POSTGRADUATE
PROGRAMS
PROGRAMS
Postgraduate Programs 2024/25
Master of Science Program in Technology and Policy
Award Title
Master of Science in Technology and Policy
Program Short Name
MSc(TP)
Mode of Study
Full-time
Normative Program Duration
Full-time: 2 years
Program Fee
RMB 230,000
Offering Unit
Innovation, Policy and Entrepreneurship Thrust Area
Society Hub
Program Advisor
Program Director:
Prof Xun WU, Professor of Innovation, Policy and Entrepreneurship
Enquiry
Master of Science in Technology and Policy (MSc (TP)) program will provide professional training to cope with the rapid development in technology-related sectors. In many areas, public policies play a fundamental role in shaping the development, application, and market. We seek to train students and equip them with cutting-edge knowledge in technology sectors and bridge the divide between industries and government. The rigorous two-year program will provide skill-based training in the uses of analytic methods for public policy and the development of expertise in best policy practices, comparable to MSc (TP) degrees offered by leading universities in Europe, the United States. This will be the first program in Greater China that offers a Master's program focusing on technology and policy. Students will acquire the knowledge, skills, and confidence to be leaders in government, business, and NGOs to shape public policies of technology, to guide and nurture technologies that change our society.
On successful completion of the program, graduates will be able to:
- Identify key development and trends in technology sector;
- Define and analyze complex problems in the development and application of disruptive technologies, and design original and innovative solutions for business and government;
- Understand policymaking in technology development and innovation and contribute to policy development through the applications of evidence-based approaches; and
- Communicate effectively with other professionals in a diverse range of task environment.
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Minimum Credit Requirement
48 credits
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Credit Transfer
Subject to the approval of the Program Director, students may apply for credit transfer or course substitution of no more than 12 credits.
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Core Courses
21 credits
IPEN 5110
Foundation in Public Policy
3 credits
Description
The course will provide an advanced foundation in the study and practice of public policy at the level required for graduate study. The course will cover both the historic foundations of policy studies, as well as emerging approaches and directions. As the study of public policy is inherently interdisciplinary, it will include perspectives from political science, public policy, economics, business and other aspects of social science. It will take a broad view of public policy, including taking up some of the core literature on public management and public administration.
IPEN 5130
Economics of Technology Innovation and Entrepreneurship
3 credits
Description
This course introduces the economics of technology innovation and entrepreneurship through the combined perspectives of microeconomics and macroeconomics. It covers microeconomic core modules concerning consumers, firms, markets, and governments, as well as macroeconomic core modules on economic growth associated with entrepreneurship and innovation.
IPEN 5150
Policy Analysis for Technology and Innovation
3 credits
Description
Technological innovation is increasingly the source of sustainable competitive advantage for firms worldwide. This course introduces a grounding in the field of technology and innovation, with an emphasis on economic policy and business strategy. The course will be highly interactive and apply multiple disciplines including economics, management, law and public policy.
IPEN 5160
Big Data Applications for Business and Government
3 credits
Description
This course will cover the key concepts and technologies of big data and data analysis, with a focus on the application of big data in formulating business strategies and policies, and related research issues on how big data affects the direction of business and policy development. The course will provide students with practical training on big data and data analysis based on real-world business or policy issues, ranging from collecting and preprocessing to organizing and analyzing large-scale data.
IPEN 5180
Disruptive Technology and Society
3 credits
Description
This course gives students a broad introduction to the key disruptive technologies, such as mobile internet, AI, and robotics, that have transformed our society. We will examine the practical applications of these technologies and discuss their socioeconomic impacts and policy responses. We will also look at the potential for businesses and governments to harness these disruptive technologies to deliver new services or improve existing ones and enhance value in public and private sectors.
IPEN 6110
Capstone Project
6 credits
Description
This course consists of 6 credits and will last for two regular terms. In the first term, students learn and integrate the latest technology topics through seminars,lectures, and workshops. After completing four micro-policy analysis reports, students can familiarize themselves with technology policy hot spots and policy analysis tools, as well as the cooperation skills and role division among the groups. In the second term, they will complete group projects on selected topics for science and technology policy under the supervision offaculty members. The participation of the university’s internal community and external organizations in these projects will be highly encouraged. The university will be responsible for the control, management and evaluation of the project. Students will exercise their teamwork skills, analyze science and technology policy issues and develop concise reports of their findings and recommendations. They should write the paper acting as an assistant to a particular decision-maker in a government, nonprofit organization, business or private sector. This course is for MSc(TP) students only. May be graded PP.
- Elective Courses
27 Credits
Students can take the following courses to meet the elective course requirement
• Any postgraduate elective courses offered by IPE
• A list of other elective courses offered at HKUST(GZ) in a particular year will be announced at the beginning of each intake.
Additional coursework may be required as part of the program preparation.
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Remarks
IPEN 5200
Uncertainty, Information and Decision Making
3 Credit(s)
Description
This course introduces the economic theories of decision making under risk and uncertainty and how agents with heterogeneous information interact strategically. Sample topics include expected and non-expected utility theories, models of strategic communication, and information design. Students will apply the theoretical tools to understand and improve real world institutions, such as employee feedback systems and transparency in organizations.
