Data Visualization (Journalism)
|Central Connecticut State University||Clarence Carroll Hall 034|
|Wednesdays||4:30 - 7:30 p.m.|
Instructor Andrew Ba Tran
There are more charts, maps, and interactives on news sites than ever before. But data-driven news stories and graphics have been moving readers, impacting communities, and changing lives for hundreds of years.
According to the undergraduate catalog, this course is Data Visualization, but it’s much more than that.
This course is about Data Journalism. Data and Journalism. Observations and stories. This class will be both technical and philosophical.
Students will learn how the best practices of journalism and data science can lead to great stories.
Data is just another source for reporters to research, interview, and glean answers from. Students will be introduced to communicating stories through visualizations like charts and maps. But more importantly, this course will also build on the skills necessary to find and tell stories using data and the knowledge required to understand the data itself.
The three-hour class will be split between discussions and hands-on workshops.
Aside from the book, we will have weekly readings chosen from news stories both recent and historical, research documents, government PDFs, Storifies, and many others. Students will be expected to discuss these readings in class.
Be prepared to talk about the following things related to the readings at class
- What data set or key idea is used to base the reading?
- Is there anything you disagree with?
- How was the data collected?
- Was that process infallible? innovative?
- What could improve on the data or the story?
- Are there other angles the writer could have approached the data or the story?
Each student’s final grade will be determined by five factors described below. While I will communicate any concerns that I have about individual performance, please do not hesitate to contact me with questions about grading or general performance.
- Participation: 20 percent
- Homework assignments: 25 percent
- Mid-term data analysis project: 25 percent
- Final data story project: 30 percent
Attendance and participation
We will talk about our readings and everyone will be expected to participate. Students will be graded daily for their insight and conversation.
Sometimes, quizzes on the readings will be done at the beginning of classes.
Unless you have an excused absence, missing a class will immediately result in a zero for participation on that day. If you show up more than 10 minutes late, that will be considered an absence for the day. This also applies to leaving early. If there’s a justified reason to miss a part of the class, you must ask for approval before class.
Journalism requires focus and attention to detail, not just showing up. It’s OK to use your laptop or phone but if you are not participating in the discussion or following the demos because you’re on Snapchat, then I will consider that an absence for the day.
Every class builds on the other so it’s very important to attend each one.
Check with me first if you must be absent. This includes CCSU-related events or obligations. It will be on you to figure out what was missed in class and to catch up.
In-class and homework assignments
Students will be required to complete exercises both during class and outside of class.
Out of class work should take less than two hours out of your week.
Homework assignments will always be due before 2 p.m. the day of class.
Late work will be penalized on a sliding scale (the later the assignment, the larger the penalty). A good rule: don’t come to class empty-handed – at the very least, show me that you attempted the assignment. Together these assignments constitute 25 percent of each student’s grade.
The mid-term will consist of two parts. A take-home exam before class on March 8 and a data analysis exam during class. Each will count fifty percent each.
Details on the take-home portion due March 8:
Obtain and analyze government data and write a summary of the analysis. Not the actual story. Instead, the memo should detail the process it will take to get to the final story or visualization. Explain what it took to get the data, who explained it to you, point out the nuances and limitations of it and how it was collected or processed. Discuss the gaps in tha data and what could be improved. Then, analyze the agency that provided the data set. Detail the scope, quality, and accesibility of the rest of the department’s data. How journalists might have used it so far or how it could be used in the future.
Details on the in-class exam will be explained on March 8.
Students will be responsible for obtaining, analyzing, visualizing, and writing a story based on a government data set, using methods learned in class.
The process mirrors that of a story that is pitched, drafted, edited, and copy-edited in a newsroom.
As such, the final product should be good enough for publication on any local news site and possibly could be by The Connecticut Mirror.
The breakdown for the final data story will be:
- Pitch & revision (25 percent)
- Storyboard (25 percent)
- Draft (25 percent)
- Final (25 percent)
In a newsroom, meeting deadlines are crucial.
Answers to the assignments will be discussed in class. Therefore, each day an assignment is late will reduce the score by an additional 20 percent.
Non-working versions will also be considered late.
Turn in homework by the deadline, even if you’re unsure of some of the answers.
Here’s what you need to send me in order to get help:
- The code you’re writing. “Hello, I’ve attached data.r, the script I’m trying to get to work.”
- What you’re trying to make it do. “This code is supposed to turn this list into a data frame.”
- What is actually happening. “I’ve tried it several different ways but I keep getting a funky error.”
- The error message you get, if one shows up. “It says Unlisted Error”
- Tell me what you’ve done to problem solve this issue on your own. If there has been no effort then I will not respond except with a link to Google. “So I’ve tried googling ‘unlisted error’ and oh wait, StackOverflow.com has the answer. Whoops. Ignore everything I just wrote.”
It is crucial to communicate the previous steps to get quality help with issues that pop up in code. It’s a good practice to develop now.
I will respond with clues to point you in the right direction to fixing the issue.
Don’t ask me the morning of or at midnight for help. You can, but I do have a full-time job. The earlier you ask me for help the greater the chance I’ll be able to respond.
I work full time so my office hours are when I happen to be online (which is often).
The best way to reach me is by email at abtran AT gmail.com.
Alternatively, students will be invited to dataconn.slack.com where they can reach me or discuss the class online in the #ccsu-2017 channel.
Do your work
And only your work. It’s OK to consult with each other. But doing your own work is the only way you will properly absorb these ideas and skills.
In the newsroom, plagiarism will derail your career. Plus there are consequences within CCSU.