Abstract
Heap Connectivity and Storage Technologies have encouraged companies to gather as much data as possible, whether tracking user behavior on a website, how a plane's engines are performing, or how electricity flows across a grid. But all that data has no value in itself. Someone has to coax useful knowledge from it. Consequently, data scientists are in demand. • But while a lot of companies want to hire data scientists, there is little agreement about what a data scientist actually is. In a Hacker News discussion about my recent View From the Valley blog post on the demand for data scientists, one commenter wrote: “‘Data scientist’ is a title recently thrown around a lot for positions that used to be called ‘data analyst,’ with no strong [machine learning] or [software engineering] ability required.” • So can we say anything more specific? Job-search firm Indeed has sketched out a picture of a data scientist as a technologist with a degree “in computer science, statistics, or a quantitative social science, along with some training in statistical modeling, machine learning, and programming.” Another Hacker News commenter stated: “I would have thought [data science involves] a serious, sustained study of statistics—starting with a strong base knowledge of the mathematics of probability and building from there. But based on the résumés I've seen, that doesn't seem to be the common opinion.” • A fuzzy definition doesn't make filling data science jobs—or helping a data scientist figure out if a job offer is right for them—any easier.
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