Oreilly.-.Data.Science.The.Hard.Parts.2023.Retail.EPUB.eBook-BitBook
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      Data Science: The Hard Parts: Techniques For Excelling At Data Science    
                               by Daniel VAUghan                                
                               (c) O'Reilly Media                               

 ■▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄   ▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄   ▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄▄■
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   DATE: 26-11-2023      ▓   SIZE: 5.55 MB            ▓   PAGES: n/a           
   FORMAT: EPUB          ▒   DISKS: 1x50.00MB         ▒   EDITION: n/a          
   LANGUAGE: English     ░   ISBN: 9781098146474      ░   REMOVED: DRM

https://www.worldcat.org/search?q=bn%3A9781098146474&fq=x0%3Abook+%2B+x4%3Adigit

 ■▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀■

  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).
 
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