A lot has been written (including by me) on what it takes to be a good data scientist; what skills and traits are essential for the job and even how to acquire them.
Remember, I’m using data scientist as a bit of a catch-all term to include data analysts, engineers, programmers, and more. But taking into account that we want data scientists to have a balance of common sense, business skills, creativity, a geeky love of statistics, and data skills management skills, I was curious what sorts of traits would be the opposite? What would add up to make a bad data scientist?
1. Someone who doesn't like scientific enquiries.
Let’s face it: if you like things cut and dried, black and white, no grey areas and no questions, data science is probably not for you. You might think it’s all about facts and numbers, but the truth is, every set of data can be interpreted in multiple ways, and more often than not, an analysis of a set of data will lead to some answers and a lot more questions. So if you don’t enjoy the pursuit of one hypothesis after another, this is probably not the field for you.
2. Someone who loves to make decisions on gut feeling.
There’s nothing wrong with gut feelings. Some of the best leaders and CEOs out there have built their empires on gut feelings. And while there is a place for intuition in data science, it has to be backed up by the facts. If you find that facts just slow you down, data sciences may not be for you.
3. Someone who only likes the detail.
If you’re only good at number crunching, for example, and don’t want to zoom out any further than that, data science may not be for you. There may be a place for you in the data world, but a data scientist has to have the business insights, bigger picture, and creativity to interpret the numbers.
4. Someone who doesn't like details.
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