Top 10 Data Science Companies to Work For in 2020

In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, "I think data-scientist is a sexed up term for a statistician....Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician."[26]


On the other hand, responses to criticism are as numerous. In a 2014 Wall Street Journal article, Irving Wladawsky-Berger compares the data science enthusiasm with the dawn of computer science. He argues data science, like any other interdisciplinary field, employs methodologies and practices from across the academia and industry, but then it will morph them into a new discipline. He brings to attention the sharp criticisms of computer science, now a well respected academic discipline, had to once face.[27] Likewise, NYU Stern's Vasant Dhar, as do many other academic proponents of data science,[27] argues more specifically in December 2013 that data science is different from the existing practice of data analysis across all disciplines, which focuses only on explaining data sets. Data science seeks actionable and consistent pattern for predictive uses.[1] This practical engineering goal takes data science beyond traditional analytics. Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models.[1]



In an effort similar to Dhar's, Stanford professor David Donoho, in September 2015, takes the proposition further by rejecting three simplistic and misleading definitions of data science in lieu of criticisms.[28] First, for Donoho, data science does not equate to big data, in that the size of the data set is not a criterion to distinguish data science and statistics.[28] Second, data science is not defined by the computing skills of sorting big data sets, in that these skills are already generally used for analyses across all disciplines.[28] Third, data science is a heavily applied field where academic programs right now do not sufficiently prepare data scientists for the jobs, in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program.[28][29] As a statistician, Donoho, following many in his field, champions the broadening of learning scope in the form of data science,[28] like John Chambers who urges statisticians to adopt an inclusive concept of learning from data,[30] or like William Cleveland who urges to prioritize extracting from data applicable predictive tools over explanatory theories.[13] Together, these statisticians envision an increasingly inclusive applied field that grows out of traditional statistics and beyond.



For the future of data science, Donoho projects an ever-growing environment for open science where data sets used for academic publications are accessible to all researchers.[28] US National Institute of Health has already announced plans to enhance reproducibility and transparency of research data.[31] Other big journals are likewise following suit.[32][33] This way, the future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms.[28] As Donoho concludes, "the scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.