programme curriculum students in classroom

Essential Modules

Participants will complete 5 essential modules to build a cross-disciplinary foundation for Business Analytics and 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. Participants who know first-year undergraduate mathematics, specifically calculus and linear algebra and programming knowledge would have an advantage.

The essential modules are:

  1. Statistics
    This module introduces participants to the complete cycle of statistical analysis in business applications and teaches them the application of various statistics techniques in tackling problems faced by businesses.
  2. Deterministic Operations Research
    This module focuses on model building, solution methods, and interpretation of results that are relevant to business decision making.
  3. Analytics in Managerial Economics
    In this module, participants will look at price formation, economic performance in imperfectly competitive markets, game theory, information economics, and empirical modelling.
  4. Decision Making Technology for Business
    This module introduces data mining methods by describing issues at the data pre-processing phase, such as handling missing values and data transformation. It includes core concept of well-known classification algorithms, emerging topics such as text mining and deep learning as well as important and practical techniques to carry out cost sensitive classification and features selection.
  5. Data Management and Warehousing
    Participants will learn about database concept, design, and query as well as data warehousing concept, design and query.

Modules in Vertical Sectors

In these vertical modules, participants delve deep into understanding different analytic techniques required for specific industry sectors and build upon knowledge, concepts and skills learnt in essential modules. These modules highlight the diverse vertical sectors that Business Analytics can be applied to and allow participants to innovate, devise and refine techniques and tools to solve complex problems.

Participants must select at least three 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
    Participants learn to analyse data that cannot fit in the computer's memory and application of such analysis to web applications.
  • Cloud Computing
    Participants gain an overview of the design, management and application of cloud computing.

2. Consumer Data analytics

  • Social and Digital Media Analytics
    Participants learn concepts, methods and tools for social and digital media analytics, and the application and management of such analytics efforts in industry sectors such as telecommunications and consumer retail.
  • Hands-on with Business Analytics (Consumer)
    Participants use tools and real-data while immersing themselves in a series of activities to bridge the divide between technical skills and business know-how.
  • Marketing Analytics
    Participants acquire critical analysis and decision making skills as they learn how to transform raw data into business intelligence using market research methods and analytics.

3. Financial & Risk analytics

  • Quantitative Risk Management
    Participants learn probability and statistical methods used by financial and non-financial institutions to model market, credit and operational risks.
  • Pricing Derivatives and Fixed Income
    Participants take part in discussions of how to use assets in the hedging of different risks, arbitrage theory and its applications to pricing for financial assets.
  • Hands-on with Business Analytics (Finance)
    Participants focus on the applications to algorithmic and systemic trading in finance through the use data analytics.

4. Healthcare Analytics

  • Information Technology in Healthcare
    Participants learn the use of Information Technology in Singapore healthcare and how they can successfully manage and evaluate IT projects in their workplace.
  • Healthcare Analytics 
    Participants gain insights to healthcare analytics, including both clinical-related and healthcare operations related analytics, and how they can select the right techniques to solve relevant problems.
  • Economic Methods in Healthcare Technology Assessment
    Participants learn how they may conduct their own research and understand research by others with Health Technology Assessment (HTA). This module also includes health econometrics, cost-effectiveness and economic evaluation in healthcare, and conjoint analysis.

5. Statistical Modelling

  • Spatial Statistics
    Participants learn how to see patterns in data, the methods to handle and analyse them and the techniques and its implementation using R.
  • Probability and Stochastic Processes
    Participants gain a good understanding of basic results and methods in probability theory and stochastic models, and apply state-of-the-art Biostatistics research methodologies.
  • Analysis of Time Series Data
    Participants with a strong interest in statistics can consider this module which includes stationary processes, ARIMA processes, forecasting, parameter estimation, spectral analysis, non-stationary and seasonal models.
  • Multivariate Data Analysis
    Participants with a strong interest in statistics can consider this module which includes dimension reduction, cluster analysis, classification, multivariate, dependencies and multivariate statistical model assessment with emphasis on non-normal theory, computer intensive data-dependent methods.
  • Survival Analysis
    Participants with a strong interest in statistics can consider this module which includes censoring, probability models for survival times, graphical procedures, inference procedures, parametric and nonparametric models, cox proportional hazards model, regression models for grouped data, Bayesian predictive distributions.
  • Categorical Data Analysis II
    Participants with a strong interest in statistics can consider this module which includes categorical response data and contingency tables, loglinear models, building and applying loglinear models, loglinear and logit models for ordinal variables, multinomial response models.

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 immerse themselves in a real-world business situation and contribute constructively back to the Business Analytics community.


NUS Business Analytics Centre
I3 building
21 Heng Mui Keng Terrace
Singapore 119613

Connect With Us

Subscribe to our Mailing List

© Copyright 2001-2019 National University of Singapore. All Rights Reserved.
Legal | Branding guidelines | Contact Us