Major in Social Data Science

SDA1001 Introduction to Social Data Science (3 credits)

This course provides students with a foundational understanding of how techniques in data science can be utilized to address complex social issues. This interdisciplinary course explores the intersection of social sciences and data science, focusing on issues such as the ethical implications, methodologies, and applications of social data. With the background focusing on research methods in social sciences, this course emphasizes how the gathering, interpretation, and analysis of quantitative data using data-science techniques can allow social scientists to address questions related to people and the society more effectively, efficiently, and creatively.

ECO2101 Introduction to Economics (3 credits)*
(Restriction(s): Students who have taken either BUS2105 Microeconomics for Business or ECO2104 Introduction to Microeconomics are not allowed to take this course.)
This course emphasizes the economic way of thinking. It introduces students to the basic principles of microeconomics and macroeconomics and shows them how economists study consumer behaviour, firm behaviour, and the performance of the whole economy. It also demonstrates how these principles can be used to analyse public policies and understand society. 

GOV2101 Introduction to Political Science (3 credits)*
This course is a general survey of the field of political science. Students are not required to have any background in the discipline. The course is designed to introduce some basic concepts and approaches in political science, and to link them to current affairs. It provides the foundation for future studies in the field.

PSY2101 Introduction to Psychology (3 credits)*
The purpose of this course is to introduce fundamental concepts and theories in psychology specifically in the daily life context. Upon completion of this course, students should have acquired basic understanding of the major theories and research findings in various areas of psychology, and how these major psychological theories can be used to examine and explain human behaviours, emotion, cognition and mental health.

SOC2101 Introduction to Sociology (3 credits)*
(Restriction(s): Students who have obtained Grade D or above in AL Sociology are not allowed to take this course. Students are not allowed to take both this course and CUS3213 Culture, Power and Government)
This is an introductory course in Sociology, starting with an overview of the nature of the discipline, followed by a survey of various aspects of the structures and dynamics of social life. General and specific examples are used to illustrate how thinking sociologically adds to our knowledge of the world around us.

*Any of two of the four existing foundation courses offered by the BSocSc Programme.

Introduction to Programming for Data Science (3 credits)
This course is designed for students to provide an introduction to data science in the digital age. Data science concerns using data to understand and analyse actual phenomena. The course covers basic IT skills, such as computer programming to assist data manipulation, data analysis and data communication. Turing award winner Jim Gray predicts that data science will be a “fourth paradigm” of science, which is data-driven and can be differentiated from empirical, theoretical and computational paradigms. Students will learn the fundamentals and appreciate the importance of data science. The first half of the course is about learning the programming language. The topics will include: the basic Python language syntax, variable declaration, basic operators, program flow and control, Python data structures, defining and using functions and recursion, file and operating system interface. In the second half of the course, basic Python packages designed for data science will be introduced, such as NumPy, SciPy, Pandas, and Matplotlib.

Mathematical Fundamentals for Data Science (3 credits)
The Mathematical Fundamentals for Data Science course offers a comprehensive introduction to the essential mathematical foundations required for data science, with a focus on probability and statistics. The course covers fundamental concepts such as sets, functions, and basic algebra, while delving into probability theory, descriptive and inferential statistics, common probability distributions, and data visualization techniques. By the end of the course, students will have the skills necessary to apply these mathematical principles to real-world data analysis scenarios, making them well-prepared for careers in data science, analytics, or any field requiring statistical reasoning.

Introduction to Artificial Intelligence (3 credits)
(Prerequisite: CDS1001 Introduction to Programming for Data Science) 
Artificial intelligence is the study of intelligent agents. Due to the continued success of 2 applying artificial intelligence to different challenging problems requiring high-level intelligence, there is an explosive interest in this field for scientists, dreamers, entrepreneurs and educators. This course is designed for students to understand and appreciate the basic principles of artificial intelligence. It covers computational intelligent systems, which can support decision making, interact with humans, navigate vehicles and achieve many other interesting and useful tasks. These intelligent systems are extremely useful in business, science, the humanities and other fields.

