Syllabus

JOUR 490 Digital Frameworks for Journalism- Summer 2015

Thursdays, 6-9pm

Michelle Minkoff, meminkoff@gmail.com, 847-331-0866

(Email/gchat is best way to reach me. Also feel free to call or text, but not after 10pm.)

Office hours: Thursdays, 5-6. Other times by appointment.

Data journalism: Finding, analyzing and presenting structured information as part of your journalistic work.

Our class site lives at medilldigitalframeworks.wordpress.com (the site you are reading this syllabus on). Precise weekly agendas, assignments and readings will be updated there. Please bookmark it and check it frequently.

Our focus

Data has never been as plentiful, and important, in human history. It shapes business decisions and government policies, but understanding data and visualizing it clearly and without bias is still rare. It’s a large part of the world we cover, and often misunderstood. Without it, our stories are missing a key source. With it, we can get closer to exclusives that others may not be able to get.

When properly produced, data visualization and graphic analysis can vastly widen the range of stories we can tell, and how we tell them. Our goal is to understand four main areas in theory, through practical examples that can be applied as part of our journalism toolkit:

•why to use data and where to get it

•how to find the story within the data

•when and why to use visuals to tell a story

•how to shape data into appropriate and powerful visual storytelling.

Classwork

Each class we’ll get our hands dirty with data, in either a more abstract sense, or by creating something very specific and literal. Every week we’ll build on previous work, and classroom discussion will be crucial to achieving great work. For these reasons, attendance is mandatory unless advance approval has been given, and participation is of utmost importance in this class.

Please add to our class list (linked on the left of the class site) a link of a graphic or visual storytelling that was recently published that you’d like to discuss at the beginning of class. Even more interesting might be a story that uses data, but doesn’t make use of visuals to illustrate its point; if you choose this option, please create a simple sketch of a graphic that could have been used to more effectively tell the story. Please complete this by 6pm the night BEFORE we meet.

Homework

Often I will ask you to submit work that we’ve started or discussed in class so I can see how you’re progressing during the quarter. In this work it will be important to show the concepts that you’re handling or having a hard time with so I can help tailor the lessons and exercises to push us forward.

Final project

The majority of work you do for this course will be on your final project. You should begin thinking about your final project subject in the first week of class.

Your project should be a story that is relevant to your assigned beat for Medill News Service. This should allow you to focus on a big idea for the entire quarter, and is intended to allow you to pick something you will have familiarity with. Considerable value will be given to unique story ideas and execution.

Your project should include original data reporting, analysis and visuals. The project will be published on the web to finish the quarter, but the work doesn’t have to meet a certain level of interactivity. More importantly, you’ll be graded on the news value, choice and quality of visuals, and the effectiveness of your data and graphics at telling a story. If your visuals include web interactivity that is smart and effective, you will be rewarded, but the most important trait of your project is effective visual storytelling.

The deadline for choosing your final project story is July 16, the fourth week of class. After I have approved your story idea by Sat, July 18, you should submit a formal, but short memo about your story idea by Thurs, July 23. I will meet with each of you in class on the 30th (while everyone else is doing an in-class assignment) to briefly discuss your idea and make sure you have a good plan for completing it thoroughly and on time. It is imperative that you communicate your story ideas with me before that date, so that we can be sure you have selected a story with good potential by the deadline.

Required reading

The world of data journalism is constantly changing, so I ask that you keep up with two blogs that show compelling examples of the type of work we’ll be discussing, to stay up-to-date. Everyone should be prepared to talk about posts in class; you will be called upon randomly.

http://flowingdata.com/

https://source.opennews.org/en-US/

You can help positively affect your participation grade by emailing our class list with reactions to posts on either of these blogs, or other articles about data visualization you find online.

Other weekly readings are posted on the class site, and respond to current news.

Optional: If you want to see what actual data journalists talk about, subscribe to the National Institute for Computer-Assisted Reporting’s email list (http://www.ire.org/resource-center/listservs/subscribe-nicar-l/). This will not affect your grade, but may provide helpful fodder for class discussion, inspiration for your final project, and educate you more about this field.

Attendance

Attending every class is mandatory, and attending for the full three hours and on time, because of the in-class assignments we’ll be working on together, as well as the class discussion we’ll have, as we try to cover a lot of ground in a compressed length of time. This is an active seminar, not a lecture, and for our work to be successful, it’s just as important for you to be there as for me to be there.

If you do not attend a class, or are more than five minutes late, your grade will be affected. If you have a valid reason for missing a class or being late, please email me in advance if you can, or let me know as soon afterward. It will be possible to work out some makeup work.

Grading

25% – Classwork/homework assignments

20% – Critiques

30% – Final Project

15% – Class Participation

Classwork/homework assignments – We will work on assignments together in class, which I will ask you to email to me at the end of class. I will also give you assignments to take home to cement the learning. These are graded 60% on completion, and 40% on being completed accurately. They should be accurate if you follow along in class, and you can always email me if you need assistance. I also encourage you to help each other with homework, but if you have worked with someone else, please let me know when you submit the assignment. This also includes adding a link of a data story and/or visualization for discussion to our class spreadsheet.

