This course provides students with an extensive exposure to the fundamental principles and essential techniques of computational social science methods, ranging from automatic collection of digital and online data to machine learning with or without human supervision. The methods are intended to complement and enhance the traditional social science methods of data collection and analysis, such as survey, experiment, content analysis, and statistical analysis. The course is useful for students who are interested in using computational methods for social, cultural, business, legal, and other areas of research.


  1. Salganik, M. (2018). Bit by bit: Social research in the digital age. Princeton University Press.
  2. Ackland, R. (2013). Web social science: Concepts, data and tools for social scientists in the digital age. Sage.


Week 1: Introduction to Computational Social Science [ppt]

Week 2: Python Programming I [ppt]

Week 3: Python Programming II [ppt]

Week 4: Web Scraping [ppt]

Week 5: Data Visualization [ppt]

Week 6: Text Mining [ppt]

Week 7: Midterm Exam

Week 8: User Profiling [ppt]

Week 9: Virtual field trip: Zoom meetings with computational social scientists

Week 10: A/B Test and Network Analysis [ppt]

Week 11: Final Project Progress Report

Week 12: Reading week

Week 13: Final Exam

Week 14: Final Project Presentation I

Week 15: Final Project Presentation II

Grading Opportunities

  1. Midterm Exam – 10%
  2. Final Exam – 10%
  3. Final Project Progress Report – 20%
  4. Final Project Presentation – 40%
  5. Weekly Reflections – 10%
  6. Class Participation – 10%

Class Policies

  1. Professional behavior is expected of all students. Demand and show respect. It is important to show respect for others and their opinions as well as expect the same for your own.
  2. Dr. Qin reserves all rights to make changes to this syllabus. However, any changes regarding due dates of assignments or dates of tests will be carried out only by class consensus. In effect, the syllabus is our “contract”. If there are updates to schedule, Dr. Qin will post your e-mail account with notification. You are responsible for keeping up with readings and due dates of assignments.
  3. Late work is not accepted and extra credit work is not offered. Details about these policies will be discussed in class.
  4. Only in extreme circumstances will students be permitted to make up exams or get deadline extensions on projects. Make-up opportunities must be discussed with the instructor in advance of the missed class period or due date.
  5. Email etiquette: When sending an email to your instructor, please include an appropriate greeting, your name, and the day/time during which your class meets. All emailed questions regarding speech drafts should be specific and brief. If you require more than what a brief question/answer will cover, please make an appointment with your instructor during office hours or extra help sessions.