Data Science: The Hard Parts: Techniques for Excelling at Data Science


Data Science: The Hard Parts: Techniques for Excelling at Data Science
Author: Daniel Vaughan (Author)
Publisher finelybook 出版社:‏ O’Reilly Media
Edition 版本:‏ 1st
Publication Date 出版日期:‏ 2023-12-05
Language 语言: English
Print Length 页数: 254 pages
ISBN-10: 1098146476
ISBN-13: 9781098146474

Book Description

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 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.

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