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Data science / John D. Kelleher and Brendan Tierney.

By: Contributor(s): Material type: TextTextLanguage: English Series: The MIT Press essential knowledge series | The MIT Press essential knowledge seriesPublisher: Cambridge, Massachusetts : The MIT Press, [2018]Description: xi, 264 pages : illustrations ; 18 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780262535434 (pbk. : alk. paper)
  • 0262535432 (pbk. : alk. paper)
Subject(s): DDC classification:
  • 005.7
LOC classification:
  • QA 76.9  K29d 2018
Contents:
What is data science? -- What are data, and what is a dataset? -- A data science ecosystem -- Machine learning 101 -- Standard data science tasks -- Privacy and ethics -- Future trends and principles of success
Summary: A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. 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. 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 modeling 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 Provided by publisher
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Holdings
Item type Current library Home library Collection Shelving location Call number Copy number Status Date due Barcode
Libro Libro Biblioteca Juan Bosch Biblioteca Juan Bosch Humanidades Humanidades (4to. Piso) QA 76.9 K29d 2018 (Browse shelf(Opens below)) 1 Available 00000149965

Includes bibliographical references and index.

What is data science? -- What are data, and what is a dataset? -- A data science ecosystem -- Machine learning 101 -- Standard data science tasks -- Privacy and ethics -- Future trends and principles of success

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. 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. 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 modeling 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 Provided by publisher

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