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 introductory course provides an overview on the study of data science, covering the development of data industry and challenges in working with big data. Topics to be covered include the definition of data science, data analytics, data visualization, data science process, development of data industry, data science coding, and the wide applications of data science in social science and policy studies. This course also equips students with the essential quantitative skills and knowledge to prepare them for the advanced data science courses (such as DSPS2102 and DSPS2201). Topics covered include data types, data presentation, data transformations, frequency analysis, descriptive statistics, probability theories, confidence intervals, hypothesis testing, correlation, as well as their applications in social science and management problems.

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 teaches students on advanced techniques to conduct prescriptive and predictive analytics using social science and policy data, in the form of cross-sectional, pooled cross-sectional, time series or panel data. Major topics to be covered include generalized least squares, autoregressive models, ARCH models, panel data methods, instrumental variables methods, simultaneous equations modeling, limited dependent variables methods and advance time series techniques. Students will learn to apply the above techniques to estimate relevant social science parameters, predict socioeconomic outcomes, and test relevant hypotheses. Together with hands-on experience with data science software, they will develop a working knowledge of applied regression techniques which are essential for making data-driven policy decisions. The applied regression techniques will also prepare students for taking the more advanced data science courses on statistical/machine learning (such as DSPS3202).

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.