This course provides a hands-on, semi-technical introduction to the methods and ideas of Computational Social Science. Lying at the intersection of Computer Science, Statistics and Social Science, the emerging field of Computational Social Science uses large-scale behavioral data, otherwise known as "big data", to study and measure human behavior with precision largely thought impossible just a decade ago. Throughout the semester, we will look at some big data sources such as network data and text data and the innovative methods that are being used to analyze them. Each week, students will have the opportunity to practice different methods byusing Python to analyze interesting data sets ranging from online review data collected from Yelp and Airbnb to human mobility data collected from cell-phones. While the course is open to advanced undergraduate students from all majors and disciplines, the majority of the materials will be drawn from sociology and criminology. Students are expected to have some basic understanding of quantitative methods, but no prior programming experience is required. On top of the weekly homework, graduate students are also expected to submit a computational report at the end of the semester as the final project.
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