Learning Materials

Open source teaching and learning resources for computational social science.

Overview

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
Why SICSS?
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
Group exercise

Day 3: Automated Text Analysis

An Introduction to Text Analysis
Text Analysis Basics
Dictionary-Based Text Analysis
Topic Models
Text Networks

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.