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Courses

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FINA6020 FinTech Analytics (2019-2021, CUHK)

DSME6682 FinTech Application (2019-2021, CUHK)

This course is designed to introduce students to the fundamental building blocks of various financial technologies and real-world applications through case studies and quantitative analyses. Students will learn the essential components of technology-driven financial applications, including the disruptive forces of digital payment, modern investment theory-driven robot investing, innovative financing and funding schemes (crowdfunding, peer-to-peer lending, etc.), and blockchain (applications such as tokenization). This course will also extend the concept of financial technologies to insurance (InsurTech), real estate (RETech), regulation (RegTech), and supply chain financing. Notably, we will discuss the complex challenges in the changing regulatory landscape.

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DSME6756 Business Intelligence Techniques and Applications (2019-2020, CUHK)

This course provides a broad introduction to business data analytics using statistical tools and machine learning techniques. The goal is to generate actionable business decisions and managerial insights. The topics of the class may include data preparation, data visualization, exploratory analysis, supervised learning (linear regression, logistic regression, neural networks, support vector machines, tree and random forest, etc.), unsupervised learning (clustering, dimensionality reduction, kernel methods), natural language processing, and economic network analytics. Python is used throughout the course. Course projects may implement various business analytics tools on consumer product data, financial data, and Internet text data for business analytics. Students will learn key data analytics techniques and machine learning packages to address existing business problems in today’s information-rich environments.

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MS6325 Operations Management (2016-2019, CityU)

Supply Chain Finance (2017, Tsinghua EMBA)

This core course focuses on understanding levers for structuring, managing, and improving a firm’s recurring business processes to achieve competitive advantage in customer responsiveness, price, quality, and variety of products and services. These levers are as applicable to banks, hospitals, and brokerages, for example, as to traditional manufacturing and logistic firms. The fundamental principles underlying state-of-the-art practices, such as Quick Response, Just-in-Time, Pooling and Risk are explored so that students learn to critically evaluate these and other operational improvement programs.

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FB8918 Machine Learning for Business Research

(2018-2019, CityU Ph.D. Elective)

This course provides a broad introduction to machine learning with applications using business research data. The topics of the class include supervised learning, unsupervised learning, and natural language processing. The course also includes guest lectures with a focus on cutting-edge machine learning research in the business domain. Python programming is used throughout the course. There are several course projects implementing various machine learning algorithms on consumer product data, financial time-series data, and web-scraped text data for business analysis. The goal is to equip research-degree students with practical hands-on exposure to utilize machine learning packages in their future careers.