Test-Driven Data Analysis

Nicholas J. Radcliffe

ISBN 10: 1032897155 / ISBN 13: 9781032897158

Published by Taylor and Francis Ltd, GB, 2026

About this Item

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?".

Seller Inventory # LU-9781032897158

Bibliographic Details

• Title: Test-Driven Data Analysis
• Publisher: Taylor and Francis Ltd, GB
• Publication Date: 2026
• Language: English
• ISBN 10: 1032897155
• ISBN 13: 9781032897158
• Binding: Hardback
• Condition: New
• Weight: 980 grams

About this title

Synopsis

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?”
  

About the Author

Nicholas Radcliffe is the Founder and Director of Stochastic Solutions Limited, a Scottish company specializing in consulting in data science, data analysis, and data engineering. He has also, since 1995, been a Visiting Professor in the Operations Research Group in the School of Mathematics at the University of Edinburgh. He is known for developing forma analysis (sic) of genetic algorithms and uplift modeling, before more recent work on test-driven data analysis.