COMM 273D | Fall 2014

Thursday, December 4

Project Show-N-Tell

In-class presentations of our final data projects.

Bring your ready-to-talk about projects to share with the class.

A writeup of the final project requirements.

Have you been faithfully recording time-based events related to your story but are looking for ways to visualize them? Try out the Knight Lab's TimelineJS, which lets you turn a Google Spreadsheet into an embeddable interactive timeline, like so:

1033 Redux

Remember the 1033 Program data that we looked at for the midterm? If you recall correctly, it revealed the distribution of military surplus only at the county level, but not which agencies actually got them. That's recently changed, partly in thanks to the persistent records-requesting of MuckRock. You can get the raw data from the Pentagon here.

Check out how The Marshall Project wrote a data story about it: The Pentagon Finally Details its Weapons-for-Cops Giveaway.

It works as a traditional story, but it dives deep into the details of the data:

You probably have not heard about some of the more obscure beneficiaries of the Pentagon giveaway: This piece was reported and written by Tom Meagher and Gabriel Dance for The Marshall Project and by Shawn Musgrave of MuckRock, an independent investigative news site. Subscribe to MuckRock's newsletter, or follow it on Twitter.

  • Police in Johnston, R.I., with a population less than 29,000, acquired two bomb disposal robots, 10 tactical trucks, 35 assault rifles, more than 100 infrared gun sights and two pairs of footwear designed to protect against explosive mines. The Johnson police department has 67 sworn officers.
  • The parks division of Delaware’s Department of Natural Resources was given 20 M-16 rifles, while the fish and wildlife enforcement division obtained another 20 M-16s, plus eight M-14 rifles and ten .45-caliber automatic pistols.
  • Campus police at the University of Louisiana, Monroe, received 12 M-16s to help protect the 8,811 students there (or perhaps to keep them in line).
  • The warden service of Maine’s Department of Inland Fisheries and Wildlife received a small aircraft, 96 night vision goggles, 67 gun sights and seven M-14 rifles.

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The first visualization, a column-chart, is not fancy, but it gets the point across. And the second is basically a giant table. But in my opinion, this works, and is a great model for combining data-reporting with the traditional narrative article.

Course schedule

  • Tuesday, September 23

    The singular of data is anecdote

    An introduction to public affairs reporting and the core skills of using data to find and tell important stories.
    • Count something interesting
    • Make friends with math
    • The joy of text
    • How to do a data project
  • Thursday, September 25

    Bad big data

    Just because it's data doesn't make it right. But even when all the available data is flawed, we can get closer to the truth with mathematical reasoning and the ability to make comparisons, small and wide.
    • Fighting bad data with bad data
    • Baltimore's declining rape statistics
    • FBI crime reporting
    • The Uber effect on drunk driving
    • Pivot tables
  • Tuesday, September 30

    DIY Databases

    Learn how to take data in your own hands. There are two kinds of databases: the kind someone else has made, and the kind you have to make yourself.
    • The importance of spreadsheets
    • Counting murders
    • Making calls
    • A crowdsourced spreadsheet
  • Thursday, October 2

    Data in the newsroom

    Phillip Reese of the Sacramento Bee will discuss how he uses data in his investigative reporting projects.
    • Phillip Reese speaks
  • Tuesday, October 7

    The points of maps

    Mapping can be a dramatic way to connect data to where readers are and to what they recognize.
    • Why maps work
    • Why maps don't work
    • Introduction to Fusion Tables and TileMill
  • Thursday, October 9

    The shapes of maps

    A continuation of learning mapping tools, with a focus on borders and shapes
    • Working with KML files
    • Intensity maps
    • Visual joins and intersections
  • The first in several sessions on learning SQL for the exploration of large datasets.
    • MySQL / SQLite
    • Select, group, and aggregate
    • Where conditionals
    • SFPD reports of larceny, narcotics, and prostitution
    • Babies, and what we name them
  • Thursday, October 16

    A needle in multiple haystacks

    The ability to join different datasets is one of the most direct ways to find stories that have been overlooked.
    • Inner joins
    • One-to-one relationships
    • Our politicians and what they tweet
  • Tuesday, October 21

    Haystacks without needles

    Sometimes, what's missing is more important than what's there. We will cover more complex join logic to find what's missing from related datasets.
    • Left joins
    • NULL values
    • Which Congressmembers like Ellen Degeneres?
  • A casual midterm covering the range of data analysis and programming skills acquired so far.
    • A midterm on SQL and data
    • Data on military surplus distributed to U.S. counties
    • U.S. Census QuickFacts
  • Tuesday, October 28

    Campaign Cash Check

    The American democratic process generates loads of interesting data and insights for us to examine, including who is financing political campaigns.
    • Polling and pollsters
    • Following the campaign finance money
    • Competitive U.S. Senate races
  • Thursday, October 30

    Predicting the elections

    With Election Day coming up, we examine the practices of polling as a way to understand various scenarios of statistical bias and error.
    • Statistical significance
    • Poll reliability
    • Forecasting
  • Tuesday, November 4

    Election day (No class)

    Do your on-the-ground reporting
    • No class because of Election Day Coverage
  • While there are many tools and techniques for building data graphics, there is no magic visualization tool that will make a non-story worth telling.
    • Review of the midterm
    • The importance of good data in visualizations
    • How visualization can augment the Serial podcast
  • Tuesday, November 11

    Dirty data, cleaned dirt cheap

    One of the most tedious but important parts of data analysis is just cleaning and organizing the data. Being a good "data janitor" lets you spend more time on the more fun parts of journalism.
    • Dirty data
    • OpenRefine
    • Clustering
  • Thursday, November 13

    Guest speaker: Simon Rogers

    Simon Rogers, data editor at Twitter, talks about his work, how Twitter reflects how communities talk to each other, and the general role of data journalism.
    • Ellen, World Cup, and other masses of Twitter data
  • Tuesday, November 18

    What we say and what we do

    When the data doesn't directly reveal something obvious, we must consider what its structure and its metadata implies.
    • Proxy variables
    • Thanks Google for figuring out my commute
    • How racist are we, really?
    • How web sites measure us
  • Thursday, November 20

    Project prep and discussion

    Discussion of final projects before the Thanksgiving break.
  • Tuesday, November 25

    Thanksgiving break

    Holiday - no class
  • Thursday, November 27

    Thanksgiving break

    Holiday - no class
  • Tuesday, December 2

    Project wrapup

    Last-minute help on final projects.
  • Thursday, December 4

    Project Show-N-Tell

    In-class presentations of our final data projects.