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Data scientists need to make sense of the big picture, rather than the big data

The web echoes with cries for help with Learning data science. “How do I get started?”. “Which are the must-know algorithms?”. “Can someone point me to best resources for deep learning?”. In response, a bustling ecosystem has sprung to life around learning resources of all shapes and sizes. Are the skills to unlock the deepest secrets of deep learning what emerging data scientists truly need though? Our research has consistently shown that only a minority of data scientists are in need of highly performing predictive models, while most would benefit from learning how to decide whether to build an algorithm or not and how to make sense of it, rather than how to actually build one.



This post first appeared on Developer Economics, please read the originial post: here

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Data scientists need to make sense of the big picture, rather than the big data

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