Credits: 3
Instructor: Sean McMinn
Contact: [email protected] On weekdays, I will try to respond to all emails within 24 hours. If you do not hear from me within that time frame, feel free to send a follow-up email.
Class time: 9 a.m. - 5 p.m., Saturdays
Office hours: During class or by appointment
Description: Reporters find news every day by talking to people and unearthing their secrets. In this class, we're going to do the same — but with data. This is not a graphic design or programming class, but a course that will teach you how to use the huge number of datasets available to reporters in order to break news and help inform your audience through text and visuals. By the end of this course, you should be able to:
- Find datasets relevant to a story you're reporting
- Process and clean data
- Analyze and "interview" data to find a story within it
- Visualize and present a story based on data
- Speak the language of reporters and technologists who use data to tell stories
- Identify common problems with data and think critically about its use in news reporting
Much of this class (and its syllabus) is based on Rachel Shorey's Spring 2018 Digital Frameworks course at Northwestern University.
- Come to class or let me know if you can't make it. I understand that situations occur that make it difficult to attend, and I'm willing to help you if you get in touch with me. Either way, you will be responsible for the material unless otherwise agreed upon.
- Because there are so few classes, you will only receive one excused absence. A second absence for any reason will result in you losing half of your attendance points (10% pts. off your grade). A third absence for any reason will earn you a zero on attendance, and you will not be able to earn more than a B- in the class. After one unexcused absence, an Early Warning notice will be filed to the student’s academic advisor.
- Complete assignments either in class or as homework due before the next class. That means assignments are due at 9 a.m. on Saturdays, unless otherwise stated.
- Late assignments will only be accepted if you contact me before the class in which it was originally due and we reach an agreement.
- Submit assignments on Github (instructions in first lecture) as specified in the assignment.
- Consume the news every week, and come to class ready to share at least one story that uses data in a substantial way. I will randomly select two students to share a story at the beginning of class, so please have a link ready to go (and I will do the same).
- Complete a final project, which will be a 1,000- to 1,300-word story with graphic proposals, based substantially on data.
- I reserve the right to change this syllabus at any time, provided all changes are pushed to GitHub where you can track them in the "commits" section for this repository. If at any time you feel like a change I made isn't fair, you can tell me why and I'll reconsider it.
- As for software, please make sure you have the following:
- Microsoft Excel
- Sublime text editor (free)
- Tableau Public (free)
- We might dabble in some other software as well, which we can download together.
- I will use email as the primary means of communicating with the class, making your use a required element. Please make sure you check your AU email daily.
- All lectures/assignments/class materials will be available on Github.
Your grade will be made up of three components:
- 20%: Class attendance and participation, including presenting data stories when I call on you to do so
- 50%: Assignments
- 30%: Final project
- A: publishable with minimal edits
- B: publishable with significant edits
- C: would need reporter’s revision to be published
- You cannot get more than a C on an assignment if there are factual errors.
- D: unpublishable, even with edits
- F: did not complete work as assigned
Note: I will attempt to have all assignments graded within one week of you turning them in, and I will send you your assignment grades via email. Questions about assignments can be asked at any time. If you'd like to see your overall course grade, please talk to me during class and I'll show you a detailed breakdown.
Students are encouraged to discuss assignments. For the most part, you will be working on different things, so talking about your work should not be a problem. Whiteboarding your way through a sticky problem with a friend or looking online for answers to technical questions are encouraged (data journalists do that all the time!). Your final work, however, must be entirely your own. A good rule of thumb is that no one else should take over your keyboard while completing an assignment. All American University policies regarding cheating apply to this class.
- 9a - 9:15a share data stories
- 9:15a - 11a lecture
- 11a - 12p interactive lesson
- 12p introduce assignment 1
- 12p - 2p working lunch (assignment, office hours)
- 2p - 4p lecture
- 4p introduce assignment 2
- 4p - 5p free time (assignment, office hours, leave early)
Class 1: Start here!/Finding data (Jan. 18)
- Lecture
- Assessment quiz
- What can data do
- Steps to a data story:
- Find data
- Clean data
- Analyze/interview data
- Present data
- Learn how to critique data stories/visualizations
- Kinds of data journalism
- Introduce final project
- Interactive lesson
- Learn to use github
- Assignment 1
- Come up with two final project ideas, complete first data reporting critique on a published data story related to each topic
- Lecture
- Government data
- Other data sources
- Data types
- Thinking critically about data
- Assignment 2
- Compile own dataset
Class 2: Cleaning data/covering the federal government with data (Jan. 25)
- Share data stories
- Lecture
- Cleaning overview
- Interactive lesson
- Five cleaning activities done with partners
- Advanced cleaning techniques using Sublime Text and Excel
- Assignment 3:
- Clean a messy dataset
- Lecture
- Covering federal government with data
- Assignment 4:
- Public records story
Class 3: Analyzing data/Stats for journalists (Feb. 15)
- Share data stories
- Lecture
- Analyzing data
- Interactive lesson
- Assignment 5
- Pitch a story
- Assignment 6
- 3 interesting questions to analyze a dataset
- Lecture
- Stats for journalists
- Assignment
- Work on final story
Class 4: Deconstructing stories (Feb. 1)
- Share data stories
- 4x: Review a story, work in groups to recreate the data reporting
Class 5: Data visualization/Presenting and storytelling with data (Feb. 22)
- Lecture
- Data viz
- Assignment
- Work on final project OR
- Extra credit data viz
- Lecture
- Presenting and storytelling with data
- Assignment
- Work on final project
Class 6: What the hell is an API, and other questions I was too afraid to ask / Final story interviews / Things smart people know more about than I do (Feb. 29)
- Lecture
- What the Hell is an API
- Interactive lesson
- Final story interviews
- No in-class assignment
- Lecture
- Panel: Things smart people know more about than I do
Due by the last class (Feb. 28) will be a data-driven story of between 1,000 and 1,350 words. This should include at least two proposals for data visualizations and interviews with multiple sources. The graphics for this story should be integrated into the story's presentation — not separate pieces. Unless otherwise discussed, the "nut graf" of your story should be something you discovered using the data skills covered in this course.
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There is a wide range of university services available to support you in your efforts to meet the course requirements and successfully completing this course, including:
- Academic Support Center (x3360, MGC 243) offers study skills workshops, individual instruction, tutor referrals, and services for students with learning disabilities. Writing support is available in the ASC Writing Lab or in the Writing Center, Battelle 228.
- Counseling Center (x3500, MGC 214) offers counseling and consultations regarding personal concerns, self‐help information, and connections to off‐campus mental health resources.
- Disability Support Services (x3315, MGC 206) offers technical and practical support and assistance with accommodations for students with physical, medical, or psychological disabilities.
If you qualify for accommodations because of a disability, please notify me in a timely manner with a letter from the Academic Support Center or Disability Support Services so that we can make arrangements to address your needs.