Course Materials for Computational Social Science

Course Materials

This repository contains all of the materials for Sociology 273, Computational Social Science Parts A/B. Designed as part of Berkeley's Computational Social Science Training Program.

This course is a rigorous, yearlong introduction to computational social science. The target audience is 2nd year and beyond PhD students who have completed their home departments' introductory statistics courses. We cover topics spanning reproducibility and collaboration, machine learning, natural language processing, and causal inference.

It has a strong applied focus with emphasis placed on doing computational social science. It makes extensive use of simulations, functional programming, and visualizations to illustrate statistical concepts and demonstrate how "computational social science" is a framework to think about how to analyze big data.

By the end of the course, students will be well acquainted with some of the latest research and advanced in computational social science research, and begin working on their own projects.

Table of Contents for Computational Social Science Labs

  1. Reproducible Data Science and Introduction
  2. Fundamentals of Machine Learning
  3. Supervised Machine Learning
  4. Unsupervised Machine Learning and AutoML
  5. Natural Language Processing
  6. Causal Inference