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Test-Driven Data Analysis

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Hardback
18 May 2026
424 Pages
RRP: $362.00
$357.00
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Test-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical pipelines and processes. It can be thought of as data analysis as if the answers actually matter.

Test-driven data analysis can be thought of as a sibling to reproducible research, with similar concerns, but greater emphasis on automated testing, and less requirement for a human to reproduce results. Extensive checklists are provided that can be used to improve quality before,during, and after analysis.

Key Features:

  • Prevents costly errors in analytical processes before they reach production through automated data validation and reference testing of data pipelines.
    • Provides actionable checklists for issues beyond the reach of automated testing.
    • Equips readers with open-source Python tools and language-agnostic command-line interfaces.
    • Addresses testing challenges for modern LLM-based systems including chat-bots and coding assistants.
    • Instills in analysts an inner voice that is always asking: “How is this misleading data misleading me?”

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    Book Details
    ISBN-13
    9781032897158
    Author
    Nicholas J. Radcliffe
    Publisher
    CRC Press
    Published
    18 May 2026
    Format
    Hardback
    Pages
    424

    This product hasn't received any reviews yet. Be the first to review this product!

    RRP: $362.00
    $357.00
    Hurry up! Current stock:

    Test-Driven Data Analysis

    RRP: $362.00
    $357.00

    Description

    Test-driven data analysis is the synthesis of ideas from test-driven development of software to data-intensive work including data science, data analysis, and data engineering. It is a methodology for improving the quality of data and of analytical pipelines and processes. It can be thought of as data analysis as if the answers actually matter.

    Test-driven data analysis can be thought of as a sibling to reproducible research, with similar concerns, but greater emphasis on automated testing, and less requirement for a human to reproduce results. Extensive checklists are provided that can be used to improve quality before,during, and after analysis.

    Key Features:

    • Prevents costly errors in analytical processes before they reach production through automated data validation and reference testing of data pipelines.
      • Provides actionable checklists for issues beyond the reach of automated testing.
      • Equips readers with open-source Python tools and language-agnostic command-line interfaces.
      • Addresses testing challenges for modern LLM-based systems including chat-bots and coding assistants.
      • Instills in analysts an inner voice that is always asking: “How is this misleading data misleading me?”

    You Can Find This Book In

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