CCST9066 Science, Technology and Big Data
Big Data Solutions to Social Problems: the Good, the Bad and the Ugly

SDG-03
SDG-09
Sustainable Development Goals

Timetable for Lectures
Non-Permissible Combination:
CCST9047 The Age of Big Data

 

Course Description

The increasing availability of big data and the rapid advances in data analytical tools provide unprecedented opportunities for us to understand complex social processes and potentially address many of the pressing challenges facing society today. As big data and analytical tools are routinely applied in our daily lives, it becomes increasingly important to understand their capabilities and limitations. This course aims to enrich students’ big data literacy through three major areas of focus: (1) Learning the basic concepts of big data, (2) Reviewing representative big data analytical techniques, and (3) Discussing opportunities and challenges of big data analytics in tackling social problems.

The course will illustrate the core principles of using large-scale datasets and computational techniques in developing big data solutions to social problems. The lecture will mainly use case studies from research papers to provide an in-depth understanding of big data analytics in various fields, from economics to sociology to policy and more. The goal is to inspire students to think creatively about how big data analytics can advance social science research and create social benefits. Meanwhile, students will learn to identify the limitations and potential biases of these big data approaches, developing a vision for using big data to benefit society in a more effective, reliable, and responsible way.

Course Learning Outcomes

On completing the course, students will be able to:

  1. Understand the concept of big data.
  2. Demonstrate an understanding of the logic behind widely adopted big data analytical techniques.
  3. Describe the basic principles of how future outcomes are being predicted based on historical and current data.
  4. Examine similarities and differences between big data analytics and the traditional way of doing science.
  5. Critically evaluate the analytical strategies adopted in using big data to solve a specific social problem.

Offer Semester and Day of Teaching

Second semester (Wed)

Study Load

Activities Number of hours
Lectures 24
Tutorials 10
Reading / Self-study 20
Group projects and case studies 30
Assessment: Essay / Report writing 20
Assessment: Presentation (incl preparation) 10
Assessment: Debate presentation (incl preparation) 10
Total: 124

Assessment: 100% coursework

Assessment Tasks Weighting
Debates 20
Group project 30
Quizzes 20
Critique essay 30

Required Reading and Websites

  • Notes provided by the lecturer
  • Selected articles from books, magazines and websites for each lecture

Reading:

  • Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.
  • Blumenstock, J., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073-1076.
  • Gao, J., Zhang, Y. C., & Zhou, T. (2019). Computational socioeconomics. Physics Reports, 817, 1-104. [Contents and Section 1 “Introduction”]
  • Hofman, J. M., Watts, D. J., Athey, S., Garip, F., Griffiths, T. L., Kleinberg, J., & Yarkoni, T. (2021). Integrating explanation and prediction in computational social science. Nature, 595(7866), 181-188.
  • Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., & Van Alstyne, M. (2009). Computational social science. Science, 323(5915), 721-723.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt.
  • Wang, Y., Jones, B. F., & Wang, D. (2019). Early-career setback and future career impact. Nature Communications, 10(1), 4331.
  • Yin, Y., Gao, J., Jones, B. F., & Wang, D. (2021). Coevolution of policy and science during the pandemic. Science, 371(6525), 128-130.

Websites:

Course Co-ordinator and Teacher(s)

Course Co-ordinator Contact
Professor J. Gao
Department of Social Work and Social Administration, Faculty of Social Sciences		 Tel: 3917 1094
Email: gaojian@hku.hk
Teacher(s) Contact
Professor J. Gao
Department of Social Work and Social Administration, Faculty of Social Sciences		 Tel: 3917 1094
Email: gaojian@hku.hk