Test-Driven Data Analysis
Language: English
Published by CRC Press Mai 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Neuware - 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 # 9781032896700
- Title
- Test-Driven Data Analysis
- Author
- Nicholas J Radcliffe
- Publisher
- CRC Press Mai 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1032896701
- ISBN 13
- 9781032896700
- Item weight
- 621 grams
- Dimensions
- 234x156x23 mm
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?”
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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