IPEN 5300
Experimental Economics and Organizational Behavior
3 Credit(s)
Description
This course introduces the methodology of experimental economics and related behavioral theories, with an emphasis on social-psychological elements of preference and organizational design. Experiments studied will include ones based on the prisoners’ dilemma, dictator game, ultimatum game, and especially the public goods game and the trust game, along with more complex designs for studying institutional and organizational problems such as creation of centralized punishment schemes and secure property.
IPEN 5400
Climate Change: Science and Governance
3 Credit(s)
Description
This course prepares students to acquire the basic knowledge of climate change, which sits on the intersection of science and governance. It will review some of the scientific facts of climate change and contrast the scientific research findings with climate governance status. Case study on transforming to a low carbon society will be conducted in later part of the course. Aspects to consider include both scientific support and governance complexity of the low carbon city idea. Students are expected to build their own analysis of the climate change issue at the end of the course.
AIAA 6011
Topics in Artificial Intelligence
1-4 Credit(s)
AIAA 6021
Topics in Machine Learning
3 Credit(s)
Description
Covers emerging topics of machine learning. Potential topics include machine learning and cognitive science, transfer learning, multi-task learning, active learning, lifelong learning, assemble learning, and advances in deep learning. Graded P or F.
DSAA 5009
Deep Learning in Data Science
3 Credit(s)
Description
In this course, theories, models, algorithms of deep learning and their application to data science will be introduced. The basics of machine learning will be reviewed at first, then some classical deep learning models will be discussed, including AlexNet, LeNet, CNN, RNN, LSTM, and Bert. In addition, some advanced deep learning techniques will also be studied, such as reinforcement learning, transfer learning and graph neural networks. Finally, end-to-end solutions to apply these techniques in data science applications will be discussed, including data preparation, data enhancement, data sampling and optimizing training and inference processes.
DSAA 5020
Foundation of Data Science and Analytics
3 Credit(s)
Description
This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.
DSAA 5022
Data Analysis and Privacy Protection in Blockchain
3 Credit(s)
Description
This course introduces basic concepts and technologies of blockchain, such as the hash function and digital signature, as well as data analysis and privacy protection over blockchain applications. The students will learn the consensus protocols and algorithms, the incentives and politics of the block chain community, the mechanics of Bitcoin and Bitcoin mining, data analysis techniques over blockchain and user/transaction privacy protection.
FTEC 5050
Machine Learning and Artificial Intelligence
3 Credit(s)
Description
This course covers the fundamentals of machine learning and artificial intelligence, and their applications in computer vision, image processing, natural language processing, and robotics. The topics include major learning paradigms (supervised learning, unsupervised learning and reinforcement learning), learning models (such as neural networks, Bayesian classification, clustering, kernels, feature extraction), and other problem solving techniques (such as heuristic search, constraint satisfaction solvers and knowledge-based systems) in AI.
UGOD 5020
Quantitative Social Science
3 Credit(s)
Description
This course builds on the knowledge of the linear regression models to introduce students advanced statistical methods to analyze survey, administrative and other types of data of interest to quantitative social scientists. The introduction of statistical methods is integrated into research contexts and designs from a holistic framework and bridge quantitative social science and computational social science (data science). Topics include measurement, prediction, causal inference, natural experiment and program evaluation (difference-in-differences, panel data, instrumental variables, regression discontinuity), applied to both survey and big data.
UGOD 5040
Urban Data Acquisition and Analysis
3 Credit(s)
UGOD 5050
Cities and Society
3 Credit(s)
Description
The course looks at some of the major drivers of urban inequality and poverty, and the key actions that cities are taking to reduce urban inequalities through urban design, infrastructure and policy. Students are introduced with tools to analyze the socio-demographic profile of households and neighborhoods/communities and their relation to spatial distribution and clustering in cities of both the developing and the developed world. A particular emphasis is placed on identifying spatial strategies that can alleviate the concentration of urban poverty and inequality to enhance urban social cohesion by optimizing access to jobs, housing, education, health, public space, transport and community infrastructure.
To qualify for admission, applicants must meet all of the following requirements. Admission is selective and meeting these minimum requirements does not guarantee admission.
1. General Admission Requirements of the University
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Applicants seeking admission to a master's degree program should have obtained a bachelor’s degree from a recognized institution, or an approved equivalent qualification.
2. English Language Admission Requirements
Applicants have to fulfill English Language requirements with one of the following proficiency attainments:
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TOEFL-iBT: 80*
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TOEFL-pBT: 550
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TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections)
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IELTS (Academic Module): Overall score: 6.5 and All sub-score: 5.5
* refers to the total score in one single attempt
Applicants are not required to present TOEFL or IELTS score if
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their first language is English, or
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they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English.
Apply online before the application deadlines.
Application Fee
RMB 250
Application Deadlines
For 2024/25 Fall Term Intake (commencing in Sep 2024):
International students*
15 Jun 2024
Chinese students
15 Jul 2024
* All international students are required to obtain a student visa (X visa) for studying in China’s mainland. For details on student visa (X visa) requirements, please click here.
