Why You Need a Data Scientist on Your Team

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A data scientist is, essentially, a statistician who can code. That might not sound like a big deal, but in reality, companies are chomping at the bit to hire data scientists–so much so that Glassdoor just named it the hottest job of 2016. The power data scientists wield to generate more revenue for growing companies is truly awesome. So what exactly does a data scientist do all day, and why is the field growing so rapidly?

“Data science is sort of a mingling of statistics, programming and machine learning,” said Galvanize Lead Instructor in Data Science Giovanna Thron. “It’s not just knowing how to apply statistics on a data set, it’s also being able to write the code to do anything … to put their ideas into action.”

Thron attributes the swell in demand for data scientists to a few key factors. But first, it’s important to understand what a data scientist does. Here’s an example:

If an online retailer like Amazon has a million users perusing its site, those million individuals’ generate information, a record, essentially, of every click, query, and purchase. From those millions upon millions of data points, data scientists can glean an understanding of the site users’ interests and product preferences. They can then make sense of all of that information, using programming languages like Python or SQL, to provide the company insights about what products, marketing or site design might work well in the future. For example, they can get a sense of a customer through their interactions with the site and expertly target products at the customers they’re most likely to purchase.

That’s extremely valuable.

Say a data scientist is able to increase customer purchases by just one percent with the recommendations she has programmed–the revenue she’ll bring in will more than cover her (ample) salary. With the insights data scientists provide, online retailers today (a field growing by about 16 percent year over year in the last quarter, a pace about seven times faster than all retail commerce growth, according to the U.S. Dept. of Commerce), are doing a better job of serving their customers than ever before.

Job growth in this field is far outpacing the national average, and here’s why the field is growing right now:

First, there are many companies today that are growing large enough to have a wealth of information–the critical mass of users one needs to have to generate enough data points–that good predictions can be made from it.

Secondly, hard drive space, the machinery used to store all of that information, is extremely cheap (it wasn’t always that way), so storing mountains of information is economical.

Next, computational power to plow through that data is stronger than it’s ever been, and getting better all the time. That means clever data scientists have powerful tools at their disposal to create models (using programming languages) that can extrapolate sound findings from unfathomable amounts of data. It’s not your grandma’s statistical modeling.

In doing so, and this is the fourth and most important reason for the growth of data science, data scientists can use their awesome powers to increase revenue in a way old-fashioned number-crunching and business intuition never could.

While online retail is a huge area of growth in the world of data science, it’s by no means the only one. For example, as the world shifts more wholeheartedly to dealing in plastic and other transactions over cash, data scientists are the first line of defense against fraud. Data scientists create models, using their statistics-savvy and programming languages like Python or R, to analyze our past purchasing behaviors and determine the likelihood of whether a given transaction is fraudulent or legitimate. In doing so, they’re able to save billions (if not trillions) every year in prevented fraud and protect you from having your money stolen.

Data scientists are also crucial to insurance companies being able to build ever-more efficient and precise cost structures by analyzing risks. They also generate revenue for companies like Twitter, Facebook, and Instagram, who sell valuable targeted advertising space by scraping sites for user data and making sense of it to target ads to the most relevant users.

Whatever the field, companies can dramatically improve business with that kind of precise insight. No wonder employers are clamoring for data scientists faster than universities can turn them out.

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