Business Analytics

The Business Analytics programme at the National University of Singapore is a comprehensive and dynamic degree designed to equip students with the fundamental skills and advanced knowledge necessary to excel in the rapidly evolving field of data-driven decision making. This programme integrates core concepts from statistics, computer science, and business management to prepare graduates for a diverse range of careers in analytics, finance, consulting, and technology sectors. Students will engage in rigorous coursework that covers data analysis, predictive modeling, machine learning, data visualization, and big data technologies, providing a solid foundation for interpreting complex datasets and deriving actionable insights. The curriculum emphasizes practical applications through real-world projects, internships, and collaborations with industry partners, ensuring that graduates are well-versed in current tools and techniques used in business analytics today. Through a combination of lectures, case studies, and hands-on training, students will develop critical thinking and problem-solving skills essential for tackling complex business challenges. The programme also fosters communication and teamwork skills, enabling students to effectively convey analytical results to stakeholders at all levels. Graduates of the Business Analytics programme will be prepared for roles such as data analyst, business analyst, data scientist, and strategic consultant. The Singaporean business environment, known for its vibrant innovation ecosystem and global connectivity, offers students excellent opportunities for industry engagement and employment after graduation. The programme is designed to be flexible, catering to students from diverse backgrounds with a passion for data and analytics, and aims to produce graduates who can contribute to the digital transformation of organizations across multiple sectors. With access to NUS’s extensive academic resources, state-of-the-art facilities, and a vibrant academic community, students will be positioned at the forefront of the analytics frontier, ready to lead in today’s data-driven world.

Essential Modules

Participants are required to complete 5 essential modules. The 5 essential modules build a cross-disciplinary foundation for Business Analytics. It enables participants to engage in rigorous study beyond the assumed disciplinary borders. This covers the interface between computer science, statistics, and other professional disciplines in the NUS Education framework. Students are expected to have knowledge of first-year of undergraduate mathematics, specifically in calculus and linear algebra. Programming knowledge is not necessary, but preferred.

The essential modules are:

1. Statistics
   This module introduces participants to the complete cycle of statistical analysis in business applications. Case studies are used throughout the programme for participants to appreciate the applications of various statistics techniques in tackling problems faced in real life situations. 
2. Deterministic Operations Research
   In this module, deterministic operations research (OR) models relevant to business decision-making will be covered. The emphasis is on model building, solution methods, and interpretation of results. 
3. Analytics in Managerial Economics
   In this module, participants will be looking at price formation and economic performance in imperfectly competitive markets, as well as game theory, information economics, and empirical modeling.
4. Decision Making Technologies
   Participants in this module will be looking at neural networks for classification, regression, clustering, genetic algorithm for optimization, decision tree methods, the support vector machine, and data mining.
5. Data Management and Warehousing
   Participants will engage in understanding database concept, design, and query as well as data warehousing concept, design and query.

Modules in Vertical Sectors

The vertical modules provide participants with a deep understanding of different analytic techniques required for specific industry sectors. It enables participants to become global citizens, sensitive to diverse vertical sectors to which Business Analytics can be applied, and to be aware of their potential to offer solutions. Importantly, these modules build on the knowledge, concepts and skills gained from the essential modules.

Participants must select at least three modules, with distinctive modules from at most two of the following vertical sectors. Participants who aspire to be a Business Analytics expert in other vertical sectors may take relevant advanced modules from the respective participating faculties, subject to approval by the Academic Committee.

1. Big-Data Analytics Techniques

• Big-Data Analytics Technology
  This module deals with the analysis of data which cannot fit in the computer's memory and application of such analysis to web applications.
• Cloud Computing
  This module aims to provide an overview of the design, management and application of cloud computing.

In addition to the modules listed below, participants can choose, with approval from the Academic Committee, to advance their knowledge in big-data analytics techniques and tools by taking modules from a wide range of advanced modules.

• Database Design
• Parallel and Distributed Algorithms
• Natural Language Processing
• Distributed Databases
• Advanced Computer Architecture
• Distributed Systems
• Knowledge Discovery and Data Mining
• Simulation and Modelling Techniques
• Computer System Performance Analysis

2. Consumer Data analytics

• Social and Digital Media Analytics
  This module aims to introduce concepts, methods and tools for social and digital media analytics, and in the application and management of such analytics efforts in industry sectors such as telecommunications and consumer retail.
• Hands-on with Business Analytics (Consumer)
  This module bridges the divide between technical skills and business know-how.

3. Financial & Risk analytics

• Quantitative Risk Management
  This module presents probability and statistical methods used by financial and non-financial institutions to model market, credit and operational risks.
• Pricing Derivatives and Fixed Income
  The objective of the module is to present arbitrage theory and its applications to pricing for financial assets.
• Hands-on with Business Analytics (Supply Chain & Finance)
  This module bridges the divide between technical skills and business know-how using a series of business case study discussions, guided group projects, and a final semester project.

4. Healthcare Analytics

• Healthcare Analytics
  This module will cover major topics in healthcare analytics, including clinical related analytics (diseases, medication, laboratory test, etc.) and healthcare operations related analytics (resource planning/scheduling, care process improvement, admission and readmission, etc.).
• Economic Methods in Healthcare Technology Assessment
  This module will cover Health Technology Assessment (HTA), Health econometrics, cost-effectiveness and economic evaluation in healthcare, and conjoint analysis.
• Information Technology in Healthcare
  In this module, students will gain knowledge and skills on managing healthcare IT projects in their workplace, learn about key considerations for healthcare IT project success, and be able to conduct an evaluation of healthcare IT products.

