Program Curriculum

The HKUST MScBA program is offered in both in one-year full-time and two-year part-time study modes. Both full-time and part-time students must complete 18 credits of Required Courses and 12 credits of Elective Courses, i.e., a total of 30 credits, to graduate. 

The list of Elective Course options is different for each intake and will be announced to students prior the start of the course registration period. 

The Required Courses and the Elective Courses offered in previous academic years are listed below for reference. 


Required Courses

Data Analysis

Covers various discrete and continuous probability models and their applications in business problems, estimation and testing of hypotheses, simple and multiple linear regression analysis.

Introduction to Business Analytics

This course describes the multiple regression method and its variants as exploratory and predictive models for fact-based management and data-driven decision making. The course adopts a case-based approach.

Visual Analytics for Business Decisions

This course focuses on various visualization analysis tools for business decisions. Making good use of visual analytics helps summarize and analyze large amounts of data effectively. In this course, the topics like visualization framework, visual analytics for spatial, temporal, network and textual data, and dynamic visualization will be included. Reporting visualization results for business decisions will also be discussed.

Business Analytics in R

The course introduces students to modern data analysis using R, with an emphasis on business, in particular, financial applications. Main topics to be covered include data exploration methods, regression analysis and time series analysis.

Social Media and Network Analysis

The course presents  concepts of social media and networks, methods and techniques  to explore and analyze the data crawled from social media and network, and business application of data mining of social network. . All these will be discussed in the context of finance and marketing. Python is the unique programming language for all cases and projects.  

High Dimensional Statistics with Business Applications

This course presents classical and modern approaches for analyzing multivariate and high dimensional data, including principal components, factor analysis, discriminant analysis, clustering, new developments in dimension reduction, large-scale covariance matrix estimation and multiple testing. All of these approaches will be covered in the context of Marketing, Finance and other important business areas. Computational issues for both traditional and new methodologies will also be discussed.

Business Modeling and Optimization

The science and technology of informed decision making with a focus on optimizing business processes. Spreadsheet decision modeling in Excel is used throughout. Emphasis is on problem formulation, spreadsheet-based solution methods, and managerial insights. Applications relate to managerial decision problems in diverse industries and functional areas including finance and accounting, human resources, marketing, and operations.

Simulation for Risk and Operations Analysis

The course covers the basic principles and approaches of computer simulation, and introduces Monte Carlo simulations with Excel and @risk, and discrete event simulation with Arena, focusing on their applications in solving business and operation problems.

Privacy Management in the Digital Age

This course provides an overview of information privacy and management in the information economy. It covers the fundamental concepts and dimensions of privacy; the impact of Internet marketing, customer relationship management, Web personalization, and data mining on consumer privacy; privacy enhancing technologies; and regulation of business practices.


Elective Courses

Special Topics in Business Analytics

This course covers selected topics in business analytics. Topics vary with recent interest, market emphasis and latest advancements.

Business Analytics Practicum

This course provides students practical experience in business analytics through independent study and business case analysis under the supervision of faculty members. 

Big Data Technologies

This course introduces the emerging technological paradigm for managing "big data”. Topics covered include a range of big data technologies, such as HDFS, MapReduce, Spark, Hive, Pig, etc.

Operations Analytics

This course focuses on the critical issues in the design, production and delivery of tangible goods, as well as intangible goods, in the business world. Topics include process analysis, capacity and bottleneck issues, waiting time management, inventory management, quality management, lean systems, supply chain management and e-commerce. Quantitative and qualitative tools will be taught to analyze the problems and create innovative solutions.

Big Data Analytics

Data plays an increasingly important role in business decision making. This course introduces the key concepts and applications of business analytics in the world of Big Data. Example business problems to be solved analytically include customer relationship management, financial trading, social media marketing, search engine strategy, etc. Hands-on experience with popular data analytical tools will be included.

Business Modeling with VBA

This course introduces students to business application modeling using Visual Basic Applications (VBA) in Excel. Students will learn to develop applications in different business areas, including finance, marketing, technology operations, etc. Essential features of VBA needed for application development will be introduced as part of the course and hence no prior experience with VBA is needed. Emphasis is on extensive hands-on problem solving.

Digital Business and Web Analytics

This course offers essential knowledge and tools for managers of digital business. Topics include e-commerce models, web analytics, Internet marketing, Internet pricing and strategy, web-based personalization, online-intermediaries, etc.