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.
November 10th-12th saw the long awaited and oft rescheduled coding event, Data Dive, finally come to fruition. About twenty-two Wesleyan students gathered in the Exley fish bowl on Friday evening, and split into teams that tackled a multitude of questions related to gun data. After two days of work, the groups presented a fascinating combination of visualizations, analysis, and activism.
The participants explored a wide range of topics, from connections between gun sales and mass shootings, to location patterns of gun sales, to keywords that purchasers typically search for. As Wesleyan’s partner for this event, EveryTown, provided the volunteers with several extensive data sets. One of these was compiled from Armslist, an advertising site specifically allowing private sellers to market their guns to other individuals. While accessing the site requires confirmation that the individual is “18 years old, will follow all local, state and federal laws, and will not use Armslist for any illegal purposes,” Armslist itself does not ensure that buyers have a license or have completed a background check. This has led to the site being criticized by organizations like EveryTown on its lax approach to gun circulation.
On October 27-28, Wesleyan will host our first DataDive event. Similar to DataFest, DataDive asks people to get to know a dataset in a very short period of time. At the end of the weekend, participants present to the information’s owner some important insights and ideas on how this data could be used. However, where DataDive is truly different is its desire for volunteers to understand specifically how their data could be used for good.
As Drop/Add has ended, we have all finalized our schedules – for better or for worse – and decided which classes we will be taking for the next semester. A lot of you will be taking classes at the Quantitative Analysis Center; this year’s WesMaps shows that almost every QAC class is at or over max capacity for enrollment. However, maybe some of you wanted to take a class to develop some data-related skills, but had absolutely no idea where to start. I’ve had many friends come to me saying that they’ve realized these might be good skills to have, but that they feel like they don’t understand enough to even pick a course. While before I may have given a few recommendations, I would now give only one: QAC201.
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.