Making extensive use of statistical tools, this programme teaches you how to convert large amounts of data into meaningful information by asking the right questions, choosing the right technology and implementing the right algorithms.

To complete the programme, you will need to attain a total of 30 credits of coursework, comprising of 5 core courses and 5 elective courses worth 3 credits each.

Programme schedule

  • Full-time programme (1 year)

    1st TRIMESTER | September-November
    2 core courses
    1-2 elective courses

    2nd TRIMESTER | December-March
    2 core courses
    1-2 elective courses

    3rd TRIMESTER | March-June
    1 core course
    1-2 elective courses

    1 elective course

Please note:

  • Core and elective courses are subject to change.
  • Courses are taught in English, except when Chinese is preferable.
  • Elective courses may be offered in the form of an intensive course every alternate year or in the summer term.

Course list

Core courses

  • Statistical Analysis

    This course is intended to equip students with the basic statistical tools required for the quantitative analysis of business problems. Topics include basic sampling and analysis of data, probability distribution functions, estimation of parameters and hypothesis testing, goodness-of-fit tests, analysis of variance, simple regression, correlation, and basic non- parametric statistical methods.
  • Decision Models and Applications

    This course covers the principal quantitative techniques used in solving financial, marketing, and operations problems in the private and public sectors. Topics include decision under uncertainty, Markov chain, linear programming, classical optimization, dynamic programming, and integer programming.
  • Data Mining for Managers

    This course emphasizes on the applications of data mining techniques in business problems from managerial perspectives. Business applications such as customer relationship management and financial analysis will be discussed throughout the course. Some basic data mining techniques will also be explained for illustration purposes. They include clustering, market basket analysis, data warehouse, and neural networks.
  • Risk and Operations Analytics

    This course introduces the analysis of key issues related to the design and management of operations using quantitative tools such as linear, integer, and non-linear programming, regression, and statistical analysis. It covers important topics such as forecasting, aggregate planning, inventory theory, transportation, production control and scheduling, and facility location, among others, and uses mathematical modeling, spreadsheet analysis, case studies, and simulations to deliver materials.
  • Business Intelligence Techniques and Applications

    This course emphasizes on the applications of business intelligence techniques in the era of big data. The techniques will include data preparation, dimensionality reduction, clustering, classification, market basket analysis, and performance evaluation. Business applications such as customer segmentation and financial analysis will be discussed throughout the course.

Elective courses

  • Managing Service Operations

    This course is designed for students to learn the latest theories, frameworks, concepts, techniques and to apply them in meeting the special challenges of managing service operations. The focus is to develop analytical thinking skills that will enable students contemplating careers in services to develop, evaluate and implement strategies for a wide range of service producing organizations. Topics will include: 1). the importance and economics of customer loyalty and approaches to build customer loyalty; 2). formulation and implementation of service strategies and the strategic service vision for greater business success; 3). management of the operational behavior of customers in service delivery; 4). design of sustainable service models that successfully incorporate a customer’s operating role; 5). analyses of customer data to inform managerial decision making; and 6). management of changes in service settings.
  • Economic Analytics

    This course is about applying economic models with data to deal with corporate decisions and strategies. The art and science of economic modeling for this purpose makes use of the principles and the tools derived from the studies of information economics, games, industrial organization and the related fields. The course is conducted in a variety of formats including lectures, cases studies and other activities. Operation and strategic issues covered in the course include setting prices, designing contracts, managing firm boundaries, controlling strategic information, analyzing entry and exit, and formulating competitive strategy.
  • Web Analytics and Intelligence

    The course focuses on the collection, analysis, and use of web data for business intelligence. Topics covered include web analytics concepts, web analytics technologies and techniques, visitor activity analysis methods, and web intelligence fundamentals. The course also evaluates practical, real-work analysis cases to demonstrate the strategic uses of web analytics for business intelligence and proper application of analytics techniques for online data optimization.
  • Business Process Analysis and Simulation

    This course introduces knowledge used to model and analyze business processes, with an emphasis on quantitative skills. A simulation package will be introduced and will be utilized to evaluate business process performance and to facilitate the decision making on business process improvement. The knowledge learnt from the course can equip students with scientific competence and help them solve practical problems in various settings related to business process management.
  • Database and Big Data Management

    This course focuses on both business data and contemporary big data modelling and management. We will examine the different natures of data and big data, selection and representation as well as use of suitable methods and tools for storing and accessing them. Topics such as data integrity, DBMS, data warehousing, NoSQL and MapReduce are covered.
  • Supply Chain and Logistics Management

