Why Data Scientists Are Happy, but Concerned

By Dennis McCafferty
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    Why Data Scientists Are Happy, but Concerned

    Why Data Scientists Are Happy, but Concerned

    Job satisfaction among data scientists has increased sharply, but there are drawbacks, including a lack of good training data for machine learning projects.

It's good to be a data scientist these days: The vast majority surveyed said they are either "happy" or "very happy" at work. They're also in high demand, with most getting contacted at least once a month for new job opportunities, according to a recent survey from CrowdFlower. However, the resulting "2017 Data Scientist Report" reveals that there is considerable room for improvement. For example, data scientists would prefer to spend their workdays building and modeling data and mining it for patterns. Instead, they spend the majority of their time on what they prefer to do the least: labeling, cleaning and organizing data. And they also indicated that a lack of access to good training data remains the biggest bottleneck for artificial intelligence (AI) and machine learning projects. (The term "training data" refers to labeled and/or structured data sets that are used to train algorithms to better process subsequent unlabeled or unstructured  data sets.) "[Data] quality levels are less predictable, and lack of access to high-quality training data is the single biggest reason AI projects fail," according to the report. "Given the massive proliferation of AI projects in virtually every sector across the globe, data scientists must work to offload routine work and streamline processes in the face of increasing data, increasing AI projects and a continued shortage of those with the necessary skills." A total of 179 global data scientists took part in the research.

This article was originally published on 2017-05-03
Dennis McCafferty is a freelance writer for Baseline Magazine.
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