Like any good website, this schedule is responsive. I will be adapting it throughout the semester to match with the skills and needs of the class.
Week 1
R – Introductions
Week 2 (Jan 14 & 16)
T- What is data journalism?
Required Readings
- A fundamental way newspaper sites have to change by Adrian Holovaty (@adrianholovaty)
- Writing code for journalism: Why I love what I do by Ryan Pitts (@ryanpitts)
- What do journalists do with documents? The different kinds of document-driven stories by Jonathan Stray (@jonathanstray)
Optional Readings
- A guide to computer-assisted reporting by Pat Stith via the Poynter Institute (@poynter)
- How to be a data journalist by Paul Bradshaw (@paulbradshaw)
Assignments
- Find and post three data-driven stories that you fins interesting. Be prepare to describe the story and its strengths and weaknesses. In your post include a link to each story, a brief description of each story, and appropriate screenshots.
R – More examples and discussion of project
Required Readings
- How the Sun Sentinel reported its Pulitzer Prize winning coverage of off-duty cops - part of IRE‘s behind the story series
- Simple math reveals errors in lucrative speed camera system – part of IRE’s behind the story series
- How Netflix reverse engineered Hollywood by Alexis Madrigal (@alexismadrigal)
- Want to build a data journalism team? You’ll need these three people by the Knight Journalism Lab
Optional Readings
- Homicide Watch: An interview from Contents Magazine
- Public info doesn’t alway want to be free - a good read on the ethics of Tampa Bay Mugshots by Matt Waite
- More articles from the behind the story series
Assignments
- Start thinking about your semester long project. You should be looking for a substantially large project, which could be published by a local or regional news organization.
Week 3 (Jan 21 & 23)
T – Math for journalists
Required Readings
- Statistics every writer should know by Robert Niles (@robertniles)
- Statistical terms used in research studies; a primer for media from the Journalist’s Resource (@journoresource)
Optional Readings
- Why math matters from the Poynter Institute (@poynter)
Assignments
- Complete Poynter’s NewsU training “Math for Journalists.” Print out the completion/congrats your done page and bring it to class on Tuesday.
R – Pitch meeting
Required Readings
- 6 questions journalists should be able to answer before pitching a story from the Poynter Institute (@poynter)
Assignments
- Come to class prepared to present your team’s idea for your semester long project. Read the Poynter piece above to understand the level of research you should do before Thursday.
Week 4 (Jan 28 & 30)
T – Finding data
Required Readings
- Read Ch. 4 in The Data Journalism Handbook
- Finding local VA disability claim data by Shane Shifflett (@shaneshifflett)
R – Using Excel for data analysis
Required Readings
- Using Excel to do precision journalism by Steve Doig (@sdoig)
- My favorite (Excel) things by MaryJo Webster (@mndatamine)
Optional Readings
- How journalists can use Excel to organize data for stories by Joshua Hatch (@hatchjt) at Poynter
Assignments
The KnoxNews is doing a story about the popularity of Peyton as a baby name. They have asked us to find some numbers to support their story. Find the baby names databases available from the Social Security Administration via Data.gov complete the following tasks.
- How many boys and girls were named Peyton in the state of Tennessee in each year from 1984 to 2012?
- What was the average for boy named Peyton before and after Peyton graduated from UT?
- Where did Peyton rank nationally as a boys name from 1998 to 2012?
Finally, they have asked us to do a sidebar story about baby names in Tennessee. Browse the database and try to find a story.
Week 5 (Feb 4 & 6)
T – Using Google Refine to clean and analyze data
Required Readings
- Ch. 1: Using Google Refine to clean messy data by Dan Nguyen (@dancow) from ProPublica
- Cleaning data using Google Refine: A quick guide by Paul Bradshaw
R – Data plans
Assignments
Come to class prepared to discuss your data plan.
Week 6 (Feb 11 & 13)
T – Writing about data
Required Readings
- Drawing conclusions from data by Jonathan Stray (@jonathanstray)
- How to: Correctly report numbers in the news from Journalism.co.uk
R – Workshop
Week 7 (Feb 18 & 20)
T – Introduction to Web Design
R – Scraping with Google Docs
Required Readings
- Watch Ch. 1 & 2 from Don’t Fear the Internet - These are short, simple videos that explain how the Internet works
- Read Ch. 2 – 4 from Scraping for Journalists by Paul Bradshaw
Week 8 (Feb 25 & 27)
T – Scraping with Outwit Hub
R – Workshop
Week 9 (March 4 & 6)
T – Scraping Assignment
R – Intro to data visualization
Week 10 (March 11 & 13)
T – Mapping
R – Mapping cont and storyboards due
Week 11 (March 18 & 20)
Spring Break! No Classes.
Week 12 (March 25 & 27)
T – Basic web design
R - Online databases
Week 13 (April 1 & 3)
T – Introduction to charts
R – Charts with Google
Week 14 (April 8 & 10)
T – Charts with Illustrator
R – Charts with D3.js
Week 15 (April 15 & 17)
T – Mapping (basic)
R – Mapping with KML files
Week 16 (April 22 & 24)
T – Class critique
R – Closing thoughts