This course introduces key concepts, theories and models in the study of policy sciences. To resolve complex and multi-level public problems of a globalized world in ways that help to identify and enhance the common interest, policy sciences provide an integrated and interdisciplinary approach for making good policy decisions. This course also aims to help students understand the process and structure in designing policies. The concerns of this course are integrating theory and practice into actionable social knowledge and apply it to real world problems.
This course focuses on the important connection between data science and public policy by identifying and analyzing the policy challenges posed by data science technologies. It assesses what policy options and tools are available to address them and examines what new capacities governments and also citizens must develop in the era of data science. Students would also be introduced to the professional and ethical responsibilities and social, economic, political and legal impacts under the changes and innovations brought by data science.
This course is intended for students with little or no programming experience. It is designed to provide students with a firm foundation for learning modern data science, which uses computer programming to wrangle and analyze data. Adopting a hands-on approach, this course covers all the basics of programming in Python, as well as general computer programming concepts and techniques, such as branching, looping and inheritance. It also familiarizes students with some of the most useful Python libraries for data science, such as NumPy, pandas and matplotlib. This course plays a foundational role in the data science curriculum.
This course provides an introduction to data science and its applications in policy analysis. Students will learn the fundamental concepts of statistics and probability theory and their role in policy research. Through hands-on exercises and case studies, students will gain practical experience in descriptive statistics, probability distributions, statistical inference, and analysis. Students will also develop skills in communicating statistical results to policy audiences through effective writing and data visualization techniques. By the end of the course, students will have a solid understanding of statistical methods commonly used in policy analysis and be able to apply them to real-world problems in policy studies.
This course introduces major methods used in policy studies research, with both quantitative and qualitative methods as the foci. This course discusses major methodological issues, stages and process of research, the use of appropriate methods, strategies and tools with the objective of enabling students to adopt a multimethod and critical approach to conduct research of public policies and data science. Course contents also include the interpretation and communication of findings to different stakeholders, research ethics and limitations, and using research to generate impact.
This course introduces statistical concepts and applications that are heavily used in quantitative social science research and policy analysis. The course serves as a foundation in empirical research on social science issues and also as a prerequisite to taking more advanced data science courses (such as DSPS2201 and DSPS3202). This course will provide an overview of the empirical challenges in establishing causal arguments in social science and policy studies, and teach the statistical methods to address them. Major topics covered include simple and multiple regression, ordinary least squares method, dummy variables, heteroskedasticity, and basic time series analysis. Students will learn to apply the above techniques for estimating social science relationships, testing hypothesis, forecasting variables, and evaluating policies. They will also gain hands-on coding experience to analyze cross-sectional and time series data using data science software.
This course is designed for students with prior programming experience interested in learning C# programming and game development. The course covers fundamental programming concepts like object-oriented programming, control structures, collections, and error handling in C#. Students will also learn how to create games with Unity, including working with the Unity editor, physics engine, game assets, and game AI. The course begins with an introduction to C# programming language and control structures and gradually progresses to advanced programming concepts, such as inheritance and polymorphism. Additionally, students will learn how to handle errors and debug code. The second half of the course focuses on Unity game development. Students will learn to use the Unity editor to create game objects, scenes, and gameplay mechanics. They will also learn to work with some of Unity's built-in features, e.g., animations, particle effects, and game AI. By the end of the course, students will have the skills and knowledge necessary to write a C# program and build a fully functional and engaging game with Unity.
This course teaches students the major computing applications and coding languages, such as R and/or Python, of data analytics and how to apply them in public policy. The emphasis of the course is to apply data analytics to make theory-guided and data-driven policy decisions. With the technical competence provided by the course, students are expected to be both a skillful user of data analytics and an intelligent policymaker informed by data science.
This course covers the fundamental concepts of database systems and their role in data science projects. Students will gain practical experience working with relational databases, as well as learn how to design effective data models and optimize database performance. Topics covered may include SQL basics, entity-relationship modeling, normalization, indexing, and query optimization. This course is ideal for individuals with prior programming experience interested in learning about databases for data science.
This course integrates public policy analysis and design thinking to give students an integrated and comprehensive approach of resolving public problems. Design thinking, with its key features of being solution-based, user involvement, co-creation of knowledge, co-evolution of solution and problem, complements the economics-based and analytical model of public policy analysis well.
Together they strengthen students' ability in serving the dual role of a capable policy designer and an effective analyst who solve the policy problem and satisfy the diverse concerns of stakeholders.
This course introduces the theories and strategies of managing technology and policy innovation by analyzing the process and key factors. It shows how organizational, institutional and social contexts are interconnected in fostering policy creativity and promoting the diffusion and adoption of innovation. It lets students understand that policy innovation via technologies is a social and organizational process and sustainable policy innovation is only possible with recognition of the technological, institutional, and human dimensions of technological innovation.
