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The Data Flow Map : A Practical Guide to Clear and Creative Analytics in Any Data Environment - Nick Ryberg

The Data Flow Map

A Practical Guide to Clear and Creative Analytics in Any Data Environment

By: Nick Ryberg

eText | 1 January 2026

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Unlock the secrets of practical data analysis with the Data Flow Map framework—a game-changing approach that transcends tools and platforms. This book isn't just another programming manual; it's a guide to thinking and communicating about data at a higher level. Whether you're working with spreadsheets, databases, or AI-driven models, you'll learn how to express your analytics in clear, common language that anyone can understand.

In today's data-rich world, clarity is the real challenge. Technical details often obscure insights that could drive real impact. The Data Flow Map framework simplifies complexity into three core motions: source, focus, and build. The first half of the book explores these concepts through illustrations and stories. The second half applies them to real-world datasets using tools like Excel, SQL, and Python, showing how the framework works across platforms and use cases.

A vital resource for analysts at any level, this book offers a practical, tool-agnostic approach to data analysis. With hands-on examples and a universal mental model, you'll gain the confidence to tackle any dataset, align your team, and deliver insights that matter. Whether you're a beginner or a seasoned pro, the Data Flow Map framework will transform how you approach data analytics.

What You Will Learn

  • Grasp essential elements applicable to every data analysis workflow
  • Adapt quickly to any dataset, tool, or platform
  • Master analytic thinking at a higher level
  • Use analytics patterns to better understand the world
  • Break complex analysis into manageable, repeatable steps
  • Iterate faster to uncover deeper insights and better solutions
  • Communicate findings clearly for better decision-making

Who This Book Is For

Aspiring data professionals and experienced analysts, from beginners to seasoned data engineers, focused on data collection, analysis, and decision making

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