Data scientists really love their jobs, survey finds

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Data scientists really love their jobs, survey finds

According to a CrowdFlower survey, more than 90 percent of data scientists said they were happy doing their jobs, and nearly 50 percent said they were thrilled.

A new study by AI firm CrowdFlower reveals that a majority of data scientists feel as though they’ve landed this century’s sexiest job. While the sexiness is debatable, it’s clear that job satisfaction rates are high within this burgeoning career path.

According to the study, more than 90 percent of data scientists surveyed said they were happy doing their jobs, and nearly 50 percent said they were thrilled.

Data scientists are effectively the human engine behind today’s most pivotal technologies, including artificial intelligence, machine learning, and algorithms and analytics. This report suggests most practicing data scientists are well aware of their importance and relish the job stability.

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4 Major HR Trends You’ll See In 2017

We are already three months past the beginning of 2017, and some HR trends and practices are being redesigned or newly created. While we have seen a dramatic few years down the line in the past; with many new practices and the discontinuity of some obsolete systems, it’s time to look out for the HR Trends 2017; which are going to be in the spotlight. We have rounded up a list of the 4 major trends of 2017 out of the many, which are worth considering.

HR Trends 2017 – An Insight

Ditching the Annual Review System – Time to Rethink

Big-shot companies like GE, Adobe, Accenture, Deloitte, and SAP (recently) announced their decision to ditch the annual performance review system, during 2015-16. After which, many other companies also followed their footsteps. This led to an increasing trend of breaking with the traditional performance review system.

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Identifying Data Bias Early

Everyone loves an employee who comes to work early, a student who arrives early to class, or a first date that shows up early.

For analytics, and especially for machine learning, early is the best time to discover data bias.

Data bias is a shift in data accuracy. For machine learning, that shift creates a dangerous signal that can mislead a model.   Machine learning applies a judgment on data that is the basis for a regression model, a predictive model, or a decision tree.

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