Syllabus, Fall 2013

Syllabus: Data-driven Interactive Journalism (Jour72312)

Fall 2013: Aug 28 – Dec 18

Wednesdays 5:00-7:50pm

Room 436

We swim in a world of data – from election results, budgets and census reports, to Facebook updates and image uploads. Journalists need to know how to find stories in data and shape them in compelling ways. This hands-on course teaches reporters and editors to gather, analyze, and visualize interactive data-driven stories. This emerging discipline touches on information and interactivity design, mapping, graphing, animation tools, and data analysis.

Participants are expected to pitch, report, and produce stories working alone and in teams. You’ll learn to use online Web tools such as Google Fusion Tables, Refine, and Maps, and integrate them in a non code-intensive development environment. Familiarity with HTML/CSS is helpful, but not required. This is not a course in coding, but programmers of all skill levels are welcome.

Class Blog: http://datadrivenjournalism.fall.2013.journalism.cuny.edu/

Class Notes: http://piratepad.net/cunydata

E-reserve: http://cunygsj.docutek.com/eres/coursepage.aspx?cid=141 (Use access code ddj2013)

Skip to syllabus in detail

Russell Chun, 404E

russell.chun@journalism.cuny.edu

Hours: Wednesday 2-5pm

Tumblr: datanarratives.tumblr.com

Amanda Hickman 419i

amanda.hickman@journalism.cuny.edu

Hours: http://piratepad.net/amandahickman

Phone: 917/655-2579

Skype: amandabee

Tumblr: http://jour72312.tumblr.com/

Nicholas Wells, Teaching Assistant

nicholas.wells@journalism.cuny.edu

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

Course objectives

  • 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 (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, students 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 their 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

Russell Chun is a multimedia developer, author, and educator specializing in visualizing science, data, and story ideas for the web. He is on the adjunct faculty at City University of New York (CUNY) Graduate School of Journalism where he teaches data-driven interactive journalism. He is also on the faculty in the Multimedia Arts Degree Program at Sessions College for Professional Design. He is the author of several books on multimedia, and has developed courses, and interactive and video products on effective multimedia.

Russell previously taught at Columbia University and the University of California at Berkeley Graduate Schools of Journalism. He’s served as an interactive consultant and trainer for News21, a Carnegie/ Knight-funded national initiative to improve the quality of journalism education in the United States. He’s judged local and national multimedia news contests.

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

Final stories will be showcased in our 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. (http://datadrivenjournalism.fall.2013.journalism.cuny.edu/)

Grading

Your grade is determined by three factors: participation, successful completion of all solo homework assignments, and successful completion of the two major team assignments. Your participation includes attending all classes, being active in discussions, workshops and critiques, presenting your story for the Data Festival, and participating in all in-class hands-on activities. Your assignments will be evaluated in terms of use of data, story and context, interactivity, and design.

Participation : 20%

Homework assignments: 20%

Assignment 1: 30%

Assignment 2: 30%

All assignments are due by noon on the day they are due. Most should be emailed to both professors 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.

Grades for each major assignment are further broken down as follows:

Pitch (25%)

Storyboard (12.5%)

Draft (25%)

Final (25%)

Revision (12.5%)

What do we mean when we say “Pitch” or “Rough draft”?

This is what we mean:

Pitches: A complete pitch should tell us who cares, why we care now, and what pre-reporting you’ve done. You must include… + a question or thesis

+ 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. 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

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.

Copyright

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. http://www.citmedialaw.org/legal-guide

As with plagiarism, when in doubt: ask.

Deadlines

Deadlines on assignments – as in any newsroom – are sacrosanct and should not be missed without exceptionally good reason, and only when your instructors have been notified in advance. If you are taking the course for credit, late assignments will be assessed a one-half grade penalty for every day overdue.

Absences and Tardiness

Participation and attendance are important ingredients to your success in the class, especially in this course where your major assignments are team-based.

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

Notify the instructors of any absences before class, or as soon as you know you will be out.

