Attend an 18-hour compulsory summer boot camp
Before the commencement of the programme, all candidates must attend a mandatory 18-hour social data analytics boot camp. The boot camp training programme is designed to provide candidates with beginner-level skills in social data analytics so that they can follow the more advanced curriculum for the degree of Master of Social Sciences in the field of Social Data Analytics. The boot camp will be taught over three to four days, organised around the following topics:
Students are required to complete 5 compulsory courses, 3 elective courses and 1 capstone project throughout their studies.
Before the commencement of the programme, all students must attend a mandatory 18-hour social data analytics summer boot camp. The boot camp training programme is designed to provide students with beginner-level skills in social data analytics so that they can follow the more advanced curriculum for the MSocSc(SDA) programme.
Compulsory Courses
MSDA7001 Introduction to social data analytics
MSDA7002 Statistical foundations
MSDA7003 Machine learning
MSDA7004 Research design and inference in the social sciences
MSDA7005 Programming for social scientists
Elective Courses
At least two elective courses from the following list*
MSDA7101 Big data solutions to social problems
MSDA7102 Simulating human behaviours with agent-based models
MSDA7103 Text as data: Natural language processing and social research
MSDA7104 Social network analysis
MSDA7105 Media data analysis
One additional elective course can also be taken from other programmes*
GEOG7308 Machine learning for geospatial data
GEOG7310 Cloud computing for geospatial data analytics
GEOG7311 Web GIS
MSBH7005 Scientific inquiry and research methods in behavioral health
SOCI7006 Research methods in media, culture and creative cities
SOWK6185 Qualitative research methods
Capstone project (Compulsory)
MSDA8001 Capstone project
Please click here for the regulations, syllabus and course descriptions.
*Offering schedule and quota is subject to availability
Students are required to complete 5 compulsory courses, 3 elective courses and 1 capstone project throughout their studies.
Compulsory Courses
Demographic theories have produced some of the best-documented generalisations in social sciences. This module offers a comprehensive exploration of demographic theories, building upon the foundational concepts of cohort versus period analysis. The course will first focus on the predominant demographic theory of demographic transition, as well as the associated explanation of the outcome of demographic change such as demographic dividend and metabolism. We will then extend our focus to encompass theories on the transition in changes in fertility, mortality and migration that have played key roles in the demographic transition. We will cover how these processes of population change have evolved by core demographic characteristics such as age, sex, education, and location. Finally, we will discuss various approaches to forecasting future population sizes and structures, complemented by illustrative scenarios at regional, national, and global levels, and discuss their possibilities and limitations in projection future population levels and structures in the short, medium, and long range. Through a series of lectures, discussions, and practical applications, participants will gain insights into the power of demographic theories and examine their implications for past and future population and development.
Credit: 6
Assessment: 50% coursework; 50% examination
Demography as a scientific discipline began over 350 years ago when statistical patterns for births and deaths were first systematically analysed. The extensive development and widespread application of technical methods have been at the core of demography ever since. This module introduces key demographic concepts and methods, to allow students to define, interpret, and analyse population change. Students will engage with key concepts such as the stable population and demographic accounting, laying the groundwork for a nuanced understanding of population dynamics. Through a combination of theoretical understanding and practical skills development, participants will be empowered to critically interpret demographic measures, engage in population analysis, and contribute to demographic research. Throughout the course, we will use the R statistical software to carry out calculations and visualise our results.
The course will begin by discussing common types of demographic data, such as censuses, surveys, and administrative sources. Emphasising hands-on experience and practical application, the course covers techniques for measuring and comparing changes in population size, composition, and growth. We then focus on a range of topics on demographic rates and measures, direct and indirect standardisations, the Lexis diagram, life tables and decomposition analysis, before concentrating on methods for analysing the components of population change (fertility, mortality, and migration). Finally, we will turn our attention to the fundamental concepts of cohort component population projections including Leslie matrices to model future age structures in population growth, and methods for forecasting population sizes with uncertainty and developing scenario-based projections often used by policymakers in studying the potential impact of demographic changes in the future.
Credit: 6
Pre-requisite: MPOP7005 Statistical computing for social science
Assessment: 100% coursework
Policymakers have tried to influence the size and structure of their populations for centuries. This course explores the dynamic field of population policies, focusing on their impact on demography, societal structures and economic development, and addressing challenges such as rapid population growth, ageing societies, low fertility, depopulation, economic migration, forced migration, climate change, and sustainable development. Emphasising historical and contemporary approaches, the course analyses case studies worldwide. Through interactive discussions, policy simulations, and critical analyses, students gain insights into the complexities of designing effective population policies amidst diverse demographic and socio-economic challenges.
