Data Journalism Heist cover page

Data Journalism Heist

Data Journalism Heist

How to get in, get the data, and get the story out - and make sure nobody gets hurt

Data Journalism Heist Edit


About the Book

Data journalism is a key skill for journalists to differentiate themselves in a world where almost anyone can publish, and competition for journalism jobs is fierce.

Whether it's hard stories from government spending and MPs' expenses, or softer stories from sports data, fashion trends or music and social activity, our increasingly digital world is providing a rich range of potential new story sources - and new forms of storytelling too.

This short ebook introduces you quickly to key techniques in finding that data and turning it into stories - through a 'Data Journalism Heist'.

This isn't about the huge investigative projects that you hear about, but the everyday stories that you can do with speed and simplicity. It's about getting in, getting the data, and getting the story out safely. No one gets hurt.

In the process you'll learn about:

  • Sources of data - where to find data stories and leads
  • Typical data stories - how to find simple stories in data
  • Basic spreadsheet techniques - finding the biggest and smallest values; calculating averages and totals.
  • Top techniques for getting stories against a deadline - using filters and pivot tables to get to the story quickly
  • Making a clean getaway - avoiding mistakes in data journalism
  • Telling the story - basic techniques in visualising and humanising your data-led story

As the book is published you'll receive regular updates as you build on previous skills towards the final story. User feedback, examples and ideas will be incorporated as they come in.

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Table of Contents

  • Huh?
  • The scouting mission: where’s the data?
    • Scouting a local government website
  • Getting in
    • The crowbar of data journalism: pivot tables
    • Who might your story be about?
    • What about that who might we want to find out?
    • Advanced filters - a dry run
    • Advanced filters - using wildcards
  • Getting (the data) out
  • The Debrief
    • No one gets hurt
    • Why does it matter - why do I care?

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About the Author

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