Key Data Science Concepts All IT Pros Should Know

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Becoming a Data Scientist: What You Need to Know

Today data science is at the heart of nearly every business and organization. As the streams of data keep growing, there is a greater need than ever before to not only collect it, but sift through it and analyze it to direct decisions. Consequently, they need the skills and expertise of a data scientist, and many even build whole data science teams.

That demand for data scientists is still generally ahead of the supply, which accounts for both the large number of openings and the higher than average salary. According to Glassdoor’s figures, the median base salary for a data scientist is $108,000. It’s not just high pay to make up for a job people don’t enjoy. In fact, it ranks as the best job in America with a job satisfaction rank of 4.3 out of 5.

Defining the Role of a Data Scientist

Far more than a mere quant, the successful data scientist is a creative thinker and problem solver with domain understanding. In light of the fact that extracting value from data entails not just skill but art, some years ago, Venture Beat suggested that “data artist” may be more accurate: “Perhaps these scientists are not the Einsteins and Edisons but the Van Goghs and Picassos of the big data revolution.”

Data scientists don’t merely observe and quantify, but come up with creative approaches to extracting insight and value from data. A successful data scientist is not just someone who has checked off the list of hard skills. He or she has to have the ability to think about how to approach a problem in a new way that opens the way to a solution and then effectively communicate what worked and why.

The question is: What does one have to do to get on track to launch a career in data science? There are core key skills that most people agree on, but there is also the question of the capabilities a data scientist has to possess to do more than merely crunch numbers and program models. In the upcoming sections of this tutorial some experts offer their insight on what it takes to prepare for a career in data science.


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Written by Ariella Brown
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As a technology writer, Ariella Brown has covered 3-D printing, analytics, big data, bitcoin, cloud computing, green technology, marketing and social media. She holds a Ph.D. in English and taught college level writing before becoming a full-time writer, editor, and social media consultant. Her best social media outlet of choice is Google+. Links to her portfolio, blogs, favorite quotes, and photos can be found at writewaypro.weebly.com.

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