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:
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.
- 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.
- 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.
- 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.
- 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.