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?”
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?”
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...
This book constitutes the thoroughly refereed proceedings of the Third International Symposium on Data-Driven Process Discovery and Analysis held in Riva del Garda, Italy, in August 2013.The six...
Take a deep dive into building data-driven test frameworks using Selenium WebDriverKey Features:A comprehensive guide to designing data-driven test frameworks using the Selenium 3 WebDriver API,...
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