This course is designed to provide students with the essential foundational concepts of modern linear algebra for understanding contemporary data science and machine learning techniques. Stressing on intuition from coding rather than traditional mathematical proofs, this course approaches linear algebra from a computational perspective, where linear algebra concepts are implemented in Python code with a focus on applications in data science and machine learning. This course is ideal for students with prior programming experience who are interested in understanding modern linear algebra and its applications in data science and machine learning from a computational perspective.
This course is designed to provide students with the essential foundational concepts of calculus for understanding contemporary machine learning and data science algorithms in which calculus is used as an optimization tool. It approaches calculus, both single-variable and multivariate, from a computational perspective, stressing on intuition from coding rather than traditional mathematical proofs. This course is ideal for students with prior programming experience who are interested in understanding the powerful optimization algorithms used in modern data science and machine learning.
This course provides a comprehensive understanding of Big Data computing systems and programming models. Students will gain hands-on experience working with mainstream open-source platforms for Big Data processing and analytics, including Hadoop and Spark. The course covers the architecture and components of Hadoop and Spark, data processing with Spark, and advanced topics such as Spark Streaming, graph processing, and machine learning. Students will learn to develop operational and programming tools for data collection, serialization, migration, and workflow coordination in Big Data pipelines. By the end of the course, students will be able to design and implement solutions to real-world Big Data problems using Hadoop or Spark. This course is ideal for students with prior programming and database experience interested in developing expertise in Big Data computing systems and programming models for processing and analyzing large-scale datasets.
This course introduces students to the machine learning concepts and techniques that are relevant to policy analytics. Major topics include linear regression, predictive modeling, classification, resampling methods, dimension reduction techniques, principal component analysis, smoothing splines. Additional topics such as decision trees, support vector machines and clustering methods may also be introduced. Students will learn how to employ these methods to conduct supervised and unsupervised learning for social and policy analytics, and gain hands-on experience with real-life data using data science software.
This course aims to provide students with knowledge, skills and applications of collaborative governance in a rapidly changing globalized world. Collaborative governance is broadly defined as the processes and structures of public policy decision making and management that engage people constructively across the boundaries of public, private and civic society for a public purpose that could not otherwise be accomplished. With the increasingly limited role of government in governance, the ability of policymakers to collaborate with non-state actors across levels and boundaries becomes critical for addressing policy challenges. The course focuses on how collaborative governance can build consensus and deliberative purposes of public policy and governance through studying its formats, process, rationales, concepts and outcomes as well as evaluation of its effectiveness and dilemma. The course also intends to nurture students to have a responsible and reflective role in society and to pursue a better governance.
The course focuses on the regulatory issues and concerns when the governments take data science as a paradigm to enhance the capacity of public policy. Advance in data science radically transforms the way data can be handled and utilized, and inevitably generates new regulatory concerns and debates due to the clash of competing values and interests. For example, while privacy and citizen rights advocates strive to decrease unexpected uses of data, many scientists and institutions may attempt to use data to design and evaluate public policies in order to maximize its effectiveness. This course discusses theoretical approaches to regulations from multidisciplinary and transdisciplinary approaches for the analysis of regulatory institutions, practices and ideas in data regulation as well as the dilemma between utilization of data and its protection, accessibility and transparency.
This course is designed to provide students with practical knowledge and skills in creating virtual and augmented reality (VR/AR) experiences for policy design and analysis. Students will learn about the capabilities and limitations of various VR/AR systems and their applications in policy design and analysis. They will also learn to create 3D models using appropriate software and techniques and use Unity as a development platform to create VR/AR applications. This course is ideal for students with prior programming experience interested in exploring VR/AR technology's potential for policy design and analysis.
This course introduces the fundamentals of systems modelling and simulation, which are increasingly popular tools for developing, implementing, and evaluating public policy. The course will expose students to diverse system modelling and simulation approaches and emphasize their applications in policymaking to equip them with the skills they will need to conduct technically-focused policy analysis. Teaching is conducted through class lectures and tutorials related to simulation software applications (e.g., Anylogic). This course is ideal for individuals with prior programming experience interested in exploring computer simulation’s potential for policy design and analysis.
This course studies how leadership and entrepreneurship are interrelated to create social and political impacts throughout the policy process, in particular, how leadership acts as an important factor to influence the social environment to nurture entrepreneurial activities. Leadership provides social source of influence and vision that one uses to inspire action taken by others, and to mobilize others to achieve a common goal. Entrepreneurship is the recognition of opportunities by coupling needs, wants, resources, and problems with innovative solutions and make changes to society. With a workshop format, the course provides students more interactive experience to equip them with the mindset, skills, and strategies of policy leadership and entrepreneurship. Policy leaders and entrepreneurs will be invited to serve as guest speakers in order to offer students the opportunities to learn from their practical experience.
This course is designed to introduce students to the concepts and methods of social network analysis (SNA), including network types, levels of analysis, data collection, descriptive methods and inferential methods, and their applications in public policy, such as policy agenda setting, policy network, advocacy coalition, policy learning and diffusion, and policy implementation. It also familiarizes students with network analysis software (e.g., Python and/or R). This course is ideal for students with basic knowledge of data analytics who are interested in understanding and using SNA in public policy studies.
This course is designed to introduce students to some common methods of Natural Language Processing (NLP), including text classification, topic modelling, text summarization, event extraction and text scaling, and their applications in public policy, such as data collection for evidence-based policymaking, interpretation of political decisions, policy communication, and investigation of policy effects. It also familiarizes students with some of the most useful Python libraries for NLP, for example, transformers, stanza and spaCy. Ethical issues are also discussed. This course is ideal for students with basic knowledge of machine learning who are interested in understanding and using NLP tools in data-driven policymaking.
The aim of this course is to provide students with practical work experiences in the application of both data science and policy studies learned in DSPS programme. Students will be given opportunities to observe and experience the management operations and to have hands-on experience in public sector, non-government organizations (NGOs), private sector etc., and to experience the work life in the sector, as well as to nurture students' practical skills. It is recommended that the internship to be conducted in summer holiday i.e. June to August, after finishing year two studies in the block mode (i.e. 5 days x 8 hours).
This course is designed to equip students with the skills to convey data-driven insights clearly and effectively. The course begins with an introduction to data visualization and perception principles, followed by an overview of visualization design principles, data preparation and exploration, and advanced visualization techniques. Students will also learn about popular visualization tools (e.g., Tableau), virtual and augmented reality technologies, ethics and bias in data visualization, data storytelling, and data visualization for big data. The course concludes with a final project, where students apply the concepts learned throughout the course to create a high-impact data visualization using advanced technologies. This course is ideal for individuals with prior programming experience interested in exploring advanced data visualization techniques and technologies.
This course introduces students with basic coding skills to modern Artificial Intelligence (AI) and its societal impacts. It focuses on the techniques of deep learning and reinforcement learning, the workhorse of modern data-centric AI technologies and their applications. Ethical challenges and policy implications of these powerful AI technologies and applications are critically discussed and their positive and negative impacts on achieving the United Nations’ Sustainable Development Goals (SDGs) critically evaluated. The main concern of the course is how to ensure that these disruptive modern AI technologies are used to do good, rather than bad, to society. An emphasis is put on the need for dedicated regulatory AI policies, both at the national and international levels.
This is a special-topics course on advanced applications of data science in decision making, visualization, research, analytics and presentation techniques to inform policy decision-making. Topics covered would depend on knowledge and expertise of the instructor, and the latest development and innovations in the field. Students will be equipped with the most advanced capacities and methods to conduct data analytics related to policy decisions.
Policy and program evaluation has become increasingly important in the face of complex and intractable public problems to ensure a systematic and evidence-based understanding of whether and how current interventions have achieved the desired results.
This course introduces the essential concepts, theories and methodological tools of policy and program evaluation. Through seminars and real-world examples, students will learn why, when and how to design and conduct rigorous evaluations in different policy contexts. Overall, this course aims to equip students with the knowledge and skills to critically assess evaluation studies and develop their own evaluation projects.
The graduation capstone project is about generating innovative ideas and implementing actionable knowledge to create impactful solutions. It offers students the opportunity to conduct a project on a selected topic under supervision related to data science and policy studies. While students are expected to integrate and apply what they have learned in the courses, internship, overseas exchanges and other learning opportunities, they are expected to address a real-world issue and to formulate feasible solutions and/or policy recommendations for the issue. Students are expected to meet their supervisor regularly, present their findings and exhibit their final product in any formats after developing a mutual consent between students and supervisor.
The graduation capstone project is about generating innovative ideas and implementing actionable knowledge to create impactful solutions. It offers students the opportunity to conduct a project on a selected topic under supervision related to data science and policy studies. While students are expected to integrate and apply what they have learned in the courses, internship, overseas exchanges and other learning opportunities, they are expected to address a real-world issue and to formulate feasible solutions and/or policy recommendations for the issue. Students are expected to meet their supervisor regularly, present their findings and exhibit their final product in any formats after developing a mutual consent between students and supervisor.
This course provides an in-depth analysis of important topics in data science and policy studies. The focus of the course may vary according to the area of expertise of the instructor and the interests of the class.
This course provides an in-depth analysis of important topics in data science and policy studies. The focus of the course may vary according to the area of expertise of the instructor and the interests of the class.