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DATA SCIENCE AND ITS RELATIONSHIP TO BIG DATA AND DATA-DRIVEN DECISION MAKING

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Companies have realized they need to hire data scientists, academic institutions are scrambling to put together
data-science programs, and publications are touting data science as a hot—even ‘‘sexy’’—career choice. However,
there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the
concept diffuses into meaningless buzz

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ABSTRACT
Companies have realized they need to hire data scientists, academic institutions are scrambling to put together
data-science programs, and publications are touting data science as a hot—even ‘‘sexy’’—career choice. However,
there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the
concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to
pin down exactly what is data science. One reason is that data science is intricately intertwined with other
important concepts also of growing importance, such as big data and data-driven decision making. Another reason
is the natural tendency to associate what a practitioner does with the definition of the practitioner’s field; this can
result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science
precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in
order for data science to serve business effectively, it is important (i) to understand its relationships to other
important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once
we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore,
only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective
that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles
underlying data science

INTRODUCTION
With vast amounts of data now available, companies in almost every industry are focused on exploiting data for
competitive advantage. The volume and variety of data have far outstripped the capacity of manual analysis, and in some cases have exceeded the capacity of conventional databases. At the same time, computers have become far more powerful, networking is ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible. The convergence of these phenomena has given rise to the increasingly widespread business application of data science

Year : 2013

By : Foster Provost and Tom Fawcett

File Information : English Language /9 Page /Size : 210 K

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سال : 2013

کاری از :  Foster Provost and Tom Fawcett

اطلاعات فایل : زبان انگلیسی / 9 صفحه / حجم : 210 K

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