Getting Started with Data Journalism


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Getting Started with Data Journalism

Writing data stories in any size newsroom

About the Book

Getting started with data journalism does not require huge resources. The basic skills can help you develop data stories in any size newsroom.

This book outlines the skills that have helped power award-winning journalism. The majority of the examples in this book are actual stories which have been run in a regional newspaper, giving an idea of the kind of content data journalism skills can be used to generate.

This book will teach you how to:

  • Source data for your projects
  • Clean messy data
  • Understand and analyse data
  • Find the story in your data
  • Create visualisations from maps to social interactives
  • Share this book

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    • Data Science
    • Writing and Publishing
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About the Author

Claire Miller
Claire Miller

Claire Miller is an award winning data journailist working for regional newspapers in the UK.

Her day to day job involves using data skills to dig out data, find stories, and create visualisations.

She runs the WalesOnline datastore (, a repository for the data and graphics relating to stories published in Media Wales newspapers. It was the first of its kind in regional newspapers and won the Digital Award at the Regional Press Awards. One of the stories on the number of children being placed in foster care outside Wales by Welsh councils, won a Data Journalism Award for investigative journalism.

Table of Contents

  • 1. Introduction
  • 2. Why Data Journalism
    • Data skills and traditional skills
    • Editorial planning for data
    • Data to story to resource
  • 3. Getting Started
    • What is data?
      • The basics of data files and formats
    • The basics of working with spreadsheets
  • 4. Sources of Data
    • Government Statistics
    • Written answers
    • Searching
    • Freedom of Information (FOI)
      • Making a request
      • FOI Tips
      • Who is covered in the England and Wales?
      • What if I do not get the information I want?
      • FOI Resources
    • Crowdsourcing
    • Scraping
      • Very simple scraping
      • Using Open Refine as a scraper
      • ScraperWiki
      • Scraping resources
  • 5. Understanding what the numbers might mean
    • Who gathered the data?
    • Watch out for small numbers and rare events
    • Consider how reliable your data is
    • What are the long-term trends?
    • Don’t cherry-pick your data
    • Be careful of what the numbers mean
  • 6. Cleaning Data
    • Bringing data together
    • Getting data out of PDFs
    • Tidying up messy spreadsheets
    • Data on websites
    • Lots of abbreviations
    • Different words, all meaning the same thing
  • 7. Getting the story out of the data
    • Simple maths
    • Spreadsheet Basics
      • Doing Sums
      • Formatting Cells
      • Using Formulas
      • Sorting
      • Filtering
    • Correlations
    • Pivot Tables
    • Data Mashups
  • 8. Telling a Story with Visualisations
    • Choosing the right visualisation
      • Tables
      • Graphs and Charts
      • Maps
      • Infographics
    • Some other things to remember
  • 9. Visualising Data - Graphs and Charts
    • Google Charts
    • Tableau
      • Getting Started
      • Bar Charts
      • Line Graphs
      • Dashboards
      • Filtering
      • Tooltips
      • Annotations and Reference Lines
      • Pages
      • Live updating graphs
  • 10. Visualising Data -Maps
    • Simple maps
    • Mapping with Tableau
    • Heat Maps
      • Openheatmap
      • Google Fusion Tables
      • Layered Maps
      • Real-time Maps
  • 11. Visualising Data - More Complex Visualisations
    • News Apps
      • Actions
    • Social interactives
  • 12. Conclusion
    • Where Next?
      • Data Analysis
      • Visualisation
      • Coding
  • 13. Appendix 1 - Full case studies
    • Bookies in Wales
      • Finding data on bookies
      • Making simple maps
      • Bringing together bookies and deprivation
      • Analysing your data
      • Making a map to show the correlation
    • Children in Care
      • Gathering data
      • Tidier data
      • Making a map
    • Parking Tickets
      • Cleaning untidy data
      • Cleaning untidier data
      • Cleaning untidiest data
      • Geolocating the parking tickets
      • Visualising your data
    • Road accidents and casualties
      • Merging the data
      • Cleaning the data
      • Visualising the data
    • School Spending on Temporary Staff
      • Using pivot tables to clean data
      • Merging data for better analysis
      • Analysing the data
    • South Wales Police Helicopter
      • Scraping
      • Tidying up unstructured data
      • Mapping your data
  • 14. Appendix 2 - Useful Links
    • Data journalism
    • Finding Data
    • Cleaning Data
    • Visualisation

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