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INNOVATION, POLICY AND
ENTREPRENEURSHIP

> THRUSTS > IPE > MSc Program
MSC OVERVIEW
Program Name
  • Master of Science in Technology and Policy
Program Short Name
  • MSc (TP)
Mode of Study
  • Full-time
Normative Program Duration
  • 2 years
Tuition Fees
  • RMB 230,000 per program
Program Director
  • Prof Xun WU
Introduction

The 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 of 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.

This program nurtures young talents, equips students with knowledge and skills that stimulate policy solutions to meet the challenges posed by the development of innovation, and facilitates collaboration amongst Hong Kong, the Greater Bay Area (GBA), Greater China, and countries in Asia and elsewhere on entrepreneurship education, research and commercialization. In short, it matches well with the University’s mission to advance learning and knowledge through teaching and research.

Learning outcomes
On successful completion of the MSc program, graduates will be able to:
MSc(TP)
01
Identify key development and trends in technology sector;
02
Define and analyze complex problems in the development and application of disruptive technologies, and design original and innovative solutions for business and government;
03
Understand policymaking in technology development and innovation and contribute to policy development through the applications of evidence-based approaches; and
04
Communicate effectively with other professionals in a diverse range of task environment.
MSC PROGRAM SPECIFICS
Curriculum

Minimum Credit Requirement

48 credits
Core Courses: 21
Elective Courses: 27

* Subject to the approval of the Program Director, students may apply for credit transfer or course substitution of no more than 12 credits.

1
Core courses

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.

2
Elective Courses

To meet the elective course requirement and individual needs, students can take the courses in different areas, which may include but not limited to courses and areas listed below.

Additional coursework may be required as part of the program preparation.

IPEN 5111

Public Management and Institutional Analysis

3 Credit(s)

Description

This course focuses on the theoretical and analytical perspective of public management and institutions. It introduces students to key concepts in the discipline of public management and institutional analysis. The course begins with a review of the evolution of thinking in this field. In the following sessions, students will be extensively exposed to theoretical frameworks. The course aims to equip students with theories that help students in building up their capacity toward academic research.

IPEN 5120

Research Design for Innovation, Policy and Entrepreneurship Studies

3 Credit(s)

Description

The purposes of the course are to introduce to students key concepts in research design, and to help them develop skills in the design of empirical research for conducting innovation, policy and entrepreneurship studies. Specific emphasis will be on the use of quasi-experimental designs in policy research, as well as on their potentials and limitations.

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 5250

Text Analysis and Machine Learning

3 Credit(s)

Description

This course serves as an applied introduction to machine learning methods for text analysis. Several approaches on text data management and analysis will be covered in this course including basic natural language processing techniques, document representation, text categorization and clustering, document summarization, sentiment analysis, social network and social media analysis, probabilistic topic models and text visualization.

IPEN 5260

Corporate Governance Research

3 Credit(s)

Description

This course is designed to introduce students to the key corporate governance phenomena examined in the field of strategic management. We will review how economic and organizational theories are applied to explain the choices and outcomes of governance design. In addition, we will also investigate some behavioral or process-related factors affecting the functioning of governance mechanisms, especially the board of directors.

IPEN 5270

Corporate ESG Practice and Research

3 Credit(s)

Description

This course focuses on the development of the ESG (Environmental, Social and Governance) movement and its relationship with other long-standing concepts such as corporate social responsibility and sustainability. It aims to review the content, antecedences, and consequences of corporate ESG practices and stimulate new research ideas in related areas.

IPEN 5280

Technological Catching-up Policies and Management

3 Credit(s)

Description

This course deals with various issues when late-comer firms and countries may encounter in the technological catching-up with the more advanced firms and countries. Technological stages and paths are studied and highlighted at the three different levels of country, sector and firms. Students will learn various theories regarding technological catching-up and have opportunities to apply them to real cases.