    This course covers the concepts, insights, practical tools, and decision support systems for the effective management of the supply chain. This course will convey both the intuitions behind many key supply chain and logistics management concepts and to provide simple techniques that can be used to analyze various aspects of the supply chain and logistics management. The role of supply chain and logistics management in the age of eBusiness will be addressed. Through readings and case studies, we will identify the current and prospective supply chain practices.
  • Technology Field Study

    This course consists of preparatory assignments in readings and a written study plan, followed by a five-day residential program to Silicon Valley, California or similar Sciences Park in Asia. The course is concluded with a written report and an oral presentation. The goal in taking classroom sessions abroad is to expose our graduate students to a wide spectrum of issues related to IT business, which includes research and development, company culture, and ways to manage a technology company. The issues are highlighted through lectures or seminars by university professors, and visits to world class companies or Research and development Centres. Students will have chance to discuss with top executives from these companies. Through this course, students will be able to understand the strategies companies adopted, and solutions in tackling business issues.
  • Artificial Intelligence for Business

    This course provides a holistic overview of artificial intelligence (AI) models, as well as their mathematical and programming background. It covers, together with others, commonly used machine learning methods such as Bayes classifiers, decision tree, random forest, boosting, support vector machine, and neural network. This course emphasizes on understanding the intuition behind the methods and on applying them to business practices. It helps build hands-on experiences by using business data and R, an analytics software package. Previous coding knowledge will be needed for this course, and a decent familiarity with basic quantitative content (such as algebra and statistics) will also be required.
  • Fintech and Its Applications

    Advance in information technology has already started a revolution in the financial industry. Every part of the financial service system is affected, from daily payment to long-term investment, from efficiency gain to logic transformation. The pace of change is fast. Too fast to catch, yet too critical to miss. Institutions which can leverage the new technologies gain superior market insights, create new business models, and will become the future leaders of the financial service industry. Individuals who can understand and work with FinTech open new gates for investment and career development. This course introduces FinTech and examines opportunities and challenges. It seeks to equip students with both a deep and practical understanding of emerging technology in the FinTech arena as well as the necessary coding skills to tackle future demand. We are going to cover topics such as the fundamentals of the financial service system, technology disruption, data management and visualization, financial data mining (Python), quantitative trading and artificial intelligence, crowdfunding, blockchain technology, and RegTech. Applications on practical FinTech problems are emphasized with case discussions and programming assignments. While we will work with computer codes, this course does NOT assume any programming background from students.
  • Advanced Business Analytics Practicum

    This course bridges the gap between classroom and real world. It requires students to complete a real-world information systems project, in collaboration with a company or an organization. Students are required to form a project team, under the joint supervision of a teacher and an executive of the sponsoring company.

Other elective courses

You may take up to 6-credits-worth of courses from other programmes within CUHK Business School or from the Faculty of Engineering, subject to approval by Programme Directors. Courses may include Customer Relationship Management, Mergers and Acquisitions, Financial Analysis and Security Trading, and Organisational Marketing.

Course exemption

You may be exempt from taking a course if you have completed an equivalent course in your postgraduate studies and achieved a grade B or above. You may be exempt from a maximum of 6 credits, subject to approval. You would need to substitute these exempt credits with other elective courses.

Enhancing academic life

Overseas field Trip

The elective course, Technology Field Study is an overseas residential programme that brings students an inspirational and empowering experience through visits to renowned companies or research and development centres in person to explore their practice in data analytics.


There will be a half-day orientation before the programme begins. Students can make use of the opportunity to get to know the group and develop relevant teamwork skills.

Graduation requirements

Your performance is assessed based on exams and assignments, according to CUHK’s grading system.

To graduate, you must complete the prescribed coursework and achieve a cumulative grade-point average (GPA) of at least 2.50.

Students who obtain a cumulative GPA below 2.50 in the term assessment will be put on academic probation. If the probation is not lifted after two consecutive terms, the student will be required to discontinue his or her studies.

Students who receive a grade D+ or below in a course must repeat the course or take an approved substitute course.

Dean’s List

Students in the top 20 percentile who achieve a cumulative GPA of 3.60 or above are eligible to be on the Dean’s List. These students will be issued a Dean’s List Certificate and the honour will be recorded on their academic transcripts.

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