If you look at any website with an audience in an IT field, the odds are good you’ll have heard of it. Unfortunately, despite the widespread popularity of data science, misconceptions abound. There’s so much misinformation about the field that both entrepreneurs looking to utilize data science in their startup and students planning out their career path can be turned away without good cause.
Don’t let yourself be one of them. Brush up on seven of the most common misconceptions about data science.
It doesn’t matter. Not one bit. The reality is this: a true data science values the technology that he feels most comfortable with. Data scientists use whatever technology stack matches the needs of their employers or their clients. That often isn’t the answer that people want to hear.
Data scientists are problem solvers. If a tool doesn’t advance that goal, then there’s no point in using it. Don’t get lost in the weeds chasing after the “hottest” tools on the market. Instead, focus on the problems and search for the tools you need to solve them.
Because of their huge number of data inputs, larger companies, like Google and Facebook, seem best equipped to make use of data science. That’s certainly true. However, even smaller companies and non-profits can take advantage of the solutions that data scientists can offer them. As long as your company deals in data, your company can benefit from the knowledge and expertise of a data scientist.
The more data you have, the more you can benefit.
No. It’s about solving problems. If a complex model is necessary to overcome your company’s obstacles, then use one. Otherwise, a complex model isn’t just unnecessary. It might actually be detrimental to your long-term goals.
Occam’s razor states that the simplest solution is usually the right one. It’s just as true here. Using simple models to solve complex problems is part and parcel of data science. There’s no reason to break out overly complicated algorithms when they aren’t needed. The challenge of being a good data scientist is in knowing which type of solution to use for which problem.
There’s no doubt that coding is a huge part of data science, but learning how to be “the best at Python” isn’t how a data scientist approaches their studies. They make use of Python because it’s elegant, easy to use, and not so inefficient as to be problematic in their work. In other words, it lets a data scientist focus on the problems.
Likewise, learning SQL isn’t a goal in and of itself. These are just the languages of data. They’re tools, and as long as you can use those tools to increase revenue for your clients, you’re doing your job as a data scientist.
It isn’t about code. It’s about problems.
To be a data scientist, you need to be well-educated and qualified. A doctorate is the simplest way to show that to an employer, but it isn’t the only way. In fact, most data scientists don’t have them. A doctorate is usually pursued by scientists looking to get into research, not industry.
Master’s degrees are far more common, but even then, it isn’t a strict requirement. Instead, it’s a leg-up on the competition and a sign that you know what you’re doing. If you have extensive experience in the industry or a long history of merge requests on an open-source platform, you might even be able to get a job with just a bachelor’s.
The idea that there's magic to data science is the single biggest misconception there is. You don't need to be a math geek to be a data scientist. Data scientists need to be good at manipulating data to match the needs of their customers and employers. Sometimes that involves using a little math, whipping up a quick program in Python, or typing a few queries into MySQL. But none of those things are what a data scientist is. He isn’t a mathematician, or a programmer, or a server administrator.
He’s a data scientist, and he’s had it with misconceptions. Have you?
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