Using Data Science to Meet Better Friends
“That person is a total dolphin!”
You’re probably asking what that could possibly mean. Well, I had recently moved to Boston and was actively looking for new friends that I could become close to. While being an extroverted person, I found it difficult to find just the right kinds of people that I was looking for — people who had a twisted dark sense of humor or would at least tolerate it, conversations that just went on and on with no sense of time passing, a carpe diem attitude towards life — you get the idea, we all have some qualities we love to see in friends.
Some people say that it’s harder to make friends after college/graduate school. But as one of my new friends and I were navigating various social events, we felt that as we get older, our standards for company become higher, and this naturally restricts our options.
This is often overlooked when people mention the concept of making friends after college, but it’s so important in realizing what we are working with. We started doing more research on this in our spare time, and truly evaluating what we ourselves were looking for, in new friends.
What is a “dolphin”, you say?
Well, this led to us hosting some social experimental events and “friendship focus groups” of our own, and attempting to play platonic friendship-matchmaker with everyone who attended these events. Most of the attendees were energetic, friendly and amicable, but as nature goes, not everyone fit this description. We definitely received our fair share of humans who brought negative energy towards others, had trouble respecting others’ boundaries, etc — and hence coined the term “dolphin” for those who actively worsened these event experiences for others.
Over time, the term progressed as an inside-team joke to include anyone that we didn’t personally see ourselves being friends with; this was not meant as an insult, but rather a signal as to our own priorities in types of friends we were seeking.
This heightened awareness was very fruitful in our own personal endeavors to find more close friends in our new city.
I say all this to answer the question — why learn data science? Our platonic friendship-matchmaking process was doing quite well after a year or so of experimenting; we hit over 70% success in achieving a second hangout among those that we matched together, and were even developing an app in our spare time as well.
However, we wanted to do more and scale our efforts to have a larger impact on fellow humans.
I personally was going back and forth on doing a data science bootcamp for a while, and when my cofounder [who was the one experienced in data science and making apps] decided to move to Florida to “find himself”, whatever that meant, I took that as a sign that it was time for me to skill up and apply some new knowledge to the startup.
The idea of curation has always appealed to me in many aspects of life — even something as simple as Amazon predicting what new grocery items I might like, is delightful and thrilling to me. In friendship, as any interpersonal dynamic, I find it’s especially important to utilize curation for optimal experiences. I started my Flatiron School bootcamp journey in order to bring meaningful introductions between humans that might not otherwise meet, but would be likely to have an amazing bond together. Machine learning had always fascinated me, as well as building in general — it seemed that several of my passions and interests coincided at this point.
Building opportunities to create experiences that are better than random chance — that’s why I decided to enroll in a data science bootcamp.
That’s the funny thing about coincidences, though — in mathematics, the term “coincident” refers to two lines or objects that are drawn right on top of one another — so it doesn’t seem like much of a random “coincidence” after all.
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