SYLLABUS in BRIEF

Lecture: what you can expect from us              | Homework: what we expect from you
--------------------------------------------------|-------------------------------------------
08-28 Course intro. What is data?                 | 08-28 McGhee report (view)
09-04                                             | 09-04 (no class)
09-11 Numeracy and basic spreadsheets             | 09-11 Datasets and scrapbooks (hand in)
09-18 Cleaning data, advanced spreadsheets        | 09-18 Spreadsheet assignment due
09-25 Mapping                                     | 09-25 Clean a dataset due
10-02 Charting                                    | 10-02 Create a map due
10-09 Presentation: Interactivity+Navigation      | 10-09 Pitch 1 due, Chart it Three Ways due
10-16 Information design and Ethics               | 10-16 Storyboard 1 due, jQuery assignment due
10-23 Open workshop                               | 10-23 Rough draft 1 and redesign assignment
10-30 Critique of Story 1                         | 10-30 Story 1 due
11-06 CartoDB                                     | 11-06 Last day to revise story 1
11-13 HighCharts                                  | 11-13 CartoDB map due, Pitch 2 due
11-20 Guest lecture and open workshop             | 11-20 HighChart exercise due, rough draft 2 due
11-27                                             | 11-27 (no class)
12-04 Critique of Story 2                         | 12-04 Story 2 due
12-11 Last class: Course wrap-up                  | 12-11 Last day to revise 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:

Due Aug 28:

Watch Geoff McGhee’s Knight Fellowship Report on Data Journalism at http://datajournalism.stanford.edu/
+ Chapter 2 Data Vis in Journalism

+ Chapter 3 Telling “Data Stories”

+ Chapter 6 Exploring Data

1 | Aug 28: Defining and Finding Data

Course introduction (expectations, syllabus review)

What is data, what are data stories? Reactions to McGhee’s data journalism video report.

Discussion: work in groups to evaluate four recent data driven stories.

Discussion: Looking for data, where to look and how to look?

Festival of Data: “In Climbing Income Ladder, Location Matters”

Festival of Data: Google Workshop Small Arms Trade http://workshop.chromeexperiments.com/projects/armsglobe/

Sep 04: No Class

Due Sep 11:

Assignment Details

Find two datasets that interest you. Tell us who maintains it, where the data can be found (the URL) and in 1-2 sentences explain why the data is interesting.

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 both the Web Developer Toolbar and Firebug plugins.

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 | Sep 11: Finding the Story in Your Data

Discuss homework: Problems, challenges, solutions,

Discuss: provenance and staying organized

Spreadsheet review: data types, rows and columns, sorting, copy and paste, selections, formulas. Introduction to Pivot tables.

In-Class Exercise: Using spreadsheets and Pivot tables

Due Sep 18: Assignment Details Spreadsheet homework, as described on the blog. Email your spreadsheet answers to both professors under the subject “Homework Week 2” by noon.

Make sure Refine is installed on your computer.

3 | Sep 18: Cleaning Data

Assignment Details

Cleaning data and advanced spreadsheets

Google Refine and common spreadsheet formulas: split, concatenate, unique, countif, sum.

In-Class Exercise: working with Refine to clean data.

Due Sep 25:

Clean a dataset with Refine and tell us your findings in a nutgraf, read about maps, and look for another dataset with geographic information: details here. For those who want a challenge, take another pass at using grep. Email your dataset, nutgraf, and description and URL of your geo dataset to both professors under the subject “Homework Week 3”

Make sure that you have Firefox installed on your computer with both Firebug and the Web Developer Toolbar plugins.

4 | Sep 25: Mapping Data

Discussion: Looking at map examples

In-Class Exercise: Mapping the flu with Google Fusion Tables to make maps

Geocoding, Shapefiles->KML, Fusing two data sets, customizing infoboxes, colors, using filters

Tool: Google Layer Wizard

Tool: Using Firebug to see how they did that

Due Oct 02:

Assignment Details

+ Pre-pitches: Come in with two good ideas for stories– what is the story you want to tell, what data do you need to tell it, where do you think you could find that data.

+ Create a map and upload it to digital storage

Email your good ideas and the URL of your map to both professors under the subject “Homework Week 4”

+ Read Ilinsky, Cairo and Groger

5 | Oct 02: Charting Data

Discuss readings

Discuss anatomy of a news-chart: all the little pieces

Chart types – what they’re good for, what they aren’t,Cleveland and McGill’s findings on readability of chart types

Pitching a story: what we expect, what you’re thinking. Round robin pitches—discuss the story ideas from your homework.

Choose teams for your first story.

