Nicholas J. Radcliffe – författare

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?”

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?”

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: "e;How is this misleading data misleading me?"e;

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: "e;How is this misleading data misleading me?"e;

For developers, content providers and sophisticated power users, Fluidinfo is an online information storage and search platform that supports shared openly writable metadata of any type and about anything. Fluidinfo helps content owners publish product information via a modern writable API, with flexible permissions and their domain name on their data. Developers can create lightweight applications that make data social while letting users personalize and search on anything.

Imagine a public storage system that has a place online for structured data about everything that exists—or that could exist. This book introduces Fluidinfo, a system that enables you to store information about anything, real or imaginary, in any digital form. You’ll learn how to organize and search for data, and decide who can use, modify, and extend what you’ve contributed.

This guide demonstrates Fluidinfo’s potential to create social data, with facilities that encourage users and applications to share, remix, and reuse data in ways they may not have anticipated. You’ll learn how to use tools for reading and writing data, and how to use Fluidinfo in your own applications by working with its writable API and simple query language.

Read and write Fluidinfo data from web applications—and reuse and build upon each other’s dataDiscover Fluidinfo’s permissions system for tags and namespacesLearn how to use Fish, the command-line tool for interacting with Fluidinfo dataDelve into Fluidinfo’s RESTful API, and learn how to make HTTP requestsUse Fluidinfo client libraries to build a simple Python utility or a JavaScript web application