Skip to content

gesiscss/css_methods_python

 
 

Repository files navigation

Introduction to Computational Social Science methods with Python

This repository will grow to house a full introductory course consisting of self-explanatory teaching modules in the Jupyter Notebook format. The final course will consist of five sections with 14 sessions that will allow easy exploration of data with a minimum of coding skills, but gradually lead participants to acquire more coding skills in Python. These resources are provided as part of the Social ComQuant project.

Notebooks are developed for Anaconda 2022.10 which can be downloaded here.

Binder

Section D: Data analysis methods

Session 7: Network analysis

Session 8: Unsupervised machine learning

Session 9: Statistics & supervised machine learning

About

A full course of self-explanatory and freely available materials on CSS methods

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors