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Six years after the Harvard Business Review dubbed it "the sexiest job of the 21st century," data scientist remains one of the most sought-after roles in business. This year, Glassdoor once again named it the best job in America, based on job satisfaction, median base salary ($110,000), and job demand.
Companies create entire departments to collect, prepare, analyze and clean data to glean business insights from it. These learnings inform and improve integrated marketing programs, content strategies, cybersecurity strategies, customer experiences, and much more.
The ability to gather and organize data puts businesses ahead of the competition, though it is a difficult task. That’s why data scientists continue to be in such high demand.
If you’re intrigued by the employment opportunities data science provides, but think you lack the technical chops required for a job in the field, think again. There are viable career paths for people without a tech background—if you keep these four things in mind.
Without a tech background, you obviously aren’t going to slide into a machine learning engineering position where you leverage natural language processing to work with enormous data sets. But data science is a large umbrella with myriad roles that require different levels of technical proficiency.
Like data analysts, who sift through data and provide reports and visualizations to explain what insights the data is hiding. In some ways, you can think of them as junior data scientists, or the first step on the way to a data science job.
Business analysts are adjacent to data analysts, and are more concerned with the business implications of the data and the actions that should result. If a company is considering which project to invest its resources in, business analysts will leverage the work of data science teams to communicate an answer, taking advantage of business intelligence tools.
Taking a step back, it’s important to remember that while hard skills like coding and statistics are required for many data science jobs, soft skills also are important. Among them:
A passion for learning
Strong decision-making abilities
Communication is key, as an important part of a data scientist’s job explaining data to other people.
But arguably the most important trait required is curiosity. You have to want to dig beneath the surface of what might be a difficult problem. You need to ask the right questions and pull insights from the data (which brings you back to communication).
While you might not have a technical background now, you can develop those skills through self-learning and more formal education.
You might follow data science blogs, read news articles about the industry, follow influencers on social media, or even up look TED Talks about how data science is changing the world. To give yourself a leg up on your competition, you may want to concentrate on learning a bit about:
Statistics and probability
Algorithms and coding
You can also find free online courses and degree programs that introduce you to these topics and help you advance your skills.
Google some of data science’s more prominent figures and you could find some surprises. Chris Wiggins, chief data scientist at the New York Times, started as a biology researcher. Bob Roger, chief data scientist for Analytics and AI Solutions, has a Ph.D. in physics. Doug Cutting, creator of the Hadoop framework, earned his degree in linguistics.
By looking into their backgrounds, you can learn how people from diverse backgrounds found their way into data science and how you might do the same.
At the very least, their stories can offer you some encouragement. Read articles they helped write or that feature them. Follow them online. If possible, reach out and see if you can make a connection. They might have some great advice for you.
If they seem out of reach, you can use LinkedIn or other professional networks to find people who might have some time to offer you tips. Offline, there are meetups in every major city that offer established professionals and newcomers opportunities to network.
With guidance from mentors, a willingness to learn some of the technical skills you lack, and curiosity about the field and your work, you will be able to find a data science job, particularly in data and business analysis. You simply have to roll up your sleeves, get out there, and do the work.
This post was written by Quincy Smith from Springboard, a company aimed at helping bridge the world's skills gap through expert-led courses like their Data Science Bootcamp.
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