Data Mining (3 credits) 
(Prerequisite: CDS2002 Introduction to Artificial Intelligence) 
Data mining is an important component of data science that discovers knowledge from huge databases. Data mining is an interdisciplinary field, integrating statistics, pattern recognition, neuro-computing, machine learning and databases. It is also one of the fundamentals to extracting interesting knowledge (domain-specific rules, patterns, constraints and regularity). Students will learn the basic principles and core ideas of data mining. This course also covers many data mining approaches to discovering knowledge from a vast amount of valuable databases in business, finance, urban and medicine. Quantitative analytical skills are taught to interpret data mining models. Current IT skills are also covered in the course.
 

ECO3002 The Economics of the Digital Economy (3 credits) 
(Prerequisite(s): (a) ECO2101 Introduction to Economics, or (b) ECO2104 Introduction to Microeconomics and ECO2105 Introduction to Macroeconomics, or (c) BUS2105 Microeconomics for Business) 
The growing presence of information and digital technology in our economy has created unprecedented opportunities and challenges. Whether we examine our daily lives as consumers or the operations of large companies, we cannot escape the influence of the digital economy. This course will survey the intersection of digital technology and the economy.

GOV3219 Introduction to Computational Political Science (3 credits) 
How has the rise of big data and computational tools transformed the process of political science research? This course provides students with a foundation in computational tools used in political science research, such as web scraping, natural language processing (NLP), social network analysis (SNS), and machine learning.

PSY3103 Psychology of Human Performance and Technology (3 credits) 
Engineering psychology is a sub-discipline of psychology that is concerned with understanding human capabilities and limitations in interacting with technology. The goal is to understand how we can optimise machine design for human operation. Many technological systems do not perform as effectively as they intended to be because their designs are not compatible with the way people attend, perceive, think, memorise, decide and act. You might have experienced the following two examples when using some poorly designed technologies: 

• Leaving your original document behind after a photocopying task 

• Spending a long time to find a common function you needed when using software (e.g. MS Excel) but ended up not finding it

In order to design human-centred systems, engineering psychologists apply knowledge and theories from cognitive psychology to systems design. In this applied course, we will extend selected topics from cognitive psychology (e.g. attention, memory, decision making, etc.) to examine how they relate to the interaction between humans and interactive systems. We will also cover a number of design and evaluation techniques from human-computer interaction (HCI) – a closely related discipline. 
NB: There is no technical engineering mathematics involved in this course

SOC3334 Science, Technology and Society (3 credits) 
This course will examine and reflect on science and technology's impact on the world economically, politically, socially and environmentally. The course will start with examining the important questions of what science and technology are and their impacts in contemporary society. The focus will then shift to (1) the various theoretical underpinnings of science and technology in society, (2) impacts of science and technology on international relations, social institutions, social groups and everyday life, and (3) the future of science and technology in human civilisation. The material presented in class will be supplemented with readings and students’ reflections on their daily life experiences.

SOC3336 Digital Society (3 credits)
Contemporary modern life can be described as digital society, but also as mobile, networked, visual, and cyborg society. As most humanity increasingly lives in urban settings, this course seeks to look at how digital society is part of Sociology and the Smart cities we now live in. It addresses mobility, big data, risk, and the social consequences of our online lives. Students will explore the emergence of digital sociology and the topics of internet pornography, online addiction, surveillance, AI, activism, Smart cities, and social networking. Gender and 6 identity issues are key themes, as are concerns about the world of work and employment. Ultimately the course will prepare students for life and work in the digital society.

SSC3319 Junior Research Project (3 credits)
(Prerequisite(s): At least one research methods course offered in the BSocSc programme)
The ability to conduct independent research is an important learning outcome for any social science graduate. This course provides a platform for students to employ and develop their research skills. Students will receive individual supervision from instructors and work towards the production of a project which incorporates key elements of a social science research project. These elements include (but are not limited to) a literature review, development of a theoretical framework, and the formulation of appropriate research questions and methods of data analysis etc. This project could be further developed to form part of students’ (optional) Senior Thesis. Students who choose not to do a Senior Thesis may submit a complete research project (with data and policy analysis, etc.) but is not a requirement for the overall assessment of the project.

SSC4319 Senior Thesis (3 credits) 
(Prerequisite: SSC3319 Junior Research Project) 
The senior thesis allows students to build upon/modify the research topic and design that they have worked in the Junior Research Project course. They will be given the opportunity to apply theories, research tools and techniques to implement the research and write up an independent thesis on their chosen topic.