Students are required to take ten courses, of which Five are required courses and Five are elective courses.
The course is designed to provide students with basic knowledge in applying microeconomics concepts in managing firms in the business environment with recent digital innovations. It introduces essential economics concepts such as consumer theory, uncertainty, competition, strategic behaviors and information economics. Students will also learn specific applications of the theories in business environment and digital markets, including pricing strategies, managerial decisions, etc.
This course aims at introducing how businesses turn big data into values and the fundamental data science principles that govern the analytical framework. Well-known algorithms for solving common data-mining tasks such as classification, probability estimation, similarity matching and clustering will be covered. The data analytics tools and associated evaluation metrics will be applied to a variety of business applications.
The vast majority of data science roles are Python-based. This course aims at equipping students with Python programming techniques necessary to manipulate the data, perform feature selection and model optimization, analyze data using machine learning, and evaluate the outputs.
This course aims to introduce basic concepts in statistical reasoning used in data analysis. The objective of this course is to assist you in developing an understanding of the basic statistical techniques utilized in analyzing data. The course will provide you with the requisite skills to be both an effective producer of basic statistical analyses using statistical packages and a more critical consumer of statistical information in the ‘era of big data’. Another feature of this course is the introduction to computer programming as problem-solving tools in data analysis. Examples from data science will be used throughout the course for demonstration.
This course aims at providing students opportunities to gain business insights, data sense, inspirations, new developments in digital economy and changes in regulatory framework from business professionals, industry leaders and government officials. By means of seminars and workshops and/or company visits, students will be able to effectively master the latest development in Artificial Intelligence, data analytics and their business applications, most up-to-date techniques and solution to business problems.
This course aims to provide students an opportunity to gain real-life working experience related to the various business activities associated with an economic organization. Under the guidance of both faculty and workplace supervisors, students will work in an organization as interns and complete work assignments. The internship assignment is expected to take up no less than 120 hours to complete, and it may or may not be paid.
Artificial intelligence (AI) refers to the simulation of human brain function by machines, whereas machine learning (ML) is a subset of Al. ML techniques have been proven to drive significant changes for enterprises. This course aims to provide an applied overview to modern non-linear ML methods as supervised learning algorithms and unsupervised learning algorithms. Such ML learning algorithms are used to analyze practical data and make predictions about the future.
In the digital economy ensuring big tech companies do not exploit their market power and that the data of individuals receive adequate protection are critical to the functions of markets. Another important international technological development is financial technologies. This course aims enable students to think strategically about business decisions by analysing the legal, economics and business dimensions regarding competition, data protection and financial technology. Students also shall develop global perspectives about the laws regulating competition, data protection and financial technology by examining differences and similarities between Hong Kong and the European Union For businesses the mitigation of risks from the penalties and liabilities from breaching data protection laws, competition laws and financial technology regulations could not be more important. This necessitate the establishment of an effective compliance system. Therefore students shall be taught how to establish a robust compliance management program to minimize companies’ legal risks.
The project aims to provide students an opportunity to gain real-world experience related to the various applications of data analytics. This also provides an opportunity for students to apply the knowledge and skills gained in class and to prepare themselves for the transfer from the academic to the work situation.
This course provides an introduction to cloud computing for students in business analytics, including its architecture, models, and services. Students will gain hands-on experience with cloud computing platforms and tools, and learn how to design, deploy, and manage cloud-based data analytics solutions.
The goal of the course is to equip students with advanced econometrics techniques and help them to develop thinking about causal inference in the econometric sense. The emphasis is on mastering the properties and the applications of the econometric models introduced. Students completed this course should be able to conduct sophisticated empirical research.
Handling data is a critical task in the modern business world. This course aims to provide an overview of the ideas, basis, and the applications of DBMSs, which are specialized sets of software facilitating the process of data handling. The discussions start by introducing various data models and their applications. Then, the discussions will be extended to topics relating to relational data model, relational algebra, structured query language SQL and entity-relationship model. Finally, important issues of database control, e.g., security, backup and recovery of data, will also be covered to enrich students’ understanding about the application of database management system.
This course educates students in applied cost-benefit analysis of: (a) private investment, (b) public investment, (c) business strategy, and (d) government policy, with a primary focus of applying economic reasoning and writing/presentation skills to deliver practical information for decision making in a complicated business world.
This course introduces the frontier of economic studies in digital currencies. It covers a variety of topics, including (i) cryptography and blockchain, (ii) cryptocurrencies and smart contracts, (iii) tokens and platforms, and (iv) central bank digital currencies. Economic principles would be used to analyse regulations of digital currencies. It also examines how the role of central banks and monetary policies evolve with the development of central bank digital currencies.
This course analyzes the economics behind the digital economy. The course examines a variety of topics, including (i) pricing and demand for digital goods, (ii) two-sided markets and platform competition, (iii) network effects and standard, (iii) online reputation mechanisms, (iv) digital payment systems and virtual currencies, (v) digitization and innovation, and (vi) economics of artificial intelligence. Economic theories, especially those from information economics, would be used to analyze and explain phenomena observed in the digital economy.
Having too much information at our fingertips can make it harder to communicate. This course aims for anyone who needs to communicate important business ideas using data to others. The topics including data connection, integration, preparation, data exploration, data visualization, data analysis and data storytelling will be covered. Students will also learn a wide range of graph types from the most basic scatter, bar, line, and bubble plots to the advanced interactive plots for different reasoning using Tableau and deck to present their data stories.
Empirical studies in economics and business analysis is entering a new era of “Big Data”. A diverse range of new unstructured/unformatted data from texts (e.g. online discussion, social media post, and product description, etc.), maps (e.g. transportation network, satellite images, and digitalized map, etc.), and networks (e.g. tweet and retweet network, bilateral trade, and citation network, etc.) becomes increasingly accessible from web-scraping and other sources. How can we take advantage of these new data sources and improve our understanding of the economy and the business world? This course introduces various regression and machine learning techniques to process, simplify and analyse these spatial, textual and network data for business and economic analytics. Real life data analytic examples will be used to walk students through the intuitions behind those statistical techniques, as well as demonstrate step by step the programming language (either R or Python) applied for each data task.
The core of this course is to teach you how to ensure a successful data project under real business scenarios.
The course aims to offer both the intuition and basic vocabulary about time series as a step towards the statistical and algorithmic knowledge required to resolve the economics and business problems. It starts from the discussion of properties of time series data. Then, the discussion extends to various types of time series models and other related topics such as unit root and cointegration. Finally, this course also emphasizes hands-on experience in applying the techniques to real-world economics and business problems with the use of programming language Python.
The course covers the foundation of the UX design process, highlights the design of online experiments such as A/B testing, and its role in measuring the potential effect of various versions of a website or a mobile application. The course also demonstrates what is a data-driven UX approach and introduces related tools and UX design methodologies.
The course provides an introduction of a variety of predictive models useful for business operations and marketing analysis. It includes modern non-linear methods as supervised learning algorithms and unsupervised learning algorithms. The data pre-processing such as dimension reduction, statistics for model evaluation, predictions and interpretation will be covered. A diverse set of real-world examples will be used to demonstrate the applicability of those methods.
The course aims to introduce students with the basic features of the innovation and entrepreneurial development of Chinese economy, and the relationship between China and global innovation development. It would further offer insights into the determinants of entrepreneurial and innovation activities, and to provide students with toolbox of economics in evaluating economic incentives, business problems, industry sustainability, and economic policies in the context of innovation and entrepreneurship development. The path of innovation and entrepreneurial development in China would be compared to those in other advanced economies and newly industrialized economies. Real business cases would be covered to enable students’ understanding.
This course aims to provide students with an understanding of the foundational elements of a smart city. Digital transformation of companies is a critical part of a smart city. The course begins with an examination of the trend of digital transformation processes in some traditional industries. It will further discuss various enabling technology infrastructures and data analytical tools behind digital transformations and smart cities. Practical case studies in retail and supply chain will be used to illustrate the concepts, approaches and benefits.
* The elective courses offered will depend upon available resources and manpower while the class size is subject to quota available.