Credit: 6
Assessment: 100% coursework
This module offers a deep dive into the intricate world of evaluating population policies, employing cutting-edge quantitative causal inference methods with both experimental and non-experimental data. Participants will grapple with fundamental questions: How can we rigorously assess the impact of policies or events? How do we discern causal relationships within observational data? The course extensively employs real-world examples to elucidate various statistical methods for causal inference, encompassing Rubin’s Causal Model, regression fundamentals, standard error calculations, analysing naturally occurring “quasi-experiments,”, instrumental variable estimation, difference-in-differences estimation, and regression discontinuity design. Participants will gain hands-on experience, mastering these techniques to conduct robust and insightful evaluations of population policies and programs. Throughout the course we will use the R statistical software to carry out calculations and visualise our results.
Credit: 6
Pre-requisite: MPOP7005. Statistical computing for social science
Assessment: 100% coursework
The volume of data produced by society continues to grow at an exponential pace. Tools that harness the increasing amounts of data are providing new insights into ongoing changes in our societies. This course introduces statistical data analysis using R; a popular open-source statistical programming language used for data mining, modelling, and visualisation. The course begins with an introduction to the R and RStudio working environments, providing an outline of common core functions for data analysis and getting help using R. Next, common data structures, variables, and data types will be demonstrated. Students will then learn how to write code scripts to utilise the popular tidyverse set of R packages for data manipulation and visualisation. Finally, we will learn how to carry out a range of statistical regression analyses in R, to summarise relationships, account for hierarchies in data sets and effectively assess and communicate our model results.
Credit: 6
Assessment: 100% coursework
Elective Courses*
At least two elective courses from the following list:
In recent years, the number of children in the world began to decline for the first time in centuries. Combined with increasing life expectancies, the size and structures of families are undergoing unprecedented changes. This course delves into the intricate dynamics of fertility and family structures, introducing fundamental concepts and measures essential for a comprehensive demographic study. We explore key theories and empirical observations to illuminate fertility changes and family formation patterns by key demographic variables such as gender, age and education. Topics covered include reproductive health, family planning, changing family patterns, intergenerational relationships, and the impact of demographic and socio-economic factors on fertility decisions. We will pay particular attention to the cause and impact of fertility changes in the East Asian context, where levels are among the lowest in the world. Through a blend of theoretical discussions, empirical research, and case studies, students will gain a nuanced understanding of the complex relationships between fertility choices, family dynamics, and broader societal trends. The course also equips students with the ability to analyse fertility and family differences across countries and regions, considering various stages of the demographic transition.
Credit: 6
Assessment: 50% coursework; 50% examination
As our global society ages, understanding how mortality is declining is becoming ever more important in developing policies to better address the needs of older populations. This course provides a comprehensive exploration of mortality patterns, health dynamics, and ageing, focusing on their interdependence across time and space. Students will delve into the micro-level drivers and macro-level consequences of the mortality transition, understanding its linkage to global health and population ageing. The curriculum considers the intricate interaction of demographic and socio-economic processes shaping mortality trends. Participants will explore various measures for health and ageing and analyse relevant data sources to gain practical insights. We will study past and emerging trends in public health, such as chronic diseases, obesity, environmental health and infectious diseases, from a demographic perspective. The course will conclude with critical reflections on strategies to overcome challenges in public health and demographic ageing, addressing issues in both low- and high-income countries, including in the East Asian context, home to some of the eldest populations in the world.
Credit: 6
Assessment: 50% coursework; 50% examination
Migration is becoming the prominent driver of population change in many regions and countries, as fertility and mortality rates decline to low levels. This course provides an in-depth exploration of migration and urbanisation, offering a holistic perspective on these dynamic phenomena. Participants will gain a comprehensive understanding of migration concepts, migration and urbanisation theories, and the main past and current migration trends globally and in Asia. The course demonstrates how and where migration is becoming a pivotal force in demographic change, impacting population size, distribution, and characteristics, as well as the socio-economic characteristics of both origin and destination. Moreover, we will delve into the complexities of measuring and defining migration and urban areas, highlighting how these factors can make cross-country comparisons more challenging than other demographic processes such as fertility and mortality. Finally, we will discuss the evolution of migration policies, their effectiveness, and their impact on development in both the sending and receiving areas. The course will facilitate learning about key migration measures and data sources, fostering the ability to comprehend common myths on the scale of migration and its impact.
