Teaching Data Journalism

#EJTA2014, Jyväskylä, May 22Turo Uskali & Heikki Kuutti

Data journalism= Journalismbased on largedata sets

Data journalism (#dj) or data-driven journalism (#ddj)

a storybased onpieces of(separate)information

PAPER FILEDOCUMENTS

A PAPERDOCUMENT

DATA

a storybased on

(large)combination

of data

INFODATABASE

INFORMATION SYSTEM

DATABASE

DATABASE

DATABASE

DATA

DATA

INFO

INFO

Paper story and data story

STORYTOPIC

SOURCESELECTION

DATAINSPECTION AND

CLEANING

DATAANALYSIS

INSPECTION OFDATA ANALYSIS

PREPARATION,VISUALISATION

AND PUBBLISHING

INFORMATIONREQUEST

documentsource

humansource

journalisticobservation

data paperdocument

officialdatabase

opendata

internetpaperofficial data

editorialdatabase

data material non-data story material

Datajournalism working process

JOURNALISTICQUESTIONS DATA

DATA CLEANINGAND ANALYSIS

COMMENTSTO ANALYSIS

DATA STORY

PRE-DATA

POST-DATA

Data story process

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 andthree visiting professionals, who were specialists indata visualization and networks, open data, and datajournalism tools.

Data journalism strories

• The themes of the dj projects varied fromlocal traffic accidents and parking tickets tothe use of Fjällräven backpacks by students’ ofdifferent Faculties in the Uni.

Feedback from the pilot course

• Almost all the teams had project specificproblems concerning the finding of suitabledata sets.

• Many good story ideas were invalidated by alack of open data.

• In hindsight, the pilot course was possibly toointensive and more than two weeks should beallowed for developing a good data journalismproject.

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

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) 250followers

• Finnish Open Data Ecosystem (2172)

DJ 2014: 11 students started, but not asingle data story yet – WHY?

Main reason: Months delayes in gettingopen data from the City of Jyväskylä

Image From Wikimedia Commons

Main lesson learned this time

• Without data, there is no data story.

Minor setback: EJC’s MOOC on Data Journalism startedtoo late - on Monday (May 19th)

Important issues in teaching dj

• Journalism laboratory: piloting, testing of keyimportance

• Journalistic questions to be answered by data• Theory and practice combination –research

based

Important issues in teaching dj

• Know-how of data access and efficient datarequests 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?

In conclusion: Three levels of datajournalism (education)

• Basic level: General dj (for daily use, based onexisting open data sets, basic data tools)

• Advanced: Investigative dj (”what is in theshadows”, weeks-months of research, FOIrequests, programming skills)

• Real-time (sensor journalism, automatednews creation based on algorithms)

Thank you!Follow research on dj

#DaJoRe