Teaching Data Journalism

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Teaching Data Journalism

  1. 1. Teaching Data Journalism #EJTA2014, Jyväskylä, May 22 Turo Uskali & Heikki Kuutti Data journalism = Journalism based on large data sets Data journalism (#dj) or data- driven journalism (#ddj)
  2. 2. a story based on pieces of (separate) information PAPER FILE DOCUMENTS A PAPER DOCUMENT DATA a story based on (large) combination of data INFO DATABASE INFORMATION SYSTEM DATABASE DATABASE DATABASE DATA DATA INFO INFO Paper story and data story
  3. 3. STORY TOPIC SOURCE SELECTION DATA INSPECTION AND CLEANING DATA ANALYSIS INSPECTION OF DATA ANALYSIS PREPARATION, VISUALISATION AND PUBBLISHING INFORMATION REQUEST document source human source journalistic observation data paper document official database open data internetpaperofficial data editorial database data material non-data story material Datajournalism working process
  4. 4. JOURNALISTIC QUESTIONS DATA DATA CLEANING AND ANALYSIS COMMENTS TO ANALYSIS DATA STORY PRE-DATA POST-DATA Data story process
  5. 5. Data journalism courses since 2013 • Data Journalism (six weeks) pilot course consisted of * 16 hours lectures * data journalism literature (The Data Journalism Handbook) * a data journalistic team project * final seminar - The pilot course had two main instructors and three visiting professionals, who were specialists in data visualization and networks, open data, and data journalism tools.
  6. 6. Data journalism strories • The themes of the dj projects varied from local traffic accidents and parking tickets to the use of Fjällräven backpacks by students’ of different Faculties in the Uni.
  7. 7. Feedback from the pilot course • Almost all the teams had project specific problems concerning the finding of suitable data sets. • Many good story ideas were invalidated by a lack of open data. • In hindsight, the pilot course was possibly too intensive and more than two weeks should be allowed for developing a good data journalism project.
  8. 8. Aiming at next level: Strategy for 2014 • Adding four more weeks • Focusing on Jyväskylä’s open data sets • Five data journalism gurus visiting, one student tutor • Connected to data journalism work methods -reseach project • Facebook’s ”help desk”, link sharing and discussion forum • Integrated to EJC’s MOOC
  9. 9. Social media connection – Collaboration via Facebook groups • Dj 2013 • Dj 2014 • Datajournalismiopet (Datajournalism instructors) 15 followers • Datajournalismin avoin tukiryhmä (Open Group for Data Journalism Assistance) 250 followers • Finnish Open Data Ecosystem (2172)
  10. 10. DJ 2014: 11 students started, but not a single data story yet – WHY?
  11. 11. Main reason: Months delayes in getting open data from the City of Jyväskylä Image From Wikimedia Commons
  12. 12. Main lesson learned this time • Without data, there is no data story.
  13. 13. Minor setback: EJC’s MOOC on Data Journalism started too late - on Monday (May 19th)
  14. 14. Important issues in teaching dj • Journalism laboratory: piloting, testing of key importance • Journalistic questions to be answered by data • Theory and practice combination –research based
  15. 15. Important issues in teaching dj • Know-how of data access and efficient data requests and negotiations + finding ready-to- use data sets • Know-how of numbers, statistics • Know-how of basic data tools (Excel, Open Refine…) • Cooperation with other schools and data gurus, constantly sharing best practices – European wide next?
  16. 16. In conclusion: Three levels of data journalism (education) • Basic level: General dj (for daily use, based on existing open data sets, basic data tools) • Advanced: Investigative dj (”what is in the shadows”, weeks-months of research, FOI requests, programming skills) • Real-time (sensor journalism, automated news creation based on algorithms)
  17. 17. Thank you! Follow research on dj #DaJoRe

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