Computational Social Science | Training

Overview

Course Data Science Text Analysis Research Methods

Overview: A course surveying text analysis, machine learning, and social network analysis for social-science research.
Audience: This course was first designed for undergraduate and graduate social-science majors. The materials are generally applicable to students of any level and degree program.
Teaching: National Chengchi University — Spring 2024

KIMEP University — Spring 2023

Stanford University — Spring 2019

Content

Course Content

Digital technology is ubiquitous in the social world. This course surveys text analysis, machine learning, and social network analysis and uses these tools to analyze online text, images, and metadata. It also examines how computational tools and digital data are changing social science. Sample code and data are provided, with exercises introduced step by step.

Requirements

No prerequisites or prior programming experience are required.

Resources

Materials: Syllabus — Syllabus-Intro-to-Computational-Social-Science.pdf

Module 1: Text Analysis — Module-1-Text-Analysis.zip

Module 2: Machine Learning — Module-2-Machine-Learning.zip

Module 3: Semantic Networks — Module-3-Semantic-Networks.zip
Relevant Literature: Browse references and supporting literature for this training module in the Organization of Power Lab Zotero library.
Open this training module's Zotero collection