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Under Armour SpeedForm Shoes Apollo 2 Womens Running Shoes SpeedForm 1266241 913 Teal/Pink NIB 101b22

Under Armour SpeedForm Shoes Apollo 2 Womens Running Shoes SpeedForm 1266241 913 Teal/Pink NIB 101b22

Item specifics

Condition:
New with box: A brand-new, unused, and unworn item (including handmade items) in the original packaging (such as ... Read moreabout the condition
Brand: Under Armour
Color: Blue Style: Running, Cross Training
Pattern: Solid Width: Medium (B, M)
Fastening: Lace Up Material: Canvas
Country/Region of Manufacture: China Heel Height: Flat (0 to 1/2 in.)
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January 12, 2018

Under Armour SpeedForm Shoes Apollo 2 Womens Running Shoes SpeedForm 1266241 913 Teal/Pink NIB 101b22

Kayleigh Karutis

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This post originally appeared on the SwitchUp.org blog.

LinkedIn’s 2017 U.S. Emerging Jobs Report—fittingly created with the power of data science—lists data science roles as one of the top emerging positions in the U.S. today, with 6.5X growth over the last five years.

IBM takes a slightly more conservative view, predicting demand for data scientists to grow by about 30 percent by 2020, but even still, those are compelling figures. As demand grows, talent supply for these roles continues to lag behind; LinkedIn alone lists more than 6,000 open data science positions, and that’s only in the U.S. Companies across the world are recognizing the need for talented data scientists on payroll, in virtually every industry known to humankind.

As we round the corner on 2018, let’s take a look at some of the trends and predictions expected to shape and drive this high-demand field over the next year.

1. The skills and responsibilities required of a qualified data scientist will become more clearly defined.

As the field has grown, a certain amount of fuzziness has occurred around the actual meaning and definition of “data scientist.” It could be argued that at this point, “data science” is a full-on buzzword.

In 2018, hiring managers and recruiters will begin to drill deeper into the specific skill sets and knowledge these professionals must have—the ability to build and test hypotheses, statistical and visualization understanding, machine learning knowledge, and model-building skills, to name some.

2. Specializing in machine learning will be an even more worthwhile path to pursue.

Data scientist was No. 2 on LinkedIn’s list of top emerging roles. Number 1? Machine learning engineers. There are nearly 10 times more machine learning engineers in jobs today as compared to five years ago, and the site lists nearly 2,000 open positions.

Ideal candidates in this field will combine their knowledge of software engineering with data science. Successful software engineers who expand their skillsets into data science through programs like Springboard’s Data Science Career Track will be uniquely positioned to snag these high-demand, high-paying jobs.

3. Data scientists with design chops will have an even bigger role to play.

Companies now recognize the critical importance of using data to drive decision making, at every level of an organization.

As the need to understand and socialize data across organizations grows, data scientists with an understanding of how to design data in a visually pleasing and easy-to-understand way will be uniquely positioned to spread data’s (and their) impact across their organization.

4. Having an understanding of agile methodologies within data science will be even more important.

The agile approach swept the design world years ago, and it’s sweeping the data science world now. Taking a fast-moving approach to this discipline—by using small, cross-functional teams focused on specific, targeted goals—can help companies move more nimbly, and solve their problems with increased efficiency.

5. Specializing in a particular field will become more important.

Because demand is so darn high, even the most general data science practitioner will likely find success. Those who do best, though, and land in the most fulfilling and impactful roles, will be the scientists who specialize in specific areas within data science itself.

That can mean diving deep into a particular methodology or technology tool stack, or focusing intently on one niche industry, but either way, those practitioners who hone in will find themselves in even higher demand, and with the freedom to pick and choose their future role.

6. Everyone will be paying attention—and will have an opinion.

“Data science” is no longer a jargon-y term that gets thrown around the water cooler at startups and tech companies. When companies like Netflix use their trove of data to call out a hyper specific subset of users, causing an uproar across the internet and calls for rules around data mining, you know data science has made its way to the mainstream.

Beyond the playful, data science also has a massive role to play in emerging technologies like AI, AR, and in the security realm, which naturally makes it fair—albeit controversial—game for dinner party fodder. With so many open roles, and nonstop news stories about data’s importance, the spotlight is on—for better or worse!

No matter how you slice it, data science has a massive role to play in 2018, and so do pros in the field. If you’re interested in launching your data science career to get in on the data rush, there’s never been a better time!

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Kayleigh Karutis

Kayleigh is a dog lover and Denver resident. She oversees content and product marketing @MasonAPI and has formerly worked @Springboard and @InvisionApp on content strategy.

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