You’ve learned so much to become a Data Scientist. Now, it’s time to kick it up to the next level with advanced soft skills – because these are important to the business for which you empower to make better decisions. Learning from the business leaders you support will help you develop a broader set of enhanced skills that will boost your Data Science quality and output.


By Rhea Moutafis, Pursuing PhD on Dark Matter physics.

Business skills won’t compromise your scientific integrity. Photo by CoWomen on Unsplash.

Data Scientists are accustomed to teaching business leaders about their craft. And business leaders are accustomed to learning loads of stuff from many different fields.

So it’s no wonder that the internet is filled to the brim with courses on Data Science for business leaders — from edXCoursera, and many more platforms.

But what about the opposite way around? Somehow Data Scientists are treated like they’re magic gurus who can talk to data and extract something business-y. There’s only one catch: the fact that most Data Scientists know nothing about business.

At the moment, the job market for Data Scientists is pretty awesome. But that doesn’t mean that you shouldn’t continuously enhance your skills. Without further ado, here are seven key competencies of business leaders that every Data Scientist should acquire.

1. Efficiency

You’re a scientist. If you’re like me, you love roaming around in datasets, without a focused question, and let the data speak to you.

But here’s the thing: you might be wasting your time.

Be honest with yourself. How often do you decide to explore something, and find nothing? How much of your day do you spend investigating things that aren’t useful to your client?

If you’re like me, a lot of your time doesn’t lead to actual results. In fact, I often spend most of my time exploring things and learning new stuff. And when I don’t watch out, I spend too much time generating no output whatsoever.

Business leaders are always thinking about becoming more efficient. Their top priority is trying to understand how to maximize their output with a minimum of effort.

Of course, you need to look at your data with a fresh eye and an open-ended mindset. But don’t let that lead to less output.

I like to schedule a time every day where I can freely roam around in the data. The rest of the day is dedicated to the goals of the project. This way, I’m prioritizing goals while keeping open to new findings that I would have otherwise missed.

#2020 jul opinions #advice #business #data scientist #data analysis

What every Data Scientist needs to learn from Business Leaders
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