The technological revolution in mobile and Internet communications has transformed how we interact and how the society functions. It poses new opportunities but also challenges to social scientists who may not have adequate training in collecting, cleaning, and analyzing large-scale unstructured data. This course aims to 1) offer a survey of methods and techniques commonly used in computational social science, and 2) introduce a pipeline of doing computational social science, starting with data collection and data wrangling, theorizing and asking questions, measuring unmeasurables, followed by useful analytical strategies and result presentation. The topics that will be covered in the course include basics in machine learning, network science, textual analysis, webscraping and APIs, explorative data analysis, and big data ethics. The programming language used in the course is Python (Students who are proficient in the R programming language are also encouraged to apply.).

Prerequisites:

• Basic programming skills in Python or R.
• Intro statistics and linear regression models.