“Data scientist is the sexiest job that has ever or will ever exist.”
These are just some of the things we hear about data, AI, and data science everyday. By now most of us can parrot back the sentiments in loose, noncommittal terms. Some can even weave the tropes, aphorisms, and hype into sentences. Usually that’s more than enough to convince leadership to hire a data scientist or to flesh out a slide as to why those hires were so pivotal.
But beyond the pitch and the powerpoint, we can’t rely on vague-ities. Not if we want to make the most out of those hires. If we want to capitalize on what data science can offer, we need to communicate in specifics, particulars, and with common understanding.
That is what this series is for; to empower you to communicate effectively with your data science, AI, and data teams.
This series is not going to make you a data scientist–and that’s not the intention. You don’t need to know how to train a model to know what it means to do so. You don’t need to be able to engineer features to know what data scientists are doing when they say that. You don’t need to know what cross-validation is to know why it’s important. You don’t need to know the details to understand the idea.
If you believe you can communicate without sharing a common language, I encourage you to discuss your favorite hobby with someone uninitiated in it. See how long you can go without explaining a term.
The fact is, jargon is everywhere–and it’s particularly prominent in this field. So, yes, we “better get used to it.” But jargon exists for a reason: it enables us to communicate specific, complex ideas quickly. And this field is chock-full of specific, complex ideas. Jargon just makes those ideas easier to communicate. So, really, we ought to embrace it.
Now, data scientists have this easy; they learn the jargon while they learn the job. It’s second nature for them to use the right words to describe their work. It’s clumsy, imprecise, and generally inaccurate to translate the right words into those that outsiders will accept.
So, let’s let the data scientists use the right words.
But how can you do that? By learning those words.
For all you not-data scientists out there, I’m making a cheat sheet.
If you want to learn the jargon, instead of just reading why it’s important to learn it, look for “Talk the Talk” posts or search the “jargon” tag. That’s where you’ll find bite-size installments of a growing cheat sheet of data science terms for non-data scientist.