Part of working in a Data Science role is about "storytelling". Yes, you read it right & I'm not joking either.
Working in a Data Science team involves a ton of communication while keeping the context simple, straight-forward & easy-to-understand for the non-technical higher-ups in a corporate setting. In fact, contrary to popular belief, an individual working in a Data Science role would rarely be building state-of-the-art predictive models to identify if an approaching celestial object in the sky is a meteorite or a black hole, pun intended.
Sorry to break to you, if you were thinking otherwise.
Suffice to say, besides coding, a Data Science role demands more than what's usually comprehended. Like, Domain Knowledge, among a few other skills, is often overlooked & considered as an underrated skill set. While quite opposite to the fact, Domain Knowledge can prove to be uplifting for the overall capabilities of the Data Science team.
I've shared an article towards the ends of this post, about Domain Knowledge and its use case. Check it out, I'm sure you might learn a thing or two ;)
Besides, while working with real-life datasets, it isn't uncommon to come across unclean & unprocessed data adding up the additional skillset requirements! For example, "handling missing values, properly" is a task, requiring careful assumptions and meticulous investigations. Failure to do so can allow bias to creep in & might as well make the model useless or not generalize well to real-world scenarios.
Well, unfortunate for us, the thing is, observed values might be missing for a variety of reasons, unknown to the analyst. Hence, it is critical to figure out the exact reasoning behind the missing values or at least if possible make certain plausible assumptions about them.
But, the topic of Missing Values is unrelated to this write-up, So I wrote a fairly detailed piece of article here - Dropping Missing Values? You Probably Shouldn't. I highly suggest that you take a look at it.
Regardless, my point is, if you're working in a Data Science role or if you plan on working someday, then prepare yourself because communicating your findings is equally crucial, just as the rest of your job. So, my advice? Start working on the next Data Science project & blog about your findings if you've not started it already.
Interested in reading more of my writings? Then you might like what I've written elsewhere too!
A short abridged version of this topic is available at - Data Scientists ARE Actually Storytellers
Want to read how your extensive years of Domain Knowledge might turn out to be useful to a Data Science team? Then check out - Domain Knowledge - Perhaps the Second Most Important Skill in Data Science
Besides, would you like to reach out to me? Then feel free to drop a tweet on Twitter or subscribe to my mailing list.
Cover Photo by Corinne Kutz on Unsplash
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