Q&A: Improving Journalism With Engineering
Front End Developer, Facebook
This is generally a time for celebrating holiday traditions, but it’s also when many are filled with anxiety about graduate school applications. For those who are considering applying to the Columbia Journalism School, we sat down with Chi-An Wang ‘17, a graduate of the dual M.S. in Journalism and Computer Science program to learn more about his experience in the program and life after J-school. Chin-An received honors in his Data Journalism II class and covered the transportation beat in Manhattan writing stories that ranged from subway maintenance to the Citi-Bike program.
Here he shares his best advice for aspiring dual degree and data journalism students as well as the personal experiences that inspired him to apply.
1. What made you pursue a dual degree in journalism and computer science?
I was inspired by the Sunflower movement, a student movement in Taiwan that protested the government signing a trade deal under the table and without a review process. At that time, different media had totally opposite opinions about what happened in our legislature. Giving my goal of understanding and improving journalism using my engineering skills, I thought that Columbia Journalism School's dual degree program was the best choice for me.
2. In your view, how does knowing computation and data journalism advance the journalistic mission?
Everyone recognizes that stories packaged with data visualization and great analysis can have a real impact. But I think computational journalism is relatively less-developed and fewer people can intuitively think of how they can apply computational technologies to journalism. My favorite part of computational journalism is applying statistical models to data and building an AI tagger that could save journalists a significant amount of work. For example, identifying political contributions sponsored by the same group of people in a huge data set could take several weeks or months of manual work for some journalists. But it might only take a few hours to train an AI model to do this and minutes to let machines make a prediction. I also love how computational algorithms, like entity recognition, can help journalists identify critical stakeholders in a messy data set that has different formats. This helps investigative journalists quickly narrow down and standardize the content that they need to analyze.
3. Can you tell us about a story or project that you worked on that wouldn't have been possible without data and computation skills?
In my Data Journalism II, an elective class in the program, I worked with my teammates to discover the cross-giving culture of the philanthropic organizations in New York City. We downloaded the annual reports for several organizations, including The Metropolitan Museum and the Museum of Modern Art (MoMa) and scraped the donors' data into spreadsheets. We did fuzzy string comparison to merge all the slightly different names in various reports and plotted several graphs to compare them. In one of our graphs, we presented each donor as a node. A donor represented a family, a couple or an organization. We connected two nodes if there was a donation between them. The result was a huge, entangled network analysis.
The analysis gave us a better grasp about how donations work between these large philanthropic organizations and who the big donors were. And with Optical Character Recognition (OCR), we were able to convert PDF files into Excel tables; with R and Python, we were able to aggregate and clean up the messy data; with data visualization and solid reporting, we were able to find the trends that informed the story.
4. What advice do you have for aspiring journalism students who are thinking about applying to the dual-degree or data journalism programs?
These are very intense programs. I promise you can't chill, but I guarantee that you won't regret the time you spend reporting in New York City and running AI algorithms in Columbia's library.
Because the dual degree is a two-year program, when your colleagues from the Journalism side have already graduated, you will still be working on your graph search algorithm. While your computer science teammates graduated, and left to work for the big tech names, you will be working on your master's project. It will require all of your discipline and passion.
Despite this, you will have the skills that will make you an invaluable employee in any top organization. The ability to use technology to find, analyze and report the facts is greatly needed. I believe the professors and student colleagues at the journalism and engineering schools can help you become a journalist/engineer hybrid with deep expertise in how to use technology to meet the demands of a career in either field.
- Applications for the M.S., M.S. in Data Journalism, and Ph.D. are due December 15.
- Applications for M.A. are due January 9.
- Applications for the dual M.S. in Computer Science and Journalism are due January 15.