Whole > Parts.sum( )
My emotions ran the gamut through the first phase of my data science foray. I found myself excited at the prospect, curious for future discoveries, overwhelmed, and determined. It’s necessary to feel all of the feelings (and yes, the absence of feelings is arguably a feeling). But one lesson sticks out through this emotional rollercoaster (that gives me flashbacks to middle school): the whole is greater than the sum of their parts. While I think this is a vital lesson in life in general, I find it relevant to data science. I found this synergistic collaboration a few ways during this first month.
A Student Within a Cohort
First, I learned the value of fellow students. Yes, the instructor and coaches are vital. But the process is enriched by collaboration with your peers. There is no better way to learn than to hack a problem with a classmate or two until the dreaded error sign no longer returns.
Beyond technical help, conversation with those who are going through the same experience builds comradery — a sense of place and purpose. I think it’s valuable to have co-conspirators when the obligatory impostors syndrome arises. You realize you’re not alone in these feelings. Plus sharing one’s struggle with peers is quite cathartic. During the insanity that is 2020, we could use a little more catharting together. (If nothing else, I think 2020 is the best evidence for the belief that we are in a simulation. But I digress.)
Partners In Data Science
Not as catchy as partners in crime but just as cool; side-by-side work with a fellow student is especially beneficial. Not only is there sharing of workload, there is also the opportunity to create work that is better than what you could achieve alone. We all bring our diverse life experiences wherever we go. And this leads to unique perspectives that are some of the world’s greatest gifts. For when another sees the world from a different angle, we can learn and broaden our view.
From a less philosophical angle (I’ll put away the Hemingway and whiskey), diversity of thought allows you to approach a problem from many angles. This could be a piece of code that you’re working on or questions to investigate. One example from my first project was my partner’s suggestion to look at foreign markets. As someone born in the US, I may default to focusing domestically. Yet my partner had different life experiences and shared a new view. Moreover, in collaboration, you may arrive at a new answer that wouldn’t appear alone. Herein lies synergy.
Storytelling with Data
Data is notoriously boring for the majority of the world. No no, it’s ok, you’re not boring… just your data. Don’t get me wrong, I geek out over data and insights. But most people don’t. And that’s ok. It’s why communication is part of our job as data scientists. And a rather important partnership is data and storytelling. The power of stories is that it plays on a primal portion of our brain — it’s hacking biology to convey a lesson, sell a point-of-view, or unite a community. Stories are good alone. Even more powerful is combining narratives with the scientific method. Science reveals the mysteries of the world in hard facts through data. With both, we unlock the riches of our natural world through communication and influence. It doesn’t matter how beautiful your clean dataset looks to you. What matters is that you convince a person, a society, or the world of a previously unseen truth.
Onward and Upward
So as I round the corner of my first month of bootcamp, I’m tired, eager, and ready to move forward. In a symbiotic gesture, a soon-to-be graduate gave us the wise advice: “You can’t go backward, you can’t stay here, so your only choice is to move forward or give up, and I don’t consider the latter an option.” This resonated with me. It doesn’t make the journey easier, but there is comfort in the solidarity of a cohort. Keep your eyes open for opportunities to create with others — you too may find that the whole is greater than its parts.
