Learning Materials

Open source teaching and learning resources for computational social science.


We provide state-of-the art training in a range of different areas in computational social science from ethics to text analysis and mass collaboration. Below you can find videos, slides, code, and teaching exercises. These lectures assume a basic, working knowledge of the R language. If you do not yet know R,this resource is a great place to start. If you are a teacher, the source code for all of our teaching materials is available here. Or, check out alternative curricula developed by organizers of SICSS partner sites here that include material in different software languages and for different types of audiences.

Day 1: Introduction and Ethics

Introduction to Computational Social Science


Ethics: Part 1

Ethics: Part 2

Ethics Additions and Extensions

Day 2: Collecting Digital Trace Data

What is Digital Trace Data?

Strengths and Weakness of Digital Trace Data

Application Programming Interfaces

Screen Scraping

Building Apps and Bots for Social Science Research

Day 2 Group exercise

Day 3: Automated Text Analysis

An Introduction to Text Analysis

Text Analysis Basics

Dictionary-Based Text Analysis

Topic Models

Text Networks

Day 3 Group exercise

Day 4: Surveys in the Digital Age

Survey Research in the Digital Age

Probability and Non-Probability Sampling

Computer-Administered Interviews

Combining Surveys and Big Data

Additions and Extension

Day 5: Mass Collaboration

Introduction to Mass Collaboration

Human Computation

Open Call

Distributed Data Collection

Fragile Families Challenge

Day 6: Experiments

What, Why, and Which Experiments?

Moving Beyond Simple Experiments

Four Strategies for Making Experiments Happen

Zero Variable Cost Data and Musiclab

Bonus Lectures by Leaders in the Field:

Check out our YouTube channel for bonus lectures by dozens of leaders in the field.