icon
Image from Google Jackets
Image from OpenLibrary

Data science: the hard parts : techniques for excelling at data science / Daniel Vaughan.

By: Publisher: Sebastopol, CA : O'Reilly Media, 2023Copyright date: ©2024Edition: First editionDescription: xvi, 237 pages : illustrations, charts ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781098146474
  • 1098146476
Other title:
  • Techniques for excelling at data science
Subject(s): Additional physical formats: Online version:: Data science.DDC classification:
  • 006.312 23 V365
Contents:
Data analytics techniques -- So what? Creating value with data science -- Metrics design -- Growth decompositions: understanding tailwinds and headwinds -- 2x2 designs -- Building business cases -- What's in a lift? -- Narratives -- Datavis: choosing the right plot to deliver a message -- Machine learning -- Simulation and bootstrapping -- Linear regression : going back to basics -- Data leakage -- Productionizing models -- Storytelling in machine learning -- From prediction to decisions -- Incrementality: the holy grail of data science? -- A/B tests -- Large language models and the practice of data science.
Summary: This hands-on guide offers a set of techniques and best practices that are often missed in conventional data engineering and data science education. A common misconception is that great data scientists are experts in the "bit themes" of the discipline, namely ML and programming. But most of the time, these tools can only take us so far. In reality, it's the nuances within these large themes, and the ability to impact the business, that truly distinguish a top