Critiques – Over the course of the quarter, complete two written critiques on items that are up for discussion in the class spreadsheet. They could be ones you submitted, or ones your classmates submitted. This is to be an at least 750 word essay on what story you think the piece is trying to tell, what it does well and ways in which you would improve it. An “A” critique will cover praise and constructive criticism for both story-driven and presentation-driven parts of a work. You can submit these critiques anytime you like, but one must be submitted halfway through the quarter, for our fifth week of class, July 23. The second one is due on the ninth session, August 20.

Final project – Covered in more detail below.

Class participation – Covers attendance, timely arrival to class, questions/comments during class discussion, active engagement during class workshops as we work through data together. If you know that you will miss a class, please email me to let me know, so we can find a way to make up the work. You can improve your participation grade by writing to the class email list about entries in the blogs posted under required reading.

Other questions

Over the course of the class, as you have questions related to course work, journalism and technology, or data journalism in general, feel free to contact me via methods listed as the top of the syllabus. I’m here to help, and more than delighted to chat.

Final project

The final project is due on August 25.

On that date you will give a short presentation showing off your work to the class, explaining your process behind the story.

Project guidelines

This is the breakdown on how your final project will be judged. This should help you decide what’s the most important way to spend your time.

Appropriate use of visuals

30%

Is it a story that is strengthened by your choice of visuals? Did you choose the right visuals to tell the story?

Quality of visuals

30%

Are your visuals easy to understand? Are they interesting and enlightening? The visuals should be professional and polished.

News value

20%

Is your story relevant to your beat? Is it an original story idea? This is also where you will score points on good reporting and writing that complement and explain the visuals.

Design

10%

On the last day of class, you will be expected to give a 5-10 minute presentation to the rest of the class on why you chose your topic, how you found/cleaned your data, how you turned it into a story and why you made the visual storytelling decisions that you did. Presentation should show what you learned, show the reasoning behind your decisions, and be clear enough that others can learn from the lessons you learned.

Presentation

10%

Does your story have appropriate fonts, colors, alignment and hierarchy? Is there a clear sense of order on the page?

Examples

Use these examples to draw inspiration for stories and visual forms that you could use.

The Evolution of King James

Inequality and New York’s Subway – I have a lot of issues with this graphic, but it’s an interesting idea to draw inspiration from.

Increased Border Enforcement, With Varying Results

Snakeheads – Not data heavy, but another type of graphic style you might choose. More explanatory than data-driven, which is often the better approach.

How far could North Korean missiles actually go?

State-Financed Preschool Access in the U.S. – Good example of a short graphic article

Images of Brain Injuries in Athletes – Another less-data centric graphic that is well-designed and informative.

Tax burden for the Dow 30 drops – Graphic article

Gun deaths shaped by race in America – Ambitious data-reported story with embedded graphics (How they did it)

Week-by-week outline

June 25 (Week 1): Go over syllabus, use class data to demonstrate what is data, different categories, why cleaning is important

July 2 (Week 2): Where to find government data for various beats, different formats it might come in. Also, creating our own data source.

July 9 (Week 3): Using Excel to clean data. Using Excel to sort/filter/find story in data. This is the key class where we will learn basic components of data analysis.

July 16 (Week 4): Overall design principles for a graphic as a whole.

July 23 (Week 5): At least one critique submitted by this point. Making the structure from unstructured: How to scrape data. First, without programming. Then, we’ll try some programming together in a language called Python. This is the only session where touching programming is mandatory, but it’s important that everyone try it once.

July 30 (Week 6): Visualization for reporting using Excel, Google charts. When to use different types of charts, elements that go into design of charts and graphs.

Aug. 6 (Week 7): So, I’ve got my data set. Now, what do I visualize? How do visuals work with my text story?

Aug. 13 (Week 8): Mapping. When to make a map, what information you need to get there. Building maps in Fusion Tables. Discuss other map tools that are out there.

Aug. 20 (Week 9): Text data analysis and visualization. Second critique must be submitted by this point.

Aug. 25 (Week 10 — NOTE THIS FINAL CLASS MEETS ON A TUESDAY): Final project presentations/Further learning/Conclusion. Final projects due.

Additional information

All students are required to adhere to the Medill Integrity Code (http://www.medill.northwestern.edu/student-life/academic-integrity-policy/) as well as Northwestern University’s Academic Conduct Policies, which are found in the Student Handbook: http://www.northwestern.edu/studentaffairs/publications/media/pdfs/handbook.pdf

Academic dishonesty can result in penalties ranging from letters of warning to dismissal from the university. Instructors may give a failing grade in a course for academic dishonesty.  It is also university policy that instructors can require students to submit their work electronically to be analyzed for possible plagiarism.

Northwestern University works to provide a learning environment for students with disabilities that affords equal access and reasonable accommodation. Any student who has a documented disability and needs accommodations for classes and/or course work is requested to speak directly to the Office of Services for Students with Disabilities (847)-467-5530) and the instructor as early as possible in the quarter (preferably within the first two weeks of class). All discussions will remain confidential. Accommodations can be made by instructors once OSSD has met with the student and verified the disability.

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