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Master of Science in Business and Data Analytics
Programme
Master of Science in Business and Data Analytics
理學碩士(商業及數據分析)
Award Title
Master of Science in Business and Data Analytics
理學碩士(商業及數據分析)
Offering Academic Unit
This Programme is jointly offered by:
Department of Information Systems
Department of Management Sciences
College of Business
Mode of Study
Combined mode

Normal Period of Study

Full-time: 1 year
Part-time/Combined mode: 2 years

Maximum Period of Study

Full-time: 2.5 years
Part-time/Combined mode: 5 years

Credit Units Required for Graduation

30

Programme Aims

This programme aims to cultivate students with professional knowledge of business data analytics through active learning of the theories, methods, supporting techniques across a wide range of knowledge areas such as applied statistics, big data management, data mining, and social media analytics.

Programme Intended Learning Outcomes (PILOs)

Upon successful completion of this Programme, students should be able to:

  1. Describe the theories, methods, and techniques for the management and analysis of complex data structures arising from typical business applications.
  2. Apply the data analytics theories, methods and techniques to design and build data-driven solutions to enhance business decision-making. 
  3. Demonstrate an understanding of and competence in the key concepts and techniques in different areas of statistics.
  4. Apply acquired quantitative knowledge to solve business problems and to make decisions.
  5. Use appropriate statistical or analytics software to investigate and solve practical problems.


Programme RequirementsCatalogue Term : Semester A 2024/25



1. Programme Core Courses (12 credit units)
Course CodeCourse TitleCredit Units
IS5413Database Management Systems3
IS6335Data Visualization3
MS5217Statistical Data Analysis3
MS6711Data Mining3


 2. Stream Core and Elective Courses (18 credit units)
Students are required to choose from one of the two streams listed below to fulfill the elective course requirements.

The Information Analytics Management (IAM) Stream
Students are required to complete the following stream core course: 

Stream Core Course (3 credit units)
Course CodeCourse TitleCredit Units
IS6941Machine Learning and Social Media Analytics3


and 15 credits of elective courses with at least 9 credits chosen from the following stream elective course list. The remaining credits may be chosen from the postgraduate elective courses offered by any department in the College of Business.

Stream Elective Courses (15 credit units)
Course CodeCourse TitleCredit Units
IS5740Management Support and Business Intelligence Systems3
IS6200Blockchain Technology and Business Applications3
IS6321Business Intelligence Applications3
IS6400Business Data Analytics3
IS5312Analytical Programming with Python3
IS5940Innovation and Technology Entrepreneurship3
IS5540Project Management and Quality Assurance3
IS6921Knowledge Management3
IS6912Information Systems Project6
IS5542Generative Artificial Intelligence for Business3
IS6423Artificial Intelligence for Business Applications3
IS6620Large Language Model with Prompt Engineering for Business3


The Quantitative Analysis for Business (QAB) Stream

Students are required to complete the following stream core course:

Stream Core Course (3 credit units)
Course CodeCourse TitleCredit Units
MS5218Applied Linear Statistical Models3


and 15 credits of elective courses with at least 12 credits chosen from the following stream elective course list. The remaining course may be chosen from the postgraduate elective courses offered by any department in the College of Business.
 
Stream Elective Courses (15 credit units)
Course CodeCourse TitleCredit Units
MS5216Decision Analytics3
MS5223Project Management3
MS5313Managerial Decision Modeling3
MS5318Predictive Analytics with Excel and R3
MS6211Statistical Modelling in Risk Management3
MS6219Predictive Modeling and Forecasting for Business3
MS6221Predictive Modeling in Marketing3
MS6601Statistical Modelling in Economics and Finance3
MS6712Contemporary Topics in Quantitative Analysis for Business3