readme.md

#Data Guidelines and Resources

We will include here our guidelines and standards for data projects (inspired by the standards of other organizations) as well as resources for working with data.

Useful data guidelines and standards:

• ProPublica's data bulletproofing
• Sunlight Labs Data Visualization guide

Resources for working with data:

• Working with version control
  • Version control with Git and Github **
• Where to get data (online sources, open-source data, government data, using APIs)
  • Awesome Public Datasets
  • NICAR Data Library
  • Intro to APIs **
  • API repository
  • Use Kimono to turn websites into APIs
  • Use Scrapy for web scraping
  • Web scraping with Python and Ruby **
  • Downloading Web Data Without Scraping
  • List of sources for international data **
  • Finding and using healthcare data **
  • Using Monarch Pro to mine data from text and PDF into tables
  • Using FOIA to get electronic data **
• How to work with different data formats
• Cleaning data
  • Advanced functions in Excel **
  • Clean data with OpenRefine **
  • Solving problems in dirty data **
  • Clean data with regular expressions **
• Data analysis and software
  • Getting started with Excel **
  • Importing data into Excel **
  • Python Intermediate Bootcamp
  • Advanced Python from NICAR2015
  • Intro to Data Science: using Python in the newsroom
  • Useful R packages for data analysis and visualization
  • Django for data analysis
• Data visualization
  • Fusion Tables for beginners **
  • Visualizing data with R
  • Working with geo data and CartoDB
  • Network analysis with Gelphi
  • Tools for social network analysis **
  • NodeXL for network analysis **
  • Intro to d3
• Statistical - predictive modeling
  • 15 hours of statistics and machine learning Youtube videos
  • Statistics in R **
  • Avoid Statistical Pitfalls **
  • Statistics terms used in research studies
• Making an application
  • Building a data news application **

** needs IRE member account

More to come