Credit: 6
Assessment: 50% coursework; 50% examination
To understand changes occurring in populations and societies, one needs to examine the space in which people are interacting. This course examines local populations and their relation to demographic factors by exploring the theory, tools and analytical frameworks for dealing with spatial population and public health data. We will investigate some key spatial issues for population analyses, including the Modifiable Area Unit Problem, whereby the variation in administrative areas can greatly influence our analysis outcomes. Throughout the course, students will learn the theoretical foundations, tools, and analytical frameworks essential for handling spatial data using packages in the R statistical software. We will explore how to visualise spatial data, and how to create “good” maps that are easy to interpret spatial data without bias. Further, we will examine common sources of geospatial demographic data, as well as methods for managing, mapping and modelling spatial data in R using the sf, terra and spgwr packages. These R package environments implement many functions of Geographic Information Systems (GIS) including methods for describing, analysing, and modelling spatial data, including geographically weighted regression.
Credit: 6
Pre-requisite: MPOP7005. Statistical computing for social science
Assessment: 100% coursework
This module provides a focused exploration of the demographic landscape in Greater China, encompassing mainland China, Taiwan, Hong Kong, and Macau. Participants will delve into the unique demographic patterns, trends, and challenges shaping this dynamic region. The course begins with an exploration of past and current key demographic indicators, including fertility rates, mortality trends, and migration patterns, providing a comprehensive overview of population dynamics. Participants will examine the impact of historical political and cultural factors on demographic changes and explore the implications for social and economic development in Greater China. We will also reflect on expected future challenges for society and policymakers, including low fertility rates, changing household structures, ageing populations and population decline. Through case studies, discussions, and practical applications, participants will gain a nuanced understanding of the complex interplay between current demographic shifts and the emerging challenges in Greater China.
Credit: 6
Assessment: 100% coursework
The course is designed to examine the concepts of social policy and ageing, and the various models available for the analysis of social policy. By analysing local and foreign services and policy regarding the elderly people, students will become familiar with the roles of government and non-government organisations in implementing public policies. This should further the understanding of the development of social services to meet the needs of the elderly in the context of economic and social change. Basic concepts of social planning, problem identification and programme implementation will be examined.
Credit: 6
Assessment: 100% coursework
Not more than one elective course from the following list:
Do Google and Facebook understand us better than we do ourselves? Are we becoming lab rats every time we go online? Is the impartially designed algorithm for predicting the probability of recidivism truly fair for sentencing individuals? What are the ethical issues underpinning big data science? When big data analytics are routinely applied in our daily lives, the ability to audit the adopted algorithms becomes crucial. This course aims to build students’ big data literacy through three major areas of focus: (1) Defining what big data is; (2) Providing an overview of existing big data analytical techniques; and (3) Discussing opportunities and challenges of big data analytics in tackling social problems.
The course will focus on elaborating the core principles of a variety of techniques adopted when predicting future phenomena through the lens of big data. We will use a case study approach to provide an in-depth understanding of various big data analytics, with the goal inspiring the students to think creatively and critically about how big data analytics can be used to making scientific discoveries and do social good.
Credit: 6
Assessment: 100% coursework
Despite its contributions to scientific development, traditional positivist, quantitative approaches (e.g., traditional variable-based statistical equations) have often been criticised for their over-simplification and decontextualisation of real-world phenomena in analysis. In contrast, systems science aims to understand complex relationships and their adaptive interactions among various elements within varying environments and systems. Systems science has been instrumental in breaking new scientific ground in diverse fields, including but not limited to engineering, decision analysis, transportation, public health, and urban sciences.
This course will pursue a solid understanding of systems science by exploring the latest advances in agent-based modelling (ABM) and the related analysis methods. ABM, a class of systems science, is an in-silico modelling to examine and predict ‘what-if’ conditions by simulating social behaviours and interactions among individual entities embedded in social structures.
This course is designed to introduce students to basic tools of theory building and data analysis in ABM to apply those tools to better understand social problems in human populations. Students will learn to use agent-based modelling on standard (free) software, paying attention to feedback processes, multilevel interactions, and the phenomenon of emergence. You will enrich your understanding of the problems people have when they share and cooperate, and examine essential models that can support you in your future career in social sciences and beyond.
