Data Science: The Hard Parts

Data Science: The Hard PartsThis 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

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 AuthorDaniel Vaughan is currently the Head of Data at Clip, the leading paytech company in Mexico. He is the author of Analytical Skills for AI and Data Science (O'Reilly, 2020). With more than 15 years of experience developing machine learning and more than eight years leading data science teams, he is passionate about finding ways to create value through data and data science and in developing young talent. He holds a PhD in economics from NYU (2011). In his free time he enjoys running, walking his dogs around Mexico City, reading, and playing music.