Test-Driven Data Analysis

di Nicholas J. Radcliffe (Autore)

Taylor & Francis Ltd, 2026

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?”

• Autore: Nicholas J. Radcliffe
• Editore: Taylor & Francis Ltd
• Collana: Chapman & Hall/CRC Data Science Series
• Anno: 2026
• Rilegatura: Paperback / softback
• Pagine: 424 p.
• Dimensioni: 234 x 156 mm
• Peso: 820 gr.
• EAN: 9781032896700

Testo in English