1) Read these Lost Stories stories:
2) Choose 2-3 of these stories, and in the comments, leave some thoughts on what you think works and what questions you have about the stories — the focus, approach, the voice, etc. Think about our conversation about the fire-behind-football-game story.
3) Look over the Birmingham News photos in Dropbox. (Captions are in a Word doc at the bottom of the page.) Come to class Tuesday with your top three. In the meantime, if you feel strongly about one (or more) of these, email me and let me know why.
4) Look over the schedule for the Lost Stories project and let me know if you have any questions about the deadlines. We will talk to Elizabeth at AMG next week about the engagement/distribution plan.
Read Fire Raged, They Played On, and the Photo Still Beguiles (in the NYT) and keep a list in a Google Doc of everything the reporter, Sarah Lyall, had to report in order to tell the story behind the photo. Before the start of class, share your Google Doc with me.
In the comments, leave some thoughts on what you think works and what questions you have about the stories — the focus, approach, the voice, etc.
1) Bookmark jn430.ua.edu. This is where I will post all of your assignments and where we will work collaboratively to discuss reading and projects.
2) Please answer this short questionnaire. Thanks!
1) Read Clara Guibourg’s 4 Mistakes in Data Journalism and How to Avoid Them and review Become Data Literate in 3 Simple Steps from the Data Journalism Handbook.
2) For your quiz, Make a copy of the spreadsheet of responses from the spring break survey we made and distributed. Using formulas in the spreadsheet itself or Fusion Tables, answer these questions for your third quiz. The quiz must be completed by 11:59 p.m. on Monday, March 28, 2016.
Over the break, please read Kathryn Schulz’ The Really Big One (sorry, people headed to the West Coast). It’s a great piece of journalism, full stop, but it’s also a great example of how data can fuel great storytelling. (That’s three uses of “great” in one sentence if you’re keeping count.) Also, read Oliver Roeder’s A Plagiarism Scandal is Unfolding in the Crossword World. It’s a great example of data-analysis-as-reporting.
We’ll talk about these when we get back to class on 3/22. We’ll also begin our important “Data of Candy” analysis then.
Have a great break.
1) One person from your group should email me by noon Thursday with a report on your project. Whoever emails me should copy the other two group members. While informal, report should include specific inquiries you’ve made, databases you’ve found, correlations you’ve considered (if any) and other models you’ve discovered. What do you like/not like about those models? How might your story/approach be similar/differ?
2) Using Google Sheets and Fusion Tables, figure out an interesting way to visualize this spreadsheet of people who supported this Kickstarter project. Then, embed the visualization in a blog post on jn430.ua.edu. Publish the post and email me the link. Do this before noon on Thursday.
3) Read Jeff Shaffer’s post on some finer points of data visualization and then publish a comment on this blog post in which you highlight something interesting/helpful from Jeff’s post.
Look over this data visualization catalogue. Publish a comment on this post in which you list three different data visualizations and what you would use them to show.
1) Read chapter from the Data Journalism Handbook about understanding data and visualizing data. Also, read the intro and chapter 1 of Alberto Cairo’s The Functional Art. In the comments section of this post, explain what you think Cairo means when he writes that “the first and main goal of any graphic or visualization is to be a tool for your eyes and brain to perceive what lies beyond their natural reach.”
2) Here is Quiz #2. Finish the quiz before Monday 2/22 at 7 p.m.
1) Prepare for your first quiz. Here’s what to expect:
- A series of multiple-choice questions about the basics of data journalism, including topics such as a “data state of mind;” general uses for and critiques of data journalism; finding data sets
- Some questions that send you looking for examples of data sets that you could use to approach a reporting project
- Some questions about the four tools/programs your classmates have presented on so far
- Some questions that ask you to find specific data (by identifying the right set and then finding data within the set)
- A series of questions about cleaning up a provided data set
2) Also, for Thursday, please read this excerpt from Paul Bradshaw’s Scraping for Journalists.