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


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 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.