Data Journalism - Article 14: First Day

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Eunice Powell
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1 Data Journalism Programme 7-8 May 2012 European Broadcasting Union, Geneva, Switzerland

2 Trainer Kevin Anderson is a journalist, journalism trainer and digital strategist. After starting his journalism career in newspapers, Kevin shifted to digital journalism in In 1998, he became the first online journalist for the BBC outside of the US, based in the BBC's flagship W ashington bureau. He joined The Guardian in 2006 as their first blogs editor. Two years later, he became digital research editor at the newspaper, helping identify and implement new technologies to support the newspaper's world-class journalism. Since March 2010, he has been an independent journalist and strategist. He works with journalists and news organisations around the world, including Al Jazeera and Reed Business Information, to help prepare them for the digital future. Objectives Review the fundamental skills you need both to plan and execute your stories based on data journalism. Get acquainted, through tutorials and practical exercises, with the tools needed to quickly " mine", analyse and visualize data. Get inspired by and learn from successful data journalism stories Monday 7 May During the first day of the course, we ll be working with pre-selected data sets. In day 2, w e ll be w orking w ith data sets that you have brought or have found. 13:00-13:30 M odule 1: What is data journalism? The history of data in journalism Computer-assisted reporting What is data journalism? o Tools and techniques that help you manage and make sense of small and large sets of data o Tools that help you uncover patterns o Tools and techniques that help you tell award-winning stories W hat s driving the data journalism revolution? o The rise of the data mash-up o Better, easier tools (both for analysis and visualisation) o The public data revolution o Crowdsourcing Discussion: W hat data projects are you currently working on? W hat projects would you like to do? 13: :45 W elcome Tour de table Introductions and expectations

Two years later, he became digital research editor at the newspaper, helping identify and implement new technologies to support the newspaper's world-class journalism.

3 13:45-14:15 M odule 2: Statistics: Common pitfalls and how to avoid them Data journalism requires an understanding of statistics. o Not your average average o Percentage abuse o Sample size o Dig deeper than national statistics 14:15-15:15 M odule 3: Spreadsheets: The data journalist s key tool Excel, Open Office, Zoho and Google Docs o Excel versus online spreadsheets o How to create an online spreadsheet and enter your data o Formatting data - dates, currencies and regional formats o Basic calculations o W eb-only features of online spreadsheets o More complex calculations o Choosing the right chart o How to create simple charts and graphs Exercise: Create a chart or charts using our sample data 15: :30 Coffee break 15:30-16:30 M odule 4: Other data tools: Google Fusion charts & Pivot Tables Google Fusion charts, a great tool for raw data o Exporting data from Google Spreadsheets and Fusion Tables. o The difference between Google Fusion Tables and Google Spreadsheets. o Uploading data into Google Spreadsheets and Google Fusion tables. o Raw data versus processed data. o Filters and aggregation in Google Fusion Tables. o How to identify dirty data Exercise: Look for other patterns in this data. W hat other aspects would you look at? What other patterns do you find? W hat questions did your analysis raise? Pivot tables: A powerful tool for analysing data Filters and aggregation in Fusion Tables are a simplified version of pivot table reports in spreadsheets. W hile aggregation in Fusion Tables allows you to easily analyse one dimension of a data set, pivot table reports allow you to o How to analyse data in multiple dimensions using pivot tables in Google Spreadsheets. o Pivot tables in Excel (and OpenOffice) Exercise: Using this set of raw data, use pivot tables to analyse the data. Think of different aspects of the data that you want to analyse.

Google Docs o Excel versus online spreadsheets o How to create an online spreadsheet and enter your data o Formatting data - dates, currencies and regional formats o Basic calculations o W eb-only

4 16:30-17:00 M odule 5: How to use data in your newsroom A look at how data desks are organised in newsrooms with examples of the Australian Broadcasting Corporation and The Guardian. Discussion: Does your news organisation currently have a data team? If you do projects using data, how are they currently done? 17:00-17:30 M odule 6: How to find your own data A look at Different data formats. National and European data sources. Generating data sets using Eurostat. Data search tips using Google. Exercise: Find two data sets which you will use for the rest of the course. At least one of the data sets much include location. 19:00 Netw orking dinner at the Café du Soleil, Petit-Saconnex, Geneva 21:30 End of Day 1 Tuesday 8 May In Day 2, we ll move on to advanced concepts and put some of the ideas from yesterday in action 09: :15 Review of Day 1 and objectives of Day 2 09:15-10:30 Module 7: Analysing data sets Is your data clean? W ill you need to focus on a certain area of your data? W ill you need pivot tables or can you simply use some basic analysis using a spreadsheet? What are the patterns in your data? W hat chart does your analysis lend it to? W hat reporting do you still need to do? W hat questions is your data asking? 10:30-11:30 M odule 8: Creating maps Batch Geo. An easy tool to map data. Zeemaps: Mapping multiple points with your data Google Fusion Tables powerful mapping tools. Exercise: Map the data set that you found yesterday.

17:00-17:30 M odule 6: How to find your own data A look at Different data formats. National and European data sources. Generating data sets using Eurostat. Data search tips using Google.

5 11: :45 Coffee break 11:45-12:30 M odule 9: Tableau Public: A power visualisation tool Tableau Public is a special tool for visualising data. o Opening data in Tableau Public. o Example 1: mapping earthquake data o Example 2: US closure data Creating a data dashboard. 12:30-13:00 Closing discussion: What projects are you now inspired to do? 13: :15 Wrap-up Key learning points Evaluation 13:15 End of the Master Class Contact Hélène Rauby-Matta Business Development Manager EBU TRAINING L Ancienne Route 17A 1218 Grand Saconnex GE Switzerland T F E raubymatta@ebu.ch

12:30-13:00 Closing discussion: What projects are you now inspired to do?

Slide 1 Investigative Reporting and Data Visualization at the high school level

Script: Investigative Reporting and Data Visualization Faculty member: Mike Jenner Slide 1 Investigative Reporting and Data Visualization at the high school level Slide 2 Investigative journalism can turn