This course is designed for anyone interested in understanding human behaviours, especially when sharing and cooperation are involved. It will be particularly useful for professionals dealing with challenges related to public goods, common resources, and cooperation. If you are studying social sciences and are curious about how a computational approach works, this course will be particularly helpful.
Credit: 6
Assessment: 100% coursework
This course introduces students to the key concepts and theoretical approaches in the study of public policy process. The course is organised into three parts. Part one examines the basic concepts used in analysing the policy process and the political and institutional contexts of policy making. Part two discusses the major theoretical approaches to the study of policy making and policy implementation and assesses their strengths and limitations. Part three analyses the politics of policy making in Hong Kong and discusses the applicability of the concepts and theories in public policy studies to the real world. Selected policy issues will also be examined to illustrate the dynamics of the policy process in Hong Kong and other areas.
Credit: 6
Assessment: 60% coursework, 40% examination
This course focuses on conceptual and analytical skills and techniques required for understanding, and suggesting solutions to, policy problems. It examines major components of public policy analysis – problem definition, policy design, and policy assessment.
Credit: 6
Assessment: 100% coursework
This course provides a comprehensive, holistic view of ageing that considers the implications for an older person’s interactions with their social and physical environments, including the immediate environment of family, friends, and home, as well as the larger social structure of community, organisations, and society. It also aims to impart knowledge to students about the most important social theories on ageing and the time dimension in the ageing process and its relation to the evolution of larger society.
Credit: 6
Assessment: 100% coursework
This course explores the types of mental illnesses among the elderly in Hong Kong. Attention will be put towards the understanding of the causes and treatments of mental illness in the elderly population. A critical review of medical, psychological and social services for the elderly with mental illness will be conducted.
Credit: 6
Assessment: 100% coursework
Death is an inevitable life experience for everyone. Death-related problem is one of the commonest issues that clients brought to counseling, but is also rated as the most uncomfortable scenario by beginning counselors. This course offers a basic orientation to the knowledge and intervention approaches in working with death-related situations, including end of life care and bereavement counseling. Major theories and models related to death, dying and bereavement would be introduced. Corresponding clinical implications and practical work approaches would also be highlighted. Apart from the knowledge and skills, the course also emphasises on personal exploration and review on one’s attitudes toward life and death issues. It is hoped that students are better equipped with knowledge competence, practice competence as well as self competence in working with death, dying and bereavement.
Credit: 6
Assessment: 100% coursework
According to the WHO, health is a “complete state of physical, mental, and social well-being, and not merely the absence of disease or infirmity.” As people age, they are increasingly facing challenges in their physical and mental health and in their social wellbeing. A better integrated health and social care system will help older people to better adjust to their aging processes and to minimise the negative impacts of aging to their wellbeing. This course is designed to help students from diverse academic backgrounds to understand the core values, conceptual models, intervention strategies, and service delivery systems of the integrated health and social care model. Building on the foundation values and knowledge of their own disciplines, students will learn how to effectively develop and implement a multi-disciplinary team in geriatric care settings.
Credit: 6
Assessment: 100% coursework
*Offering schedule and quota is subject to availability
Capstone project (Compulsory)
This course aims to teach students how to integrate and apply the knowledge and skills they acquired through the programme. Students will articulate their research objectives, conduct a relevant literature review and develop indicative methodology. The course provides students with the opportunity to undertake a major piece of supported independent research. It is an opportunity to apply skills and techniques learned during the taught component of this programme to develop substantive original research of interest to the student. Projects will be supervised by academic staff affiliated with the Population and Policy Analysis programme.
Individual projects and research questions are chosen and formulated by students, and supported during the research process by one-to-one or small group meetings with a nominated member of academic staff, and student-led group meetings to seek peer support. The project may address a methodological or practical issue using desk-based research and secondary data sources or may involve primary data collection. It may also be carried out in conjunction with an external organisation (such as a local government, a charitable association or a commercial organisation) to address a relevant research or practical issue of interest to them (such as assessing potential impacts of policy changes) and make use of their data or other inputs. Regardless of the nature of the project itself, all projects must have a clearly defined aim and set of specific objectives that are novel or original and which relate to this programme of study. All projects should be written up as an academic piece of work, using the guidance provided during the module.
Credit: 12
Assessment: 100% coursework
Please click here for the regulations, syllabus and course descriptions.