In addition to the modules listed above, students can choose, with approval from the Academic Committee, to advance their knowledge in healthcare analytics areas by taking modules from a wide range of relevant modules.

• Health Economics & Financing
• Management of Healthcare Organisations
• Measuring and Managing Quality of Care
• Introduction to Health Services Research
• Collection, Management & Analysis of Quantitative Data

5. Statistical Modelling

• Spatial Statistics 
  At present, almost all data that is collected is stamped with a location. This spatial information can help us in our understanding of the patterns in the data. The course is designed to introduce students to methods for handling and analyzing such data. Topics covered include basic concepts of spatial data, prediction (kriging) for stationary data, and modelling the three main types of spatial data - geostatistical, areal and point pattern. R will be extensively used to demonstrate and implement the techniques.

• Analysis of Time Series Data 
  Topics include Stationary processes, ARIMA processes, forecasting, parameter estimation, spectral analysis, non-stationary and seasonal models.

• Multivariate Data Analysis 
  Topics include Dimension reduction, cluster analysis, classification, multivariate, dependencies and multivariate statistical model assessment with emphasis on non-normal theory, computer intensive data-dependent methods.

In addition to the modules listed above, students can choose to advance their knowledge in statistical modelling by taking modules from a wide range of relevant modules.

• Probability and Stochastic Processes
• Survival Analysis
• Categorical Data Analysis
• Applied Data Mining

The MSBA Capstone Professional Consulting Project

The industry-linked capstone professional consulting projects analyse and provide solutions to today's real-world business analytic problems. It enables participants to become leading experts in Business Analytics field and to be constructive and responsible members of a society.

Programme requirements for the Bachelor of Business Administration with Specialisation in Business Analytics at National University of Singapore include a combination of core courses, elective modules, and capstone projects designed to equip students with essential skills in data analysis, statistical methods, and business strategy. Students are expected to complete foundational courses in mathematics, statistics, and programming to build a strong analytical base. Advanced coursework covers topics such as data mining, machine learning, and decision support systems, with an emphasis on practical application in business contexts. To graduate, students must fulfill a minimum credit requirement, typically around 120 academic units, which include lectures, tutorials, and project work. Additionally, students are encouraged to undertake internships or industry projects to gain real-world experience and develop professional competencies. Language proficiency requirements, such as proficiency in English, are mandatory, and students must pass all assessments and maintain a minimum academic standing, often a cumulative GPA of 2.0 or higher. Certain electives may require prerequisites or departmental approval. The programme also integrates case studies, group projects, and presentations to foster teamwork and communication skills vital for business analysts. Students are advised to consult the official university handbook for detailed course descriptions, enrolment procedures, and specific graduation prerequisites.

The National University of Singapore offers various financial support options for students enrolled in their Business Analytics programme. Scholarships are available for both undergraduate and postgraduate students, including merits-based scholarships that recognize academic excellence, leadership qualities, and potential contributions to the university community. These scholarships often provide full or partial tuition fee waivers, as well as stipends to assist with living expenses. The university also offers bursaries and grants aimed at students demonstrating financial need, ensuring that talented individuals can access high-quality education regardless of their economic background. Students are encouraged to apply for external funding sources, such as government loans and scholarships available through Singapore’s Ministry of Education and other organizations, which can supplement university-provided financial aid.

The NUS Business Analytics programme may also be eligible for postgraduate loan schemes, particularly for students pursuing master’s degrees, allowing them to finance their studies through manageable repayment plans post-graduation. In addition to direct financial aid, the programme's students benefit from work-study opportunities, internships, and industry collaborations that can help offset costs and provide practical experience, enhancing employability upon graduation. For international students, specific financial assistance options are available, including tuition discounts, scholarships tailored for international applicants, and support services to help navigate the financial aspects of studying abroad.

The university maintains a dedicated Office of Financial Aid & Scholarships, which provides comprehensive guidance on available funding options, application procedures, and eligibility criteria. Detailed information about the application process, deadlines, and documentation requirements can be accessed through the official NUS website. Overall, the financing studies at NUS are designed to ensure that students from diverse backgrounds can pursue their Business Analytics degrees without undue financial hardship, facilitating access to Singapore’s vibrant academic environment and career opportunities in data analytics, business intelligence, and digital transformation.

The Bachelor of Business Administration in Business Analytics at the National University of Singapore is a comprehensive undergraduate program designed to equip students with the necessary skills and knowledge to excel in the data-driven business world. The curriculum offers a blend of theoretical foundations and practical applications, focusing on areas such as data analysis, statistical methods, machine learning, and business intelligence. Students are trained to interpret complex data sets, develop models, and make strategic decisions that enhance business performance. The program emphasizes a strong foundation in quantitative methods, programming, and analytics tools, including R, Python, and SQL, preparing graduates to handle real-world data challenges across various industries. Instruction is delivered by experienced faculty members who are experts in the fields of data science, economics, and business management. In addition to core coursework, students engage in project-based learning, internships, and industry collaborations to gain hands-on experience. The university's state-of-the-art facilities and partnerships with leading tech companies provide ample opportunities for practical exposure. Graduates of the program are well-equipped to pursue careers in data analytics, consulting, finance, marketing, and operations. They may also choose to continue their education through advanced degrees in analytics, data science, or business administration. The program's interdisciplinary approach ensures that students develop a holistic understanding of the role of analytics in modern business practices, making them valuable assets to employers seeking data-savvy professionals. Overall, the Business Analytics programme at NUS aims to shape innovative, analytical thinkers capable of leveraging data to solve complex business problems and drive strategic growth in today's competitive environment.