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The goal of data science is to improve decision making through the
analysis of data. Today data science determines the ads we see online, the
books and movies that are recommended to us online, which emails are filtered
into our spam folders, and even how much we pay for health insurance. This
volume in the MIT Press Essential Knowledge series offers a concise
introduction to the emerging field of data science, explaining its evolution,
current uses, data infrastructure issues, and ethical challenges.
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It has never been easier for organizations to gather, store, and
process data. Use of data science is driven by the rise of big data and social
media, the development of high-performance computing, and the emergence of such
powerful methods for data analysis and modelling as deep learning. Data science
encompasses a set of principles, problem definitions, algorithms, and processes
for extracting non-obvious and useful patterns from large datasets. It is
closely related to the fields of data mining and machine learning, but broader
in scope. This book offers a brief history of the field, introduces fundamental
data concepts, and describes the stages in a data science project. It considers
data infrastructure and the challenges posed by integrating data from multiple
sources, introduces the basics of machine learning, and discusses how to link
machine learning expertise with real-world problems. The book also reviews
ethical and legal issues, developments in data regulation, and computational
approaches to preserving privacy. Finally, it considers the future impact of
data science and offers principles for success in data science projects.
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