IPEN 5290

Public Problems and Policy Design

3 Credit(s)

Description

This course is designed to provide learning opportunities regarding how to analyze and structure messy unstructured public problems. This course consists of a series of different fake public problem cases to help students experience setting up, analyzing, and designing policies. Over the course of the term, students analyze the cases quantitatively and qualitatively, and then propose solutions with an integrated manner of the analysis results. Much of the work is done in small groups or individually.

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 5310

Behavioral Economics and Public Policy

3 Credit(s)

Description

This course introduces behavioral economics - the incorporation of insights from psychology into economics - with an emphasis on its value for improving empirical predictions and policy decisions. Students will learn the major themes of behavioral economics and apply them to improve the design, implementation, and evaluation of public policies in a wide variety of domains.

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.

IPEN 5500

Science, Technology and Innovation Policy

3 Credit(s)

Description

The course introduces the conceptualizations of innovation policy and its instruments. It also develops evaluation methods to analyze the effects of these policy instruments and policy mixes. Cases of conceptual and empirical studies focus on the issues of innovation funding schemes and publicly funded science systems.

IPEN 5700

Strategic Management of Technology and Innovation

3 Credit(s)

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 5800

New Venture Creation

3 Credit(s)

Description

This is an introductory course to entrepreneurship research. Entrepreneurship is defined as the creation and growth of business ventures, either as new organizations or inside existing ones, and as transformation of existing organizations. This course covers fundamental readings and current research with an emphasis on business venture creation. The objective is to give enough training that students can follow and contribute to entrepreneurship research.

IPEN 5810

Data Science in Empirical Economics

3 Credit(s)

Description

In the digital age, there is more data available than ever before on human behavior: from analyzing an elected official’s opinion on Twitter to identifying a farmer’s crop choices through satellite images. This course aims to familiarize students in applied economics, public policy, and relevant disciplines with recent research that has used big data to push the cutting-edge of the applied economic and public policy fields. Through a combination of problem sets and independent projects, students will acquire the statistical and computational tools needed for making use of big data in empirical research.

IPEN 5820

Environmental Economics and Sustainable Development

3 Credit(s)

Description

This is a graduate-level interdisciplinary course focusing on the economics of environmental and sustainable development problems and the solutions to those problems. Students will learn to use tools from applied economics and relevant disciplines to better understand and evaluate a series of current policy questions, such as air and water pollution, climate change, environmental amenities, agricultural production, ecosystem services, and biodiversity.

IPEN 5900

Policy and Technology for Carbon Neutrality

3 Credit(s)

Description

All industries in China are actively taking effective actions to develop new and clean technologies in order to achieve the carbon peak and neutrality goal of shouldering the common destiny of human beings. This course examines the scientific, technological, and policy approaches that China and the rest of the world can take to achieve carbon peak and carbon neutrality.

AIAA 6011

Topics in Artificial Intelligence

1-4 Credit(s)

Description

Selected topics in Artificial Intelligence (AI) of current interest of current interest in emerging areas and not covered by existing courses. May be repeated for credit if different topics are covered. May be graded by letter or P/F for different offerings.

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)

Description

The course introduces students to different methods of collecting data in the social sciences for urban analysis, focusing on sampling surveys designs and analysis in urban settings. Since alternative data sources (e.g., passive measurement, social media and administrative data) become increasingly available in recent years, the course will also cover other modes of data acquisitions such as using new  technology on wearables, sensors, and apps in urban research settings, and exploration of cutting edge methods for collecting and analyzing web data, and how they can be used in combination with traditional survey data.

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.

MSC ADMISSION
Admission Requirements
1
General Admission Requirements of the University

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:

TOEFL-iBT: 80*

TOEFL-pBT: 550

TOEFL-Revised paper-delivered test: 60 (total scores for Reading, Listening and Writing sections)

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

their first language is English, or

they obtained the bachelor's degree (or equivalent) from an institution where the medium of instruction was English.

To qualify for admission, applicants must meet all of the following requirements. Admission is selective and meeting these minimum requirements does not guarantee admission.

Enquiry
MSc in Technology and Policy Program
Email: mstp@hkust-gz.edu.cn
Apply Now

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