Data Journalism
Overview
This course teaches some of the skills and techniques necessary for obtaining, analyzing and communicating structured information effectively. Students will scrutinize techniques used in previously published projects and will also analyze data on their own, primarily using Excel, HTML, CSS and Javascript (and JS libraries like D3), and, in some cases, command-line tools.
Most classes will begin with a short lecture and discussion about a topic, technique or published piece of journalism that used data journalism.
This will be a technical course, but the primary goal of the course is journalistic; we hope not to get too bogged down in the fiddly bits of the latest technologies. We'll try to demystify the technological side of things so you feel comfortable getting started and thinking programmatically. By the end of the course, you should be able to ask good questions of datasets, get answers from them, and communicate them effectively to a general audience.
Class Schedule
Class meets on Wednesdays from 6:30 to 10 p.m. Kevin and Amanda will also have weekly online office hours (time TBD).
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Jan. 29
Getting started: configuring your computers, getting software, hacking 101
Overview of why we’re all here and getting up to speed with github, the terminal, and all the softare you'll need. We'll also show how you almost never need to start from scratch. -
Feb 5
Asking data questions: sort, filter, aggregate, merge
These four skills are 98% of newsroom data analysis. -
Feb. 12
Finding hidden data and thinking like a robot
Your web browser knows where the data is if you know where to look. -
Feb. 19
Putting the pieces together, interviewing data
What we've done so far adds up to something + adding some journalism. -
Feb. 26
Aggregating, a way to summarize data
We pick up skill 4/4 on our data analysis game hunt. -
March 5
"This is the job." -Lester Freamon
What your job will really look like. Also, we finish our bold plans for graduation rates. -
March 19
Walking the walk
It's easy to critique. It's hard to program. -
March 26
Charts
Bar charts and line charts are easy and fun. -
April 2
Maps and more
You don't need to be a cartographer, but you should know about maps. -
April 9
Project help
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April 16
Project help
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April 23
Project Help
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April 30
Presentations
Useful links
Your Professors
Kevin Quealy — Bald, Minnesotan, talks too much.
Amanda Cox — Sometimes known as the Godfather.
Shadow Professor
Shan Carter — Developed data viz curriculum in 2013; swell guy