Description

Book Synopsis
Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.

Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods.
 
The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by mak

HumanCentered Data Science An Introduction

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    £33.00

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    Order before 4pm today for delivery by Thu 9 Jul 2026.

    A Paperback by Cecilia Aragon, Shion Guha

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      Publisher: MIT Press
      Publication Date: 3/1/2022 12:00:00 AM
      ISBN13: 9780262543217, 978-0262543217
      ISBN10: 0262543214
      Also in:
      Algorithms and data structures

      Description

      Book Synopsis
      Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets.

      Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods.
       
      The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by mak

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