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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).

Thể loại:Computers - Organization and Data Processing

Năm:2023

In lần thứ:1

Nhà xuát bản:O'Reilly Media

Ngôn ngữ:english

Trang:257