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Data Science (MIT Press Essential Knowledge series) by [Kelleher, John D., Tierney, Brendan]
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Data Science (MIT Press Essential Knowledge series) Kindle Edition


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Length: 282 pages Enhanced Typesetting: Enabled Page Flip: Enabled
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Product description

Product Description

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.

About the Author

John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at the Technological University Dublin. He is coauthor of Data Science and Fundamentals of Machine Learning for Predictive Data Analytics.

Product details

  • Format: Kindle Edition
  • File Size: 1208 KB
  • Print Length: 282 pages
  • Publisher: The MIT Press (6 April 2018)
  • Sold by: Amazon Australia Services, Inc.
  • Language: English
  • ASIN: B07C4HKQ9N
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Screen Reader: Supported
  • Enhanced Typesetting: Enabled
  • Average Customer Review: Be the first to review this item
  • Amazon Bestsellers Rank: #204,527 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
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Amazon.com: 4.3 out of 5 stars 15 reviews
In-Ho Keum
4.0 out of 5 starsExcellent intellectual tour to the data science
27 August 2018 - Published on Amazon.com
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6 people found this helpful.
Cai
5.0 out of 5 starsMust-read for anyone who wishes to enter data science
26 January 2019 - Published on Amazon.com
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Yogesh Kumar
4.0 out of 5 starsgood data science primer
6 May 2019 - Published on Amazon.com
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Mark H Mortensen
5.0 out of 5 starsGood overview for the moderately informed techie
30 October 2018 - Published on Amazon.com
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2 people found this helpful.
Abhi
5.0 out of 5 starsPerfect Guide
14 March 2019 - Published on Amazon.com
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