https://github.com/dlab-berkeley/Computational-Social-Science-Training-Program
This course is a rigorous, year-long introduction to computational social science. We cover topics spanning reproducibility and collaboration, machine learning, natural language processing, and causal inference. This course has a strong applied focus with emphasis placed on doing computational social science.
https://github.com/dlab-berkeley/Computational-Social-Science-Training-Program
Last synced: 9 months ago
JSON representation
This course is a rigorous, year-long introduction to computational social science. We cover topics spanning reproducibility and collaboration, machine learning, natural language processing, and causal inference. This course has a strong applied focus with emphasis placed on doing computational social science.
- Host: GitHub
- URL: https://github.com/dlab-berkeley/Computational-Social-Science-Training-Program
- Owner: dlab-berkeley
- Created: 2020-07-14T00:53:09.000Z (almost 6 years ago)
- Default Branch: main
- Last Pushed: 2025-09-09T18:07:49.000Z (10 months ago)
- Last Synced: 2025-09-09T20:54:21.566Z (10 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 95.3 MB
- Stars: 268
- Watchers: 12
- Forks: 103
- Open Issues: 3
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-causal-ai - Computational-Social-Science-Training-Program - This repo contains all of the materials for Sociology 273, Computational Social Science Parts A/B. Designed as part of Berkeley's Computational Social Science Training Program. *(Jupyter Notebook)* (🚀 GitHub Repositories / 🌟 **Real-World Magic**)