What Programming Languages Do You Need to Work in Data Science?

Data science employers want their software engineers to know Python, along with Hadoop, R, and Spark, but the picture is changing quickly

1 min read
Illustration of people working with data in different ways
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Data scientists and software engineers who work with big data are in high demand. Thinknum Media called this field the hottest profession in 2019. Job search site Indeed earlier this year reported that job listings for data scientists jumped 31 percent between 2017 and 2018, while searches only increased 14 percent.

But what skills do you need to fill this lucrative niche?

Indeed set out to answer that question by looking at 500 tech skill terms related to data science that appeared in tech jobs posted on the site during the past five years. The analysis determined that, while Python dominates, Spark is on the fastest growth path and demand for engineers familiar with the statistical programming language R is also growing fast. Also on the radar: Hadoop, Tableau, SAS, Matlab, Redshift, and TensorFlow. [See graph, below, which omits Python because demand is literally off the charts, and because it is not strictly a data science skill.]

In terms of exactly how these skills are being applied, Indeed looked four fields that require data scientists. Machine learning came out on top—and is growing the fastest—followed by artificial intelligence, deep learning, and natural language processing. [See graph, below.]

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Asad Madni and the Life-Saving Sensor

His pivot from defense helped a tiny tuning-fork prevent SUV rollovers and plane crashes

11 min read
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Asad Madni and the Life-Saving Sensor

In 1992, Asad M. Madni sat at the helm of BEI Sensors and Controls, overseeing a product line that included a variety of sensor and inertial-navigation devices, but its customers were less varied—mainly, the aerospace and defense electronics industries.

And he had a problem.

The Cold War had ended, crashing the U.S. defense industry. And business wasn’t going to come back anytime soon. BEI needed to identify and capture new customers—and quickly.

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