Summer Institute in Computational Social Science
The Summer Institute in Computational Social Science (SICSS) is co-organized by Christopher Bail and Matthew Salganik with the purpose to introduce graduate students, postdoctoral researchers, and beginning faculty to computational social science
The inaugural institute, sponsored by the Russell Sage Foundation, was held at Princeton University from June 18 to July 1, 2017. More details as well as slides and materials for SICSS 2017 are available at https://compsocialscience.github.io/summer-institute/2017/
The second institute, sponsored by the Russell Sage Foundation and the Alfred P. Sloan Foundation, was held at Duke University from June 17 to June 30, 2018. More details as well as slides and materials for SICSS 2018 are available at https://compsocialscience.github.io/summer-institute/2018/
For its third iteration, SICSS will return to Princeton from June 16 to June 29, 2019. Application materials can be found at https://compsocialscience.github.io/summer-institute/2019/
How to Get a Job in Computational Social Science
How to Publish an Article in Computational Social Science
How to Get Grants in Computational Social Science
How to Publish a Book in Computational Social Science
Computational Advances in Social Science Experiments
Behind the Bot Study
Digital Trace Data Case Studies Using Social Media Advertising Data
Data Ethics Section 2: Ethical Considerations and Case Study
Data Ethics Section 1: Data isn't just data, and ethical considerations
Experimental Possibilities of Generative Language Models
Representing Text as Data
Measurement and Causal Inference Using Text as Data
Introduction to Computational Social Science
Why SICSS?
Tutorial on deep learning for causal inference
Using images and video data for social science: Challenges and opportunities
Introduction to text analysis in python
Communicate and Collaborate
Modeling
Programming Basics in R
Data Visualization
Data Wrangling
R Basics
Installing R and RStudio
Welcome to SICSS Bootcamp
Using Empirica for High Throughput Virtual Lab Experiments
Opportunities and Challenges with Industry Collaborations