This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline—machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one.

Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries.

With this book, you will:

Understand how data science creates value

Deliver compelling narratives to sell your data science project

Build a business case using unit economics principles

Create new features for a ML model using storytelling

Learn how to decompose KPIs

Perform growth decompositions to find root causes for changes in a metric

Daniel Vaughan is a leader in AI, ML and data science. He's the author of

Analytical Skills for AI and Data Science

(O'Reilly).

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