Data Journalism 101 - Day 2 by Michael J. Berens
Upcoming SlideShare
Loading in...5
×
 

Data Journalism 101 - Day 2 by Michael J. Berens

on

  • 557 views

Michael J. Berens presents the final part of the free, two-day webinar, "Data Journalism 101," hosted by the Donald W. Reynolds National Center for Business Journalism. ...

Michael J. Berens presents the final part of the free, two-day webinar, "Data Journalism 101," hosted by the Donald W. Reynolds National Center for Business Journalism.

For access to the webinar materials, visit http://bit.ly/datajourn101.

For more information about training for business journalists, please visit http://businessjournalism.org

Statistics

Views

Total Views
557
Views on SlideShare
557
Embed Views
0

Actions

Likes
0
Downloads
31
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Data Journalism 101 - Day 2 by Michael J. Berens Data Journalism 101 - Day 2 by Michael J. Berens Presentation Transcript

    • Data Journalism 101 (Part II) Donald W. Reynolds National Center for Business Journalism at ASU Michael J. Berens – The Seattle Times
    • Meet my editor – a guy who thought a special project was something that took two hours instead of one
    • Database types •  Obtained from a public agency or other institutional source (Part I)] •  Scrapped from the web or digital document – copy and paste (Part I) •  Created from scratch using any mixture of paper records •  Hybrid data analysis – layering existing data with your own database
    • Poll Question: Have you ever created a database from scratch?
    • Every database begins with a single cell
    • Cells, fields and headers, oh my
    • Segregation is good Address number from street name Middle initial from first name First name from last name
    • Basic fields
    • Bad Sorry, dude
    • Good Not bad, grasshopper
    • Better Brilliant. You rock!
    • Hiding in plain sight •  A health care professional was administratively charged with sexual misconduct with patients. •  His punishment? •  He was only allowed to treat women age 50 or older (re: public record posted on Wa. Dept. of Health website)
    • Basic Fields •  License # •  Name •  Occupation •  Offense type •  Dates of action •  Sexual misconduct; unprofessional conduct; moral turpitude
    • Paper to Excel
    • Paper to Excel
    • Poll Question: What is your suggestion for a unique field?
    • Tapping the power of Excel •  Sorting •  Filtering •  Basic calculations
    • Pick the column
    • Alphabetized by name
    • Filtering
    • Chevron marks
    • Filtered by profession
    • Calculations •  Always begins with an equal sign •  Basic math structure using names of cells •  =A1+A2
    • Data training resources Investigative Reporters and Editors: www.ire.org http://www.ire.org/nicar/ Reynolds Center http://businessjournalism.org http://businessjournalism.org/registration/llc/
    • Keep the trash – everything has value
    • Look for signature cases
    • The strategy •  Get the basic data •  Get the basic files •  Create a spreadsheet – add on categories •  Dive deeper for paper records – understand the system
    • Elephants and zoos
    • Adult family homes
    • The federal government has launched a grant program that pays states to relocate seniors. They call it “rebalancing.” Poll Question: What would you do with this information?
    • Develop your nose for data