Due Oct 09:

Assignment Details

Solo – Chart it 3 ways: we’ll give you a single data set, we want you to chart it three different ways and compare the pros and cons of each. Email your work to both professors with the subject “Homework Week 5”

+ Read Cairo: The Functional Art, Reading part 3: pages 73-86, on presentation

Team – pitches for your first story. A complete pitch should tell us who cares, why we care now, and what pre-reporting you’ve done. You must include…

+ a question or thesis

+ 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!)

+ the name of one source you have already spoken with or at least three potential expert sources and your plans for reaching them

+ a sketch or wireframe of your planned visualization (you can bring this in on paper)

Pitches must be posted to the class blog, in the “story 1 pitches” category.

6 | Oct 09: Pitching, Presenting, Visual Encoding

Integrating the presentation: HTML/CSS/JS and jQuery UI Annotating the data, Interactivity

Workshop: Pairs of teams work with each other to discuss pitches.

Discussion: storyboards

Due Oct 16:

Assignment Details

Solo – integrate the data and visualizations you made previously with jQuery and iframe it on the class blog. Use at least three different views of the data and provide navigation between those three views. Include a headline and text to provide context to your work. Use the category “Hype Assignment”. For this assignment, we’re looking more at your handling of navigation, usability, and interactivity to integrate multiple visualizations.

Team – refined pitches and storyboards for your first story. A storyboard organizes your content conceptually and spatially. This semester, when you turn in storyboards, you should also include a revised pitch. 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. Scan your sketch and include it with your post.

Read selections from Tufte, Quantitative Display of Information, on e-reserve in the Library: pages 91-105, 176-190. Use the password (ij2012) to access the material.

7 | Oct 16: Ethics and Information Design

Discussion: Intentional use of space

Discussion: Principles of design – grids, hierarchies, color, typography, white space, scale, repetition, consistency.

Discussion: Ethics, avoiding distortion, responsible presentation of data

What do we expect in a “rough draft”?

Due Oct 23:

Assignment Details

Solo – design assignment. Re-design one of the following data visualizations:

The Atlantic: Classified Documents

Good: Teacher Salaries

National Geographic: High School Foreign Exchange

Thank Stanford’s Jeff Heer of Stanford for these examples

Identify the weaknesses in visualization and/or design and explain how you would improve them. If you do this assignment on scratch paper, you need to scan or photograph it. Email your re-designs to both professors with the subject line “Homework Week 7”

Team – Rough drafts of your first story. 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. Keep in mind that this draft represents 25% of your grade on this story.

8 | Oct 23: Open Workshop, Completeness

Discussion: Completeness, academese

Open workshop

Due Oct 30:

Assignment Details

Team – post final story on the class blog in the category “Story 1 Final” Include 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.

+ Credit all images and code libraries

+ Clearly attribute all data, including direct links where appropriate.

+ See the rest of the checklist to ensure that your story is complete: http://datadrivenjournalism.fall.2013.journalism.cuny.edu/checklist/

9 | Oct 30: Critiques

Discussion: Critique our first finished data stories

Assign Teams for Second Story

Due Nov 06:

Assignment Details

Solo – Bring in two good ideas for your second story.

Team – one last chance to revise your first story.

Solo post mortems: what did you learn in the course of building your first story? What role did you play on your team? What role did your collaborators play? Describe one thing you would do differently if you tackled this story again. Post mortems are private: email them to the professors with your week 10 homework.

10 | Nov 06: CartoDB maps

Discussion: Anything great in the revised projects?

In-class exercise: working with CartoDB and styling maps with CSS

Round robin story meeting

Due Nov 13:

Assignment Details

Solo – Create a CartoDB map. See post (TK) for more detail.this week, you’ll walk through the CartoDB tutorials (Timelines are due Nov 27)

Team – Prepare pitches for your second story. If you don’t remember what we expect in a complete pitch, see week 5.

11 | Nov 13: HighCharts

Workshop: Pairs of teams work with each other to discuss pitches.

In class exercise: HighCharts, Mr. DataConverter, and understanding different data formats.

Due Nov 20:

Team – Refine pitches and develop storyboards and rough drafts.

12 | Nov 20: Guest Lecture

Lecture: Guest, Lam Thuy Vo, Interactive Editor of Al Jazeera America.

Open workshop for rough draft feedback

** Nov 27: no class (Friday schedule)**

Due Dec 04: Final story #2

13 | Dec 04: Critique

Critique of second story

Due Dec 11: Revisions to second team story.

Solo – Post mortem on story 2.

14 | Dec 11: Wrap up

Discussion: revised projects

Discussion: closing thoughts

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