Course Description: This course is designed for the Graduate Journalism Program. Students should have already completed Public Affairs Data Journalism One. Enrollment otherwise is subject to instructor approval.
Some data journalism assignments for this class can be coordinated with reporting efforts from other journalism courses with approval from all instructors.
Students will produce timely data-driven stories for Peninsula Press. You will learn to go deep on mining data for story, reporting and writing out that story. You will learn the basics of mapping and visualizing data. Instruction will include the use of a variety of software programs: spreadsheets, OpenRefine, DocumentCloud, Tabula, Trifacta, SQL, Tableau, Python/Jupyter Notebooks and R/RStudio among others. During the course, you may work with other journalism organizations on collaborative projects. You will work on a data project either of your own choosing or one we come up with together. Content approved by editors will be published on the Peninsula Press news site, http://peninsulapress.com/.
Professional journalism disclosure: When interviewing anyone for a story (on or off campus) you need to properly identify yourself “as a student reporter writing BOTH an in-class assignment AND a PUBLIC story – one that could be published by major media outlets: sfgate.com, The San Francisco Chronicle or KQED via the Peninsula Press.” You must make this very clear. You need to MAKE SURE that your interviewee understands their words and image could appear in major media: the San Francisco Chronicle or KQED via The Peninsula Press.
Student membership in Investigative Reporters and Editors
Learning Goals: By the end of the quarter, you should demonstrate:
Integration of narrative writing style with data-driven
- Proficiency in finding, cleaning, analyzing and visualizing data for
- Proficiency in mining unstructured text for news nuggets and trends.
- Proficiency in Web research and data collection
Assignments: There are four main assignment categories. Students will produce:
- Group work to be described in class discussion. This will include a public records component.
- One data-driven individual news project, including a story and data visualization or map. If possible, this will be published.
- Individual analysis and memo detailing your findings on either campaign finance data or open policing data and using either R or Python. This is not intended for publication, but more to help you hone your skills in R or Python.
- Ongoing, small assignments and in-class work, which may include at least one off-the-news class assignment.
Deadlines: Assignments should be uploaded to Google Drive and the link submitted in Canvas before or at the time and date assigned. Screenshots and links to data visualizations should be included in Google Docs along with headlines, accompanying stories, etc.
Some assignments may also be uploaded to the Peninsula Press WP site. Just as in a newsroom, you cannot miss deadlines. If you are having difficulty completing an assignment, you need to email me as soon as you can so we can address the situation. Often, obtaining data is difficult and depends on others. But keeping your partners and your editor informed is critical.
Structure: Most classes will include both discussion/lecture on features of data journalism and either hands-on data journalism practice or class discussion on your projects.
Each student is expected to participate in class discussions, contribute constructive critique of work, and come to class having completed assignments and readings. Student work will be discussed and critiqued in class.
Class begins promptly. Lateness or unexcused absence will affect your grade. If you have to miss class, notify me in advance. Please refrain from being on a device in class unless it is related to class. Here are a few interesting articles about the pros and cons of using devices in the classroom.
How students contribute when they use devices in classes, by Steve Buttry.
Stanford’s Honor Code applies: http://www.stanford.edu/dept/vpsa/judicialaffairs/guiding/honorcode.htm
Group project = 15%
Individual Data Project = 30% (data acquisition, analysis, story, visualization or map)
Data analysis work and memo = 25%
Small Assignments = 15%
Class participation = 15%
Data journalism involves a lot of moving parts. You will be graded on your ability to find and obtain data, your ability to accurately analyze data for a story, how you tell a story both in the narrative and through visualization or maps. We will use AP style in our stories.
Readings and viewings: Much of the work will be assigned via online links or for you to look up in the IRE archives. Be prepared to discuss clips and readings and lead and participate in discussions.
One final note: I think journalists are often idealistic cynics. They question everyone, trust but verify and still think they can change the world. That’s why I became a reporter, that and I found it more fun than anything else I had tried. So, this quarter, let’s have some fun!