21秋-香港浸会大学-数据分析和商业经济硕士
背景介绍
申请难点
留学规划与提升

Knowledge
Students will be able to identify the challenges of digital economy and apply economic principles to think strategically about business decisions.
Creativity
Students will be able to demonstrate ability in choosing appropriate algorithms and implementing programming languages for business analysis.
Skills
Students will be able to define business problem clearly and identify appropriate analytical tools to address the issues.
Communication
Students will be able to interpret the data outcomes and deliver the crucial findings and insights effectively through data visualization for business analysis.
Citizenship
Students will be able to formulate solutions to real-world problems with data analytics and/ or economic principles.
Admission Requirements
- A Bachelor degree from a recognised university, or equivalent, with satisfactory academic performance. The applicant is expected to possess an undergraduate degree either in business or in non-business fields with good quantitative training.
- Proof of English proficiency is required for applicants whose first degree were obtained from non-English-medium institutions. This can be fulfilled by obtaining a minimum TOEFL iBT score of 79; or a minimum of 6.5 in IELTS; OR other equivalent qualifications.
院校解读
留学方案
案例分析
Core Courses
ECON 7870 Managerial Economics and Business Strategy
The course is designed to provide students basic knowledge in applying economics concepts in managing firms in the business environment. It introduces essential economics concepts associated with the functioning of firms and markets. Through the understanding of the production costs, consumer demand, and market structures, students are able to analyse and formulate the supply decision of a firm. The course will also cover various managerial objectives and corporate behaviour in doing business. Students will learn about and understand the importance of competitive strategy and price strategy in business environment.
ECON 7880 Foundations in Big Data Analytics: Concepts and Techniques
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.
ECON 7890 Foundations in Big Data Analytics: Programming
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.
ECON 7900 Statistics for Data Science
This course aims at introducing 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.
ECON 7920 Executive Workshop Series
This course aims at providing students opportunities to gain business insights, 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 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.
Elective Courses
The elective courses offered will depend upon available resources and manpower while the class size is subject to quota available.
ECON 7025 Business Economics Internship
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.
ECON 7035 Artificial Intelligence for Business
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.
ECON 7045 Law and Economics of Data Protection
The use and protection of data is a cornerstone of the digital economy. This course aims enable students to think strategically about business decisions by analysing the legal, economics and business dimensions of data protection. The economic dimensions concern the trade offs arising from protection of personal data as well as broader economics themes like competition and trade barriers. The legal considerations center upon laws protecting the privacy of individuals and rights of access as well as use in Hong Kong and abroad. For businesses the mitigation of risks from the penalties and liabilities from breaching privacy and data protection laws necessitates the establishment of an effective compliance system.
ECON 7055 Projects for Data Analytics
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.
ECON 7430 Applied Cost-Benefit Analysis
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.
ECON 7850 Economics of Digital Markets
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 paymentsystems 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.
ECON 7910 Data Visualization with Story-telling
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 smoothed, animated, 3D, and interactive plots for different reasoning using either Tableau to present their data stories.
ECON 7930 Analytics for Spatial, Textual and Social Network Data
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.
ECON 7940 Data-driven Decision Making
How can business managers formulate, implement and monitor project activities subject to time and budget constraints? This course provides an understanding of key issues and illustrate several data-driven strategies in business project management and dynamic scheduling. The methodologies spanning from setting project schedules, analyzing the potential risks and their impact on the schedules, measuring and evaluating the performance are unveiled. This course goes through the processes from data collection to translation of data into business insights for improved decision-making and performance.
ECON 7950 Business and Economic Forecasting with Big Data
The course aims at deepening students’ understanding about statistics and data analytics who can discuss the various risk metrics, particularly those involving extreme events. It offers both the intuition and basic vocabulary as a step towards the financial, statistical, and algorithmic knowledge required to resolve the industry problems, and it depicts a systematic way of developing analytical programs for finance in the statistical language R. Build a hands-on laboratory and run many simulations. Explore the analytical fringes of investments and risk management. Bennett and Hugen help profit-seeking investors and data science students sharpen their skills in many areas, including time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.
ECON 7960 User Experience and A/B Testing
The course covers the design of online experiments and analysis of A/B testing, and its role in measuring potential effect of various versions of a website or mobile application. The course also demonstrates the processes of implementing whether a new launch of product or website brings a substantial change through counterfactual analysis. The processes span from designing experiments with enough statistical power, characterizing various metrics for evaluation, analyzing the results and draw business insights, and ensuring the participants of the experiments are adequately protected.
ECON 7970 Applied Predictive Modeling
The course provides an introduction of a variety of predictive models useful for business operations and marketing analysis. It includes count data models, association rules and structural equations modeling. 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.
ECON 7980 Innovation and Entrepreneurial Economy: China and Global Development
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.
ECON 7990 Economics of Smart Cities
This course aims to provide students with an understanding of the foundational elements of a smart city. It begins with an examination of the trend of urbanization and various challenges of urban development today. It will further discuss how the concept of smart city and its applications can address those challenges. Meanwhile, the course will also build awareness of potential concerns in developing smart city applications including data privacy and security issues. Students should get familiar with various enabling technology infrastructures and data analytical tools behind smart cities. Cases in China as well as around the world will be used to illustrate the approaches, benefits and risks involved.