منابع یادگیری مهارت‌های مقدماتی علوم‌اجتماعی محاسباتی

• Week1: Shell Scripting
• Week2: Python
• Week3: R
• Week4: Data Cleaning, Descriptive Analysis, and Visualizations
• Week5: Probability and Regressions
• Week6: Causal Inference
• Week7: Regression Discontinuity and Instrumental Variables
• Week8: Fixed Effects, Difference-In-Difference
• Week9: Standard Errors
• Week10: Text-as-Data (NLP)
• Week11: Network Analysis

• Shell Scripting Tutorial

• NYU Data Bootcamp
• “Introduction to Python for Science”
• Getting started with Jupyter notebooks
• NumPy quickstart
• Pandas getting started

• Simon Ejdemyr, “R Tutorials: Basics”
• Hadley Wickham and Garrett Grolemund, "R for Data Science"
• Getting started with R Markdown
• R Packages
• Top 50 ggplot2 Visualizations

• World Bank’s Development Impact Evaluation (DIME):
  • Tidying Data
  • Data Cleaning
  • Data Construction (includes constructing panel data)
•  “Missing-data imputation”
•  Jason Osborne, “Notes on the use of data transformations”,Practical Assessment, Research, and Evaluation
• “Statistical Data Types” chapter of Andrius Buteikis, Practical Econometrics and Data Science
• World Bank DIME, Descriptive Analysis in R
• J-PAL, “Data Visualization”
• United Nations’ guide to making data meaningful
• R Graph Gallery (useful for getting ideas and example code in R)
• Kieran Healy,Data Visualization: A Practical Introduction

• Chapter 2 and 3 of Scott Cunningham’s Causal Inference: The Mixtape
• Regression and Other Stories, Gelman et al, 2022
• “Binary Choice Models”
• UCLA Statistical Consulting Group’s introduction to interaction terms Decomposing, Probing, and Plotting Interactions in R/Stata
• Probability cheatsheets

• Josh Angrists and Victor Chernozhukov, “Applied Econometrics: Mostly Harmless Big Data” (companion course to Mostly Harmless Econometrics)
• MIT 14.771/ Harvard 2390b “Empirical Methods” handout
• Chapters 4 and 5 of Scott Cunningham’s Causal Inference: The Mixtape
• Gary King’s slides on Matching Methods for Causal Inference

• Chapters 6 and 7 of Scott Cunningham’s Causal Inference: The Mixtape

• Chapters 8 and 9 of Scott Cunningham’s Causal Inference: The Mixtape
• Josh Blumenstock’s lecture notes on fixed effects models
• Fixed and Random Effects Models in Python

• On non-standard errors:
  • Summary of Athey, Abadie, Imbens and Wooldridge (2017) (full paper here)
  • Nonstandard errors in code
• Intro to power analysis
• Thomas Brambor, William Roberts Clark and Matt Golder, “Understanding Interaction Models: Imroving Empirical Analyses”,Political Analysis14(1):2006.

• Introductory Online Courses:
  • Chris Bail’s Text as Data course
  • Introduction to NLP in Python, Sentiment Analysis in Python, and Advanced NLP with spaCy 
• Online Open Access Book:
  • Speech and Language Processing (3^rd Edition), Jurafsky and Martin
• Data Collection
  • Chapter 17 (Collecting Data from the Web) of Rochelle Terman’s PLSC 31101: Computational Tools for Social Science course notes
  • Different text file formats
  • Working with APIs
  • HTML and CSS
• Topic Modeling
  • David M. Blei, “Surveying a suite of algorithms that offer a solution to managing large document archives,”Communications of the ACM55(4):2012
  • Chris Bail’s Text as Data Course, Topic Modeling
• Free Seminars:
  • NLP + CSS Tutorials
• Useful R and Python packages:
  • Interactive LDA Visualization
  • Structural Topic Modeling
  • Textnets, R package for automated text analysis using network techniques
  • Keyword Assisted Topic Models
• Other Useful Resources:
  • Learningregex
  • regexcheatsheet
  • Glossary of Linguistics Terms

• Interactive Introduction to Network Science
• Aaron Clauset’s course, A Brief Introduction to Network Science
• Brian Keegan’s Network Science course slides
• Carlos Castillo’s Network Science course slides
• Creating, displaying, and sharing interactive network visualizations on the web

ژورنال‌ها، مراکز، دپارتمان‌ها و کنفرانس‌های علوم اجتماعی محاسباتی

• ژورنال‌های علوم اجتماعی محاسباتی
• مراکز و دپارتمان‌های علمی و دانشگاهی
• کنفرانس‌های علوم اجتماعی محاسباتی
• برخی از مراجع دسترسی به داده‌ها

• General Science Journals (Nature, Science, PNAS, Science Advances, PloS One, Scientific Reports)
• Nature Human Behavior
• EPJ Data Science
• Journal of Computational Social Science (Springer)
• Journal of Mathematical Sociology
• Journal of Artificial Societies and Social Simulation (JASSS)
•  Advances in Complex Systems
•  Social Science Computer Review
• Physica A
• Socius

• Carnegie Mellon University (Computational, Society, & Organization)
• Northeastern University (CSS Initiative, Network Science Ins)
• ETH Zurich (Professorship of CSS)
• Oxford (CSS initiative)
• George Mason University (Computing and Data Science)
• GESIS (Department of CSS)
• Indiana (Center for Complex Networks and Systems Research)
• Chicago (MSc in CSS)
• Amherst University (CSS Institute)
• Stanford (Center for CSS)
• Columbia (CSS Working Group)
• Microsoft (CSS Group)
• Stony Brook (Center for CSS)
• Imperial College (CSS Research Group)

• International Conference on Computational Social Science (IC2S2) 
• International Conference on Web and Social Media (ICWSM)
• International World Wide Web Conference (WWW)
• Political Networks Conference (PolNet)
• International Conference on Social Media and Society
• Analytical Sociology Conference
• Network Science Conference (NetSci)

• Harvard Dataverse
• Index of Complex Networks (University of Colorado Boulder)
• OpenABM
• Google Trend
• Twitter API
• Facebook API
• American National Election Study (ANES)
  General Social Survey (GSS)