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On August 31st DataMeet in conjunction with Jayadevan from Economic Times and the School of Data held an introduction to Data Journalism workshop.

Here are the notes:

Notes from Data Journalism Workshop

What is data journalism? Group discussion (10 am - 10.30 am) :

  • Bringing data for the masses by giving context to data and writing stories that people can relate to.
  • Taking insights from data - through analysis starting with a story
  • Meaningful infographics
  • visually gripping data

Global and Indian Context for Data Journalism: Jayadevan's session (10.30 am to 11.30 am)

  • New digital era
  • Print media is on a decline, off late.
  • New media organizations emerging, mostly digital and online media.
  • Internalization of media
  • Global media houses like Bloomberg, New York Times, Business Insider pushing country specific pages.
  • Share economy, ready to share data sets and interchange and exchange data.
  • Example: Indian election was covered by World Media
  • India has favorable demography for digital journalism.
  • Data is important part of any news.
  • Generalization of media houses is dead. Everyone is doing everything.
  • Shorter news cycle. Unlike Print media days
  • Social Media remains the prime distribution Platform.
  • Digital journalism has shorter turn around time. Things go viral or die out.
  • Aggregating data is imp for effective story.
  • Need to leverage power of dynamic data presentation when reporting on the web.
  • Unlike just scanning the print stuff to the web.
  • Dynamic graphs and interactive maps are way to go for effective presentation on the web.

Good and Bad Data Journalism: Thej's session (11.30 am - 12.15 pm)

See examples here

  • Example of good data story, What makes a good data story? Story, InfoGraphs or Conclusions?
  • Nightingale's visualization on number of deaths of soldiers due to poor conditions- Eye opening conclusions. A trend emerging from a story narrated with data in a map.
  • Propublica's “Hidden story behind redistricting of constituencies have a corporate hand behind them”- Explaining cause and effect with data, Power of an interactive map compared over a time.
  • Climate change visuals - Less data very very few words but really beautiful visuals also does equally great job.
  • The Hindu story about ongoing sexual assault cases and their resolution in New Delhi. - Power of reporting data and explaining it in the followup stories.
  • Mapping access to toilets according to the social groups. Using maps to tell powerful stories
  • Gun control in America by state. - Creative presentations and comparisons. Putting lot of information effectively.
  • Great story needs great data and even greater presentation.
  • Bad examples of data stories: The Globe and The Daily Mail,gun laws in the state, Fox job loss, Gallup LGBT Percentage, Health assessment
  • Choosing right scales, colors and graphs important for effective presentation of data.
  • Tools for data analysis and data visualization.
  • Map, time and place is a important context building medium in data presentation.
  • Choice of color contrast, scale and percentage representation is most important. Think through before visualizing.

Sources of Data: Nisha's Session (12.15 pm to 12.45 pm):

  • Data bill not as powerful as RTI, And why?
  • Demand driven data sets on the website, Neat visualizations, updated data.
  • Easy to download in import friendly format.
  • Other sources of government data:,,
  • Newsletter from the Ministry of Health is released weekly. Pretty good, demographically spread.
  • Datameet data catalogue, OpenCorporates, WorldBank is a great resource.
  • Data laws in India is a grey area.
  • Data cannot be copyrighted and but the process can be.
  • Lookout for the copyright associated and licenses when picking up data for a story.
  • Government data is best to start with, Do report the source department. But careful with certain organizations/department like ISRO
  • Technically, drawing maps in India is not allowed. Only Survey of India can do.
  • Always verify the authenticity of data before reporting. Hence Gov data is generally preferred.
  • Report the source, credits, references, place and dates.
  • Seek permission if the data is outside creative commons or belongs to a private firm.
  • – collection of recent the court cases (Post 1985) and ability to search.

12:45 to 1:15 LUNCH

Tableau Demo: Nisha's session: (1.15pm-1.45pm)

  • Introduction to Tableau Public, Data visualization tool.
  • Import your dataset and drag-drop to the dashboard.
  • Formula free and a great first level analysis tool.
  • Used by Business community for research, Social sector, Journalists and freelancers.
  • Similar to Infogram but more functional and powerful.
  • Gets public when you save it.

CartoDB Demo: Thej's Session (1.45pm- 2:45pm)

  • Creating beautiful maps with your data.
  • Example of BMTC bus stop density visualizing with CartoDB with maps.
  • Introduction to Visuals, Data view and Map view.
  • Introduction 'Table to clipboard' firefox/ chrome plugin for moving data from a web table(s) to excel sheet in a clean way.
  • Introduction to 'iMacros' firefox/chrome plugin to record a macro and perform a repetitive operation.
  • Twitter hashtag interactive maps using CartoDB
  • Introduction to ScraperWiki - extract data from web pages and pdf.
  • Introduction to Mapbox by TileMill beta for creating interacting maps.
  • Plans about Free PDF event
  • Used by cartographers for creating interactive maps.
  • Intro to QGIS tool

Chai break (2.45pm-3:00pm)

Visualization Roadmap: Nisha's Session (3:00pm- 4:00pm)

  • Think of a story.
  • Gather the stats.
  • Mine dataset related to it.
  • Narrow down the Audience : web/print.
  • Language of the article.
  • Thinking of Personas while drafting.
  • Creating visuals for it, Keep it simple.
  • Pick relevant scales : Country/Regional.
  • Validation of the finding and the overall article.
  • Case studies discussed in class:
  • Petroleum import from Iran.
  • Water and Garbage data for new buildings in Whitefield and Mahadevapura
  • Tree Planting data in Bangalore

Sharing tools from the Visualization Roadmap Session: Thej's Session (4:00pm- 4.30pm)

  • Introduction to Bhuvan, India's Remote sensing Portal
  • Analysing increase in area for Bangalore Map from 2004 to 2014.
  • Concept for Map overlay
  • Introduction to Timeline - Beautifully crafted timeline
  • Making a TimeLine' two types one with Time and events other with Time, places and events.
  • Introduction to StoryMap - Map that tell stories.
  • Public api's from
  • Odyssey, Nice data visualization tool.
  • Introduction to Fusion Tables - Google's version of Tableau
  • Case study of Bangalore Urban Metropolitan Project (BUMP).
  • Resource and data rich portal for research and digging stories.

Feedback and Survey: 4.30 pm -4:45 pm

  • Split it in two days
  • Hands on with tools.
  • Audience should come prepared with a specific problem or story.
notesfromdatajournalismworkshop1.txt · Last modified: 2015/03/16 23:37 by thejeshgn