Your SlideShare is downloading. ×
0
Data Journalism 101
(Part II)

Donald W. Reynolds National Center for Business
Journalism at ASU
Michael J. Berens – The S...
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 digit...
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.
• ...
Basic Fields
• 

License #

• 

Name

• 

Occupation

• 

Offense type

• 

Dates of action

• 

Sexual misconduct; unprof...
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:/...
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 ...
Elephants and zoos
Adult family homes
The federal government has launched a grant
program that pays states to relocate seniors.
They call it “rebalancing.”

Pol...
Develop your nose for data
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Data Journalism 101 - Day 2 by Michael J. Berens
Upcoming SlideShare
Loading in...5
×

Data Journalism 101 - Day 2 by Michael J. Berens

536

Published on

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

0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total Views
536
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
32
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

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

  1. 1. Data Journalism 101 (Part II) Donald W. Reynolds National Center for Business Journalism at ASU Michael J. Berens – The Seattle Times
  2. 2. Meet my editor – a guy who thought a special project was something that took two hours instead of one
  3. 3. 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
  4. 4. Poll Question: Have you ever created a database from scratch?
  5. 5. Every database begins with a single cell
  6. 6. Cells, fields and headers, oh my
  7. 7. Segregation is good Address number from street name Middle initial from first name First name from last name
  8. 8. Basic fields
  9. 9. Bad Sorry, dude
  10. 10. Good Not bad, grasshopper
  11. 11. Better Brilliant. You rock!
  12. 12. 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)
  13. 13. Basic Fields •  License # •  Name •  Occupation •  Offense type •  Dates of action •  Sexual misconduct; unprofessional conduct; moral turpitude
  14. 14. Paper to Excel
  15. 15. Paper to Excel
  16. 16. Poll Question: What is your suggestion for a unique field?
  17. 17. Tapping the power of Excel •  Sorting •  Filtering •  Basic calculations
  18. 18. Pick the column
  19. 19. Alphabetized by name
  20. 20. Filtering
  21. 21. Chevron marks
  22. 22. Filtered by profession
  23. 23. Calculations •  Always begins with an equal sign •  Basic math structure using names of cells •  =A1+A2
  24. 24. 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/
  25. 25. Keep the trash – everything has value
  26. 26. Look for signature cases
  27. 27. The strategy •  Get the basic data •  Get the basic files •  Create a spreadsheet – add on categories •  Dive deeper for paper records – understand the system
  28. 28. Elephants and zoos
  29. 29. Adult family homes
  30. 30. 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?
  31. 31. Develop your nose for data
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×