Cher Qin, Shuyuan Hung, John Jiang, and Kevin Koech, four juniors from the Wesleyan University, come home with a surprising win at NYU. After their return to campus, we sat down for a short conversation about their experiences.
Many know that Wesleyan has a very large collection of prints dating back to the 15th century, stored in the Davison Art Center (DAC). Not many are aware that, through the efforts of the DAC staff, the collection comes with an extensive dataset containing metadata for all records. In the fall of 2017, students from the Introduction to Network Analysis (QAC 241) got a chance to view some of the famous prints and then search for new insights in art history using their quantitative skills. This post describes the experiences, accomplishments, and challenges of working with art history data.
It’s bizarre to go to a university where it’s practically a given that your classmates will your mind when they tell you about their summer. This could be daunting, as not all of us have the resources for a big internship or trip around the world. However, you don’t need to travel to have a story worth sharing, a fact that seventeen Wesleyan students took advantage of this summer. The QAC’s Summer Apprenticeship is a program in which students partner with a faculty mentor to work on a data-based research project. I spoke to a couple of participants and asked them to tell me about their work.
With data science languages, sometimes learning the basics can be the hardest part. The QAC offers several .25 credit classes that introduce students to the necessities of different languages, but even fitting all the necessary information into a half a semester can be difficult. This past quarter, Professor Pavel Oleinikov utilized a website called DataCamp to help his students get comfortable with the basics of Python. DataCamp is an online collection of data science lessons that teaches users through videos and repetitive exercises. The website has an in-browser code box that allows users to code right on the website without having to download any software. Each lesson takes roughly 30 minutes to 1 hour to complete, making it a convenient way to nail down a specific skill.
It can be easy to think of data science as cut and dry analysis consisting solely of numbers. But according to Economics major Leah Giacalone ’17, if people think of it that way it’s just because they haven’t tried it yet. “Personally, I’ve always found being able to code super exciting,” she said. “The first time I wrote code and then it worked was the most exciting thing ever. I always tell people that and they don’t believe me.”
If you are someone who doesn’t believe in the passion underlying data science, then maybe it’s time to give it a go, because an increasing number of companies are utilizing passion as a power source for their problems. An example of this is Kaggle, a website founded in 2010 that allows companies to post their data and research problems online so that people from around the world can compete to create the best solution. Kaggle is using the overflow of big data to its advantage to create a sort of Kickstarter for data science. It’s engaging, fresh, and possibly a good way for data analysis hopefuls to break the ice with coding.