Final Capstone For Computational Journalism: Think Independently And Work Cooperatively
For Computational Journalism students, we decided to do our final 2 credits with Professor Upton who is teaching how to use data to tell stories, and 2 credits with Magazine, Newspaper & Online Journalism (MNO) students on their capstone project.
For the 2 credits with Professor Upton, we were required to create interactive charts for a project about sports data. The sports dataset we got was pretty big (with a size of 1.7MB) and we were asked to find the most important information which has the potential to catch people’s eyes. Though Professor Upton explained the data for us, we still needed to do a lot of research.
The first problem we met was trying to learn and understand how sports divisions work. At the very beginning, we didn’t know anything about sports in America. But after the Professor’s explanation and our research, we understood how the organization works, where the money comes and goes from, and how different divisions and people corporate and compete with each other.
The second issue we faced was trying to figure out how to clean the dataset and find a story angle for visualization. We had a large amount of data and most of it was are about money used in sports, so we finally decided to create two main categories: revenue and expense, then 6 subcategories under each of them.
The third problem we had was deciding which tool should be used to create charts, and what kind of charts we wanted. To be honest, there is some kind of “formula” in the field of data visualization. For example, designers often use line charts to show consistent change, use pie charts to show the percentage, use bar charts to compare the data and also show the change, etc. However, when we were trying to use the theory in our charts, we found the chart looked boring. There was no dramatic change from year to year or from division to division. The amount of money was consistent, and the line just looked smooth. Then we tried the bubble chart and bar chart, which didn’t work well either. After discussion, we realized that the problem was not about which chart we used but the data we had. Since the data was not interesting enough, we couldn’t expect any huge fluctuation for the visualization. Finally, all we could do was change the scale of the charts and make it look better. When choosing the visualization tool, we also struggled. We had tried Tableau, R, D3, Excel, and ended up with Google charts. I personally highly recommend Google charts to you if you’ll do data visualization one day (either for your job or for your boss) — it’s convenient and friendly to front-end engineers.
During the capstone, we spent the most time researching, changing ideas with others and thinking of solutions. Though we got help from Professor Upton, we did all the things by ourselves. We made the decisions. We solved the problems. We created the charts. And we achieved our goal and learned. We learned how to do our own projects, cooperate with others, and maximize the effect of our work.
