Syllabus

Syllabus: Data-driven Interactive Journalism (Jour72312)

Spring 2014: Jan 31 – May 16
Friday 9:30 am – 12:20 pm
Room 438

It isn’t hypberbole: journalists today have access to more data than ever before, as well as to better tools to understand that data and retell the stories it holds. Whether you want to write about cities, the environment, health, economics or box office results, you can turn rough numbers into a chart or map that tells a story. This semester we will work together to analyze, scrutinize and visualize the numbers behind interactive data-driven stories.

Data visualization is an emerging discipline that incorporates information design, and interaction design, mapping, graphing, data analysis and a bit of HTML and jQuery.

Students will pitch, report, and produce stories working both alone and in teams.

You’ll learn to use web-based tools such as CartoDB and HighCharts to create maps and charts that tell a story. You’ll learn some HTML, CSS and JavaScript along the way — just enough to show off your work. This is not a course in coding, but programmers of all skill levels are welcome.

Class Blog: http://hickman.spring-2014.dataviz.journalism.cuny.edu/
Class Notes: http://piratepad.net/cunyjdata

Course Reserves: http://cunygsj.docutek.com/eres/coursepage.aspx?cid=151&page=docs (Access code dv2014)

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Amanda Hickman 419i
amanda.hickman@journalism.cuny.edu
Hours: http://piratepad.net/amandahickman
Phone: 917/655-2579
Skype: amandabee
Tumblr: http://jour72312.tumblr.com/

Course objectives This three-credit course explores complex storytelling using data. You will pitch, report, conceptualize, design, and produce informative and compelling data-driven pieces. The course emphasizes:

  • Data collection
  • Editing and organizing data while maintaining its integrity
  • Basic statistical methods and concepts, the foundation of solid data reporting
  • Understanding technologies available to create online, interactive data-driven stories
  • Design basics, effective visual communication, and data visualization
  • Applying interactivity to data-driven stories
  • Critical evaluation of professional data-driven news stories
  • Understanding what makes a particular project successful
  • Seeking out innovative uses of data
  • Understanding the development process for creating data stories

Course outcomes At the end of this course, you will be able to:

  • Identify patterns in data that help uncover news trends
  • Conceptualize clear and concise ways to illustrate these trends
  • Create interactive graphics using both custom tools and web-based services
  • Evaluate effectiveness of data-based storytelling projects, both of your own creation and across the industry.
  • Instruct and supervise fellow journalists and programmers in identifying and producing stories that can become effective data stories.

About the Faculty

Amanda Hickman works at the intersection of journalism and civic engagement, and especially values reporting that makes it easier for individuals to participate in democratic processes. As program director at DocumentCloud, she helped reporters around the world analyze, annotate, and publish primary source documents. Amanda managed development of a series of games about public policy issues as Gotham Gazette‘s director of technology. She has spent more than a decade reporting on local and international events and working directly with community based organizations to understand, and draw their membership into, the political process. Amanda has taught at Columbia Graduate School of Journalism, NYU’s Gallatin School and CUNY Graduate School of Journalism.

WordPress and Digital Storage

Your major stories will be uploaded to CUNY’s Digital Storage server. Note that this web hosting will be available to you for two years after you graduate, so you should make plans to backup work you are proud of and find hosting for it off of campus servers.

Some assignments will be posted to a class blog.

Students will be required to present their stories in class for critique. Posts to the class blog are public by default, but you can choose to keep them private if you prefer. Students are encouraged to submit superior and/or timely work for publication elsewhere, including school outlets such as the New York City News Service.

Software Requirements

We’ll be using a handfull of free and open source software tools this semester:

You should already have TextWrangler and Excel installed. You’ll need both.

You will need to create accounts on JS Fiddle, CartoDB and Stack Exchange GIS. Important! Before you create your CartoDB account, make sure you have the information you need to get the student discount. Their standard free option is not adequate to our needs and upgrading is much more difficult than using the discount in the first place.

Grading

Your grade is determined by three factors: active participation in class, your homework assignments, and the two major team assignments.

Participation : 20%
Homework assignments: 20%
Assignment 1: 30%
Assignment 2: 30%

Grades for your two team stories are further broken down as follows:

Pitch (25%)
Storyboard (12.5%)
Draft (25%)
Final (25%)
Revision (12.5%)

This means that if you complete a brilliant story but don’t put real effort into your pitch or rough draft, you can’t get better than a C on the story.

All assignments are due by 5 PM the day before we meet for class. Most should be emailed with “Homework Week X” in the subject line, where X is number of the week. If we can’t find your homework because you got creative with the subject line, you won’t get credit for it. Really.

Pitches: A complete pitch should tell us who cares, why we care now, and what pre-reporting you’ve done. You must include…
+ a proposed title or headline
+ a story slug — up to three words that capture the essence of your story
+ a list of the story’s key elements
+ a news hook, or explanation of why this story matters now
+ a description of and link to the data (which means you have to find your data!)
+ one source you have already spoken with or at least three potential expert sources and your plans for reaching them

Storyboards: A storyboard organizes your content conceptually and spatially. This semester, when you turn in storyboards, you should also include a revised pitch and a proposed nut graf. Your nut graf will change your story develops, but it should capture all of the main elements of your story.

We use wireframe and storyboards interchangeably here. We’re looking for a simple sketch (on paper, in Word, or PowerPoint, Illustrator, or any number of online storyboarding tools) that shows us how you intend to integrate your visualizations, words, and navigation elements. Use simple boxes to tell us where your different elements will be positioned in a design, and how a user will navigate through the content. Check out Mark Luckie’s thoughts on sketching/storyboarding, with examples, from 10,000 Words.

Rough Drafts: A rough draft does not have to have the polish of a final project, but it should be close. You should have created the visualizations that you plan to use. Your classmates should be able to evaluate a rough draft on its merits, without a guided tour of forthcoming features. A complete rough draft includes: + Clean data in spreadsheets, already normalized, sorted, manipulated
+ Visualizations of the data with labeled axes
+ Captions
+ Credits
+ A headline
+ At least three links to other reporting that puts your story in a broader context.
+ Introductory text that includes information gleaned from at least one human source.
+ A source list, exactly like the ones you hand in for Craft II.

You’re not required to quote your source, but you do need to be able to tell the class what insights your human source provided.

Final Story: Your story must be posted to the class blog, with an excerpt before the jump and the full story after a jump. If you wish to host your final story elsewhere, you may, but you still need to post a headline, excerpt, image and linked text to the class blog.

Plagiarism and Copyright

It is a serious ethical violation to take any material created by another person and represent it as your own original work. Any such plagiarism will result in serious disciplinary action, possibly including dismissal from the CUNY J-School. Plagiarism may involve copying text from a book or magazine without attributing the source, or lifting words, code, photographs, videos, or other materials from the Internet and attempting to pass them off as your own. Please ask the instructor if you have any questions about how to distinguish between acceptable research and plagiarism.

In addition to being a serious academic issue, copyright is a serious legal issue.

Never “lift” or “borrow” or “appropriate” or “repurpose” graphics, audio, or code without both permission and attribution. This applies to scripts, audio, video clips, programs, photos, drawings, and other images, and it includes images found online and in books.

Create your own graphics, seek out images that are in the public domain or shared via a creative commons license that allows derivative works, or use images from the AP Photo Bank or which the school has obtained licensing.

If you’re repurposing code, be sure to keep the original licensing intact. If you’re not sure how to credit code, ask.

The exception to this rule is fair use: if your story is about the image itself, it is often acceptable to reproduce the image. If you want to better understand fair use, the Citizen Media Law Project is an excellent resource.

When in doubt: ask.

Deadlines

Deadlines are real: I plan my lessons around the assumption that you will have pitches to workshop on Feb 14. If you don’t, it disrupts the whole class. So deadlines should not be missed without exceptionally good reason, and I should always be notified in advance.

The exception: you will be handing in a handful of exercises this semester, follow up work on spreadsheet skills, CartoDB, Open Refine, jQuery and HighCharts — these are all described as “exercises” in the syllabus. Everyone gets one pass on these deadlines. As long as I have the exercise within a week of the original due date, I won’t hold it against you. But you only get to do that once.

If you’re stumped or stuck or worried that you are falling behind, talk to me. I can help get you caught up and unstuck.

Absences and Tardiness

Participation and attendance required and are important to your success in the class.

Please be on time for class and back to class from breaks. Repeated tardiness will result in a reduction of grade in participation.

SYLLABUS in BRIEF

Lecture: what you can expect from us Homework: what we expect from you
Jan 31 Welcome, What is data? Jan 31 McGhee report (view)
Feb 07 Visual Encoding, CSVs, Pivot Tables Feb 07 Pre-pitches, data sets
Feb 14 Cleaning data, FTP, Pitches Feb 14 Pitch 1, spreadsheet exercise
Feb 21 Mapping with Carto DB Feb 21 data cleaning exercise
Feb 28 Class will not meet Feb 28 storyboard 1, highcharts exercise
Mar 07 HighCharts, HTML Mar 07
Mar 14 Presentation, Navigation, jQuery Mar 14 map exercise, Pitch 2
Mar 21 Completeness, Advanced HighCharts Mar 21 jQuery exercise,Storyboard 2
Mar 28 Doing More w/Maps Mar 28 Rough Draft 1
Apr 04 Story 1 Critique Apr 04 Final Story 1
Apr 11 Show Your Work Apr 11 de-stresser week
Apr 18 Spring Break
Apr 25 Guest Lecture Apr 25 Rough Draft 2
May 02 Jobs Report Drill May 02 Final Story 2
May 09 Story 2 Crit, Hands on TBD May 09 Revisions Story 1
May 16 Wrap Up May 16 Revisions Story 2

SYLLABUS in DETAIL

Festival of Data: Every week one student will choose a data driven story to present in class. Prepare to discuss the strengths and weaknesses of the story, the authors’ use of data as well as their use of interactivity, and to identify the underlying technology. Blog your story in the “Festival of Data” category by 5 PM on your week.

Every Week:
+ Read Kevin Quealy’s blog, “Charts and Things”: http://chartsnthings.tumblr.com
+ Read Source, Knight-Mozilla’s blog on code and journalism http://source.mozillaopennews.org/en-US/learning/

Due Jan 31:
Watch Geoff McGhee’s Knight Fellowship Report on Data Journalism at http://datajournalism.stanford.edu/, especially
+ Chapter 2 Data Vis in Journalism
+ Chapter 3 Telling “Data Stories”
+ Chapter 6 Exploring Data

1 | Jan 31: Defining and Finding Data
Welcome and Expectations
What is data, what are data stories? Reactions to McGhee’s data journalism video report.
Discussion: work in groups to evaluate recent data driven stories.
Discussion: Finding data
Festival of Data: “In Climbing Income Ladder, Location Matters”

Due Feb 07:
Assignment Details
Pre-pitches: Find three datasets that interest you. Write a short blog post that describes the provenance of the data (who maintains it?), where the data can be found (include a link) and in less than 200 words each, explain why the data is interesting. One data set should be compost related.

Begin a scrapbook on WordPress, Tumblr, Pinterest or some other aggregation service. Email your dataset URLs and scrapbook URL to both professors under the subject “Homework Week 1″.

Make sure that Firefox is installed on your computer, with the Web Developer Toolbar. Install Tabula, Create a Magellan account with CartoDB — start from http://cartodb.com/academic to get your Magellan account for free.

Read Cairo: The Functional Art, Reading part 1: pages 25-31, 36-44, on thinking through a visualization as a tool for the reader; what graphical form best serves the goal? On e-reserve in the Library

2 | Feb 07: Visual Encoding, CSVs, Pivot Tables
Discuss homework: Problems, challenges, solutions,
Discuss: Visual Encoding, Pitching
Hands-on: Tabula, Pivot Tables

Due Feb 14: Assignment Details
Spreadsheet exercise, Pitch 1, Make sure Open Refine and Filezilla are installed on your computer.

3 | Feb 14: Cleaning data, FTP, Pitches
Basic HTML and FTP
Hands-on: Cleaning data with OpenRefine
Workshop Pitch 1

Due Feb 21: Assignment Details
Open Refine exercise, make sure you have TextWrangler installed. Map reading

4 | Feb 21: High Charts
Discussion: Visual Encoding
Hands-on: Highcharts

Due Feb 28: Assignment Details
HighCharts Exercise, Storyboard 1, Readings: Steele and Iliinsky, Designing Data Visualizations Chapter 4: Choose Appropriate Visual Encodings (in Library); Cairo: The Functional Art, Reading part 2: pages 118-129, on Cleveland & McGill’s perceptual accuracy

Extra Credit: Review my “maps” links and John Keefe, Dave Cole, Matt Stiles talk on maps from NICAR 2013

5 | Feb 28: Class will not meet
We’ll discuss Week 1.

Due Mar 07: Assignment Details
Storyboard 1; Read Cairo: The Functional Art, Reading part 3: pages 73-86, on presentation

6 | Mar 07: HighCharts, HTML, Pitch 2

Visual encoding
Pitches
Hands-on: CartoDB

Due Mar 14: Assignment Details
Map exercise, Read selections from Tufte, Quantitative Display of Information, on e-reserve in the Library: pages 91-105, 176-190.

7 | Mar 14: Presentation, Navigation, jQuery
Hands-on: jQuery and Bootstrap
Integrating a presentation
Usability
Anatomy of a chart

Due Mar 21: Assignment Details
jQuery exercise, Storyboard 2

8 | Mar 21: Completeness, HighCharts API

Due Mar 28: Assignment Details
Rough Draft 1

9 | Mar 28: Doing More with Maps

Due Apr 04: Assignment Details
Final Story 1

10 | Apr 04: Story 1 Critique

Due Apr 11: Assignment Details
Take a deep breath and get caught up.

11 | Apr 11: Show Your Work
Planning ahead
Building data explorers

Apr 18: no class (Spring Break)

Due Apr 25: Assignment Details
Rough Draft 2

12 | Apr 25: Guest Lecture
Lecture: TBD
Hands-on: TBD — Regular Expressions, CSVkit, Beginning R? We’ll vote.
Infographics

Due May 02: Assignment Details
Final Story 2, Read up for jobs drill (find WSJ, NY Times coverage of January 10 jobs report, as well as Feb, Mar, April)

13 | May 02: Jobs Report Drill
The BLS jobs report comes out at 8:30, by noon we will have some great charts ready to go.

Due May 09: Assignment Details
Revisions to Story 1

14 | May 09: Story 2 crit, Hands-on TBD
Hands on TBD (See Week 12)

Due May 16:
Revisions to Story 2

15 | May 16: Wrap Up
Discussion: closing thoughts
Fill out student evaluations