Data Journalism 1
summary
Argues that data journalism is reporting editing and distribution applied to datasets and gives concrete tools and results: Django CMS origin leads to widespread newsroom sites; DocumentCloud spawns Backbone and Underscore; ProPublica’s prescribers dataset analyzes 1.1 billion prescriptions and finds 3% of prescribers write half the scripts; Panda provides hosted dataset storage and entity reconciliation; census reporter and Message Machine expose representative votes and campaign email A B testing; a charity investigation finds ~ $1B raised but only ~$50M reaches causes and an app gets 1M visits in 24 hours.
AI-written summary · about
chapters (21)
- 0:00Data journalism and open source
- 1:13Defining journalism
- 3:59What makes data journalism unique
- 5:46Bridging journalism and tech
- 7:32Early journalism-tech bridges
- 10:03Developers as journalists
- 13:13Fellowships for tech in news
- 14:17Impactful data journalism projects
- 17:30Public data access challenges
- 18:56America's worst charities case
- 21:22Talent drain from newsrooms
- 22:47Flaws in journalism curricula
- 24:53Master's for news apps
- 27:21Education access barriers
- 29:48Grad school for journalism jobs
- 31:18Data journalism and low-income communities
- 33:02Retraining journalists and hardware
- 36:11Open source and data journalism overlap
- 37:34Tools for data journalists
- 40:46Practicality vs real-world
- 41:49Closing remarks
