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Test-Driven Data Analysis

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Paperback / softback
424 Pages
18 May 2026
RRP: $132.99
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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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    Product Details
    ISBN-13
    9781032896700
    Author
    Nicholas J. Radcliffe
    Publisher
    CRC Press
    Published
    18 May 2026
    Format
    Paperback / softback
    Pages
    424

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

    RRP: $132.99
    $131.00
    Ships in 2-5 business days

    Delivery estimate

    Hurry up! Current stock:

    Test-Driven Data Analysis

    RRP: $132.99
    $131.00

    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 Title In

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