fundamental concepts and techniques necessary for designing, managing, and improving operations and processes in both manufacturing and service industries.
Analysing and continuously improving enterprise-wide processes is critically important for achieving world class performance in business operations. Hence the course will adopt a "process management" viewpoint while addressing a variety of strategic and tactical issues.
Topics covered in the course include Operations Strategy, Process Analysis & Improvement, Inventory Management and Supply Chain Management. The course will use a mix of lectures, case studies, and problem-solving exercises, to introduce the students to the latest tools, techniques, issues, and strategies in operations management.
The course aims to impart knowledge to allow one to use RPA to automatically extract data from various disparate data sources including web browsers, pdf, Microsoft Excel, Enterprise Systems and to clean dirty or disparate data format to achieve uniformity for analysis purpose.
The main audience for this course is data analysts who would benefit from the automation extraction and cleaning of data. It is also applicable to anyone who wants to automate their business processes that include high volume manual tasks. Anyone who takes this course benefit from improvement in productivity in their own work and looking at bigger career prospect as RPA skillset is one that is highly in demand right now.
Clearly communicating your findings and its implications for business to managers is the most important aspect of the business analytics role. This course presents the fundamentals of storytelling using data visualization for persuasion and effective evidence-based decision making. Topics in this course include understanding the analytics, designing the message, designing appropriate visuals, and developing communication strategies based on business analytics that aim to persuade calls to action. Being able to call decision makers to action through effective communication of results derived from the business analytics process is an important skill. The skill to develop effective data visuals and communicate can mean the difference between success and failure for an organisation’s aims and objectives.
This course covers the fundamental concepts of time series analysis and should give students a foundation for working with time series data. Topics include univariate ARIMA modeling, model identification and diagnostics, equilibrium correction model, and GARCH model and its applications in volatility estimation. This course will emphasize hands-on applications using computer software and real-life data and focus on using and applying techniques rather than deriving proofs and learning formulae. The prerequisite for this course is Linear Regression Models.
Leading People Globally
The need for business analysts who are well versed in the interdisciplinary fields of business and technology is greater than ever. Organisations are looking for graduates who are adept at working with large data set, applying insights and business intelligence
to solve business needs and drive transformation.
Through our programme’s strong industry partnerships, you can also take advantage of internship opportunities with industry leaders such as DBS, KPMG, and GE Digital. This can open doors for you to strong employment prospects, and provide real, hands-on experience to prepare you for a career in business analytics.