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.
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Going Back to the Basics?
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.
Detecting Trends in Community Engagement: At Wesleyan and Beyond
When it comes to activism and community service, Wesleyan has always tried to stay ahead of the curve. But this can be difficult, as the concerns and trends of community engagement are constantly shifting. Often, new topics will seemingly erupt out of nowhere, and it will take a while for word to spread. There are so many existing concerns that it can be difficult for new voices to be heard and for old voices to catch on to the changes. It might seem as though the trends in community engagement are shifting constantly, without any pattern. But can technology detect one?
Making Books Unfamiliar: The Art of Novel Analytics
On March 2nd, Matthew Jockers gave a talk at Wesleyan about his research on using quantitative methods for analysis in literature. His talk was titled “Novel Analytics: From James Joyce to the Bestseller Code.” The following article is an exploration of his talk and the ideas he brought forward.
What makes something a piece of art? This might sound like a pretty theoretical question, but English professor Matthew L. Jockers believes that it is possible to take a technical approach.
“Art shows how things are perceived, not known,” Jockers explained. This is a definition that could cause tension in the literary world. After all, writing is messy, personal, and painfully subjective. And yet – “We tend to emphasize the idea that the text is withholding an ‘essential truth,’” Jockers explained. In this way, a literary critic wants to be able to anticipate a certain meaning, causing an endless tug-of-war between objectivity and subjectivity. Jockers does not wrestle with this tension, as evidenced by his book The Bestseller Code, in which he uses analysis to tackle that all-elusive question: What makes a bestselling novel?
Racism and Diversity in Data Analysis
In light of the recent election, it is more important than ever to look at how and where we are responsible for perpetuating prejudice. In a previous article, DataCrunch introduced the concept of “Weapons of Math Destruction,” which are data models built from a limited or biased sample of data that result in toxic feedback loops. Since this explanation is most often attributed to artificial intelligence, there is little discussion about how this description could also illuminate the workings of the human mind. While many might want to think of this narrow-mindedness as below the mental capacity of human beings, such a viewpoint is dangerous in that makes having a conversation about prejudice difficult.