What I call monogamy in a technological environment is to remain faithful to only one development language. So yes, I know you’re thinking coding and being married (or in a relationship) are two completely different things. I don’t agree. In the following article, I’ll walk you through my field investigation into the polygamous lives of data scientists.
Monogamy, oh what a word! Lust or restraint, property or freedom, single or plural, nature or nurture, all types of opposing concepts come to mind. So why in the h*ll are the words data and science coming to ruin all of this excitement?
Everything began a few weeks ago as my friends and I sat at a coffee shop discussing marriage, relationships, and… monogamy. That’s when the question we’ve all asked ourselves at least once came up: are human beings meant to date only one person at a time? Does this belief come from our nature or from what society expects of us?
As the afternoon went on, and as the conversation reached its climax, my friends and I came to the same conclusion: monogamy is not natural. Let me explain our reasoning: by nature, every species wants to produce the best possible offspring, and the odds of this happening only increase by multiplying partners. It really all comes down to basic science.
At this point, you’re still wondering how data science relates to any of this aren’t you? Well, as I walked home later that day, my mind began to wander… I thought about the conversation I’d just had with my friends. I thought about the concept of monogamy. I thought about work (because hey, what working person doesn't?). And that’s when monogamy and data science collided. It’s not as crazy as it sounds - let me explain.
What I call monogamy in a technological environment is to remain faithful to only one development language. So yes, I know you’re thinking coding and being married (or in a relationship) are two completely different things. Personally, I don’t fully agree. I believe both are a question of love, hate, addiction, fatigue, and pride all mixed up together…
In your daily work, you are maybe already used to being unfaithful - ie. using different technologies to fulfill different needs. In this case, congrats, you’ve made my point on your own and you can probably just skip to the last part of this article for best practices in technological infidelity.
As for the rest of you, keep on reading. In the following article, I’ll walk you through my field investigation into the polygamous lives of data scientists.
When managing a data science project that involves multiple people some may feel that imposing one single tool for everyone is the most efficient approach. Why? Because it’s nice to have standardized code in one language, to ensure that code written by anyone is compatible with the rest of the codebase. BUT, very often, the perfect library to solve your problem is written in another language. In this case, it seems that the best approach is to rewrite the whole thing in your favorite language or to write bindings to the other language's library. But, as most people who are still reading this article know, doing the latter not only limits full performance, but can also give way to bindings that are incomplete or not up to date, making compilation and execution harder.
Ok, you get it: this is a perfect example of the ineffectiveness of technological monogamy.
So what can you do? What languages should you use for what specific use case? To answer these questions, I went and asked our team what they thought the main advantages of each languages and tools are, and how you can make them work together. Because yes, exploring new possibilities, breaking down some barriers, and taking risks feels great. And empowering. Turns out, this isn't only true in life. It's true in data science as well.
R, the statistical computing language:
First of all, its statistic libraries are amazing:
And the perks don't stop there:
Python, for data science MacGyvers:
SQL, the first, the last, the everything?
Julia/Matlab, a scientist’s first love
Scala, the hipster data scientist’s language of choice
This is all well and good but you wouldn’t build an Ikea shelf with the wrong screwdriver, and the same goes for a data project! None of these languages are right for every single thing you want to do. You need to switch screwdrivers when you change project (or a least make sure the one you’re using is still the best).
However, even if YOU can switch languages or tools, when you’re working on a data science project, the problem is to get people to work together, each with their own languages. You need a common environment where you can share information about the projects, see what everyone else is doing, and understand how the project is moving along.
When you do, multilingual data projects are a real plus! Some of our data scientists won a Kaggle competition thanks to language mixing. They were also working in three different cities and on three different schedules. How did they do it? They used Data Science Studio. Why? Because:
However, back to our monogamy issues. The problem with most amoral activities is usually the planning, or THE ORGANIZATION that goes around it… The real reason it’s so scary to cheat on a partner is that it requires a lot of extra organization and careful planning. For data science polygamy to succeed, you need to make sure that every member of your team - or partnership - can communicate, collaborate, and plan according to and with one another. Same goes with polyamorous relationships. Just saying.
So how exactly is this technological "ménage à trois" supposed to work? It all comes down to one concept: collaboration. By this point, I hope I’ve convinced you that using various languages in data science projects is a good (the best) way to:
So now you're thinking: “Ok, great, this girl has just sold me some sort of utopian data science environment. But in reality, complete technological polygamy in a unified environment is impossible. I know because I've tried.” Well, you are wrong. It is possible! That’s right: we don't only believe in bringing data, technology, and people together, no matter their level of expertise or skill set. We actually do it, one deployed and running data product at a time. In DSS, we believe that teams can only be effective if all members come as they are.
To find out more, and to get actionable tips on how to create such an environment for your teams, download our free ebook: “Building a Successful Data Lab”.
I’ll conclude with a few words of wisdom that apply to both life and work: monogamy is a choice, not an obligation. On that note, wishing all polygamous and monogamous people out there a climactic Valentine’s day!
PS: I could have talked to you about the Babel tower too, right... but it wouldn’t have been as much fun:)
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