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. 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. 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. JOURNALISTIC
QUESTIONS
DATA
DATA CLEANING
AND ANALYSIS
COMMENTS
TO ANALYSIS
DATA STORY
PRE-DATA
POST-DATA
Data story process

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. 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.

8. 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.

9. 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

10. 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)

11. DJ 2014: 11 students started, but not a
single data story yet – WHY?

12. Main reason: Months delayes in getting
open data from the City of Jyväskylä
Image From Wikimedia Commons

13. Main lesson learned this time
• Without data, there is no data story.

14. Minor setback: EJC’s MOOC on Data Journalism started
too late - on Monday (May 19th)

15. 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

16. 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?

17. 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)

18. Thank you!
Follow research on dj
#DaJoRe