Master of Science [M.Sc] (Business Analytics)

Students 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. Students who know first-year undergraduate mathematics, specifically calculus and linear algebra and programming knowledge would have an advantage.

 

Analytics in Managerial Economics

In this module, students will look at price formation, economic performance in imperfectly competitive markets, game theory, information economics, and empirical modelling.

Data Management and Warehousing

Students will learn about database concept, design and query as well as data warehousing concept, design and query.

Operations Research and Analytics

This module focuses on model building, solution methods, and interpretation of results that are relevant to business decision making.

Foundations of Business Analytics

 

This module aims to provide a foundation for data analytics techniques and applications.  It aims at:

• Emphasizing on understanding of intuitions behind the tools, and not on mathematical derivations;
• Incorporating real-world datasets and analytics projects to help students bridge theories and practices; and
• Equipping students with hands-on experiences in using data analysis software to visualize the concepts and ideas, and also to practise solving problems.

 

The module covers commonly used analytics tools such as logistic regression and decision tree. 

Advanced Analytics and Machine Learning (Pre- requisite DBA5106)

 

This module provides a general introduction to advanced data analytics methods. It is the sequel to the first foundation module and covers advanced decision tree techniques, support vector machine, unsupervised, semi-supervised and reinforcement learning. 

 

Big Data Analytics Technology 
Students learn to analyse data that cannot fit in the computer’s memory and apply such analysis to web applications. Topics include map-reduce as a tool for creating parallel algorithms that operate on very large amount of data, similarity search, data-streaming processing, search engine technology, and clustering of very large high-dimensional datasets.

Cloud Computing 
Students gain an overview of the design, management and application of cloud computing. Topics include managing virtualization, cloud computing environments, cloud design patterns and use cases, data centre architectures and technologies, cloud services fulfillment and assurance, orchestration and automation of cloud resources, cloud capacity management, cloud economics, case studies.

Neural Networks and Deep Learning
Students gain knowledge of deep neural network and the ability to apply deep learning methods effectively on real world problems. Students design, develop and evaluate deep learning-based solutions to practical problems, such as in the areas of computer vision, bioinformatics, fintech, cybersecurity and games.

The NUS Master of Science in Business Analytics (MSBA) can be taken either as a 1-year (13-month) intensive full-time programme or a 2-year part-time programme. The corresponding maximum candidature periods are 2 years and 4 years respectively.

 

Full-time students will be matriculated in August, the start of the academic year, and complete their studies by end April/early May the following year.

 

Full-time students admitted to the programme must attend a two-week bootcamp held two weeks prior to the start of Semester 1. Part-time students are strongly encouraged to attend. The bootcamp familiarises students with NUS academic culture, as well as skills and knowledge that are foundational to business analytics.

 

The Capstone Professional Consulting Project is a three-month internship between May and August/September of each academic year.

 

Students must achieve a final Cumulative Average Point (CAP) of 3.0 (average grade of B-) out of 5.0 to graduate. Failure to meet any of the above requirements will result in termination from the programme. Students will complete the degree requirement and graduate in September, and join the NUS commencement ceremony in July of the following year.

Tuition fee for Academic Year (AY)2022/23 intake:

• Application fee: S$100
• Singaporeans and PRs - S$53,000
• International students - S$58,000

Tuition fees are subject to Singapore Goods and Services Tax (GST - 7%).

Tuition fees do not include the cost of recommended textbooks and readings.

 

The cost of travel, accommodation and miscellaneous expenses are not included and are to be borne by the participant.