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?"