Test-Driven Data Analysis (Chapman & Hall/CRC Data Science Series)

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. • Addr