A Practitioner's Guide to Data Science
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Describing Data Science is like trying to describe the silence of the forest- it should be easy, but somehow capturing the words is impossible. Data Science is the art of turning data into actions. This is accomplished through the creation of data products, which provide actionable information without exposing decision makers to the underlying data or analytics (buy/sell strategies for financial instruments, a set of actions to improve product yield, or steps to improve product marketing). Data Science supports and encourages shifting between deductive (hypothesis-based) and inductive (pattern...
Describing Data Science is like trying to describe the silence of the forest- it should be easy, but somehow capturing the words is impossible. Data Science is the art of turning data into actions. This is accomplished through the creation of data products, which provide actionable information without exposing decision makers to the underlying data or analytics (buy/sell strategies for financial instruments, a set of actions to improve product yield, or steps to improve product marketing). Data Science supports and encourages shifting between deductive (hypothesis-based) and inductive (pattern-based) reasoning. This is a fundamental change from traditional analytic approaches. Data Science creates an environment where models of reality no longer need to be static and empirically based. Instead, they are constantly tested, updated and improved until better models are found. While there are countless industry and academic publications describing what Data Science is and why we should care, little information is available to explain how to make use of data as a resource. This book has tried to explain it a little.
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