The web is a dynamic ecosystem where socio-technical systems like Google, Facebook, Wikipedia, and other platforms shape and reflect human behavior on a global scale. In this course, students will explore how to investigate social phenomena on the web using data science as a research methodology. Concretely, students will learn to formulate research questions about online socio-technical systems; collect, clean, and analyze web-native data through a variety of Python libraries; investigate human behavior and its interplay with algorithmic systems using quantitative and qualitative methods; and critically evaluate findings within the broader context of societal, cultural, and ethical considerations. This course includes a semester-long research project on a provided theme, which culminates with an incrementally written research paper.

Units: 1

Max Enrollment: 18

Prerequisites: One of the following CS 230, CS 230P, or CS 230X and permission of the instructor.

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Degree Requirements: DL - Data Literacy (Formerly QRDL)

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes: