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.
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.
In 2013, Netflix came out with Orange is the New Black, one of the first original series to be debuted on an online streaming network. It was an immediate success, and ushered in years of Netflix continuously “getting it right”: House of Cards, Arrested Development, Unbreakable Kimmy Schmidt, numerous Marvel shows, Sense8 – and, most recently, Stranger Things.
What’s fascinating is that there seems to be pattern of streaming video networks coming out with great original shows while cable TV shows are declining in quality and originality. When Stranger Things came out in early July of 2016, Netflix had another hit, and I heard many people saying in awe, “How does Netflix keep getting it right?”
It turns out there’s a secret to their success: Big Data.