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 head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

About the Author

Daniel Vaughan is a leader in the data science, AI and ML spheres, and has written extensively about these topics. He is the author of two books, both published by O'Reilly Media: "Analytical Skills for AI and Data Science" (2020), and "Data Science: The Hard Parts" (2023). . He is passionate about making better decisions with data and evidence.He holds a PhD in economics from NYU (2011). In his free time he enjoys running, meditating, walking his dogs, reading, and playing music.

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