NLU Meghalaya Library

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How to think about data science / Diego Miranda-Saavedra.

By: Material type: TextSeries: Chapman & Hall/CRC data science seriesPublisher: Boca Raton, FL : CRC Press, 2023Description: 1 online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781003340850
  • 1003340857
  • 9781000885729
  • 1000885720
  • 9781000885675
  • 1000885674
Subject(s): DDC classification:
  • 001.4/22 23/eng/20230125
  • 005.7 23
LOC classification:
  • QA276.12 .M57 2023
Online resources: Summary: This book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.
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This book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.

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