teaching data journalism by andreas veglis - milan 2015

Teaching Data Journalism in

the School of Journalism &

MC-Greece

Andreas Veglis – Professor

Media Informatics Lab School of

Journalism & Mass Communication

Aristotle University of Thessaloniki

Greece

Media Informatics Lab – School of Journalism & MC

• Very rapid advanced of Information and Communication technologies

• Digitalization of data

• Digital data processing, storing, distribution

• Continuous production of new data

• Ability to find data on the internet

Abundance of digital data

Suitable conditions for the introduction

of Data Journalism

• Kind of Journalism that is

conducted with the help of

• Can allow a journalist to

communicate a complicated

story with the help of

visualizations.

Defining Data Journalism……

• Data Journalism, Computer-Assisted Reporting,

Computational Journalism, Data-driven Journalism .

• Journalism done with data.

• «Data can be the source of data journalism, or it can be the

tool with which the story is told — or it can be both. Paul

Bradshaw, Birmingham City University

• Only finding interesting data does not constitute data

journalism – for example the case of Wikileaks.

Data Journalism Definition

• Data Journalism is the process of extracting useful information from

data, writing articles based on the information and embedding

visualizations (interacting in some cases) in the articles that help

readers understand the significant of the story or allow them to

pinpoint data that relate to them.

Data Journalism - example

• http://schoolofdata.okfn.g

r/2014/05/07/european-

student-mobility-2001-

Why Journalists have to work with data?

• News stories from multiple

sources.

• The combination of various

news allow the whole

picture of an event.

• Data: small pieces of

information, unrelated at first

glance.

• Journalists should approach

data as a chance to find new

stories.

Data Journalism in Guardian, 1821

Mortality in the British army (1856)

Stages of data Journalism

Data Compilation

Data Cleaning

DataUnderstanding

Data Validation

Data Visualization

Article Writing

Data compilation

• may be supplied directly by an organization,

• may be found with the help of advanced searching

techniques,

• may be compiled by scraping web pages,

• may be collected by converting documents to other

formats that can be analyzed, and

• may be collected by means of observation, surveys,

online forms or crowdsourcing.

Data Cleaning (Scrubbing)

• The process of detecting and correcting (or removing)

corrupted or inaccurate records from a dataset

• Forms of Cleaning:

– removing human errors and

– converting the data into a format that is consistent with other

data the journalist is using.

• Cleaning methods:

– using find and replace commands or filters on spreadsheets

– Using specialized tools, like Google’s OpenRefine.

Data Understanding

• Data not easy to be understood.

• Further data is needed in order for existing data to

become meaningful.

• Journalists ought to be data-literate. They must have the

ability to:

– consume knowledge, produce coherently and think critically

about data.

– understand statistics and how to work with large datasets, how

they were produced, how to connect various datasets and how

to interpret them.

Data Validation

• Cross-checking data, obtaining additional data.

• Data cannot always be trusted.

Data Visualization

• It is the graphical display of abstract information for data analysis

and communication purposes

• The visualization can be static or it can be interactive.

• There is a user input and the changes made by the user must be

incorporated into the visualization in a timely manner.

• Infographics graphic visual representations of data or

knowledge, which are able to present complex information quickly

and clearly .

Article Writing

• May include special characteristics:

– external links to other articles or related material,

– multimedia content,

– mashups,

– static or interactive visualizations.

Types of Data Journalism

• By just the facts

• Data-based news stories

• Local data telling stories

• Analysis and background

• Deep-dive investigations

Open data sets to be used in Data Journalism

projects

• Easy to find

• Standard format

• Easy to use or re-use

• Specific usage licenses

Linked data

• Easy to acquire relevant data

• Way to verify the data

Open & Linked Data

Chicago Tribune

Datablog της Guardian

Ukraine’s election results 2012

Language communities of Twitter

Necessary skills for Data Journalism

• Finding, compiling data.

• Cleaning data.

• Understanding and combining data.

• Validating data

• Visualizing data

Data Journalism Courses in the School

of Journalism and MC

• BA Program – elective course – Spring Semester .

• ΜΑ in Digital Media, Communication and Journalism -

European Journalism elective course – Spring Semester.

• Life-long learning for professional journalists Spring 2016

Data Journalism Course structure

• Introduction to Data Journalism – Defining Data Journalism – Historical

evolution.

• Data Journalism teams – Case studies – Transforming data to stories

• Searching for data sets – Searching techniques

• Law for data and data sources.

• Data scraping –using Google Spreadsheet.

• Basic statistics for Journalists – Data classification and data filtering

• Pivot Tables

• Working with messy data – cleaning and filtering

• Data Visualization – choosing the suitable visualization type – examples

• Data Visualization tools (Google Spreadsheet, Google Fusion Tables,

Tableau, Data wrapper, many eyes, Infogram)

Course Evaluation

• Lab exercises during the lessons (50%)

• Final Data project (50%)

Data Journalism web site

• http://datajournalism.jour.auth.gr

Greek version of the Data journalism

handbook

• http://data-journalism.okfn.gr/handbook/

• http://iwrite.gr/bookstore/the-data-

journalism-handbook/

Information Validation

• Data from various sources

• Are they valid?

• Validation process

Verification Handbook

http://verificationhandbook.com/

Verification Handbook

http://verificationhandbook.com/

Thank you for your attention

Andreas Veglis – professor

E-mail: veglis@jour.auth.gr

Webpage: http://blogs.auth.gr/veglis

Twitter: @veglis

teaching data journalism by andreas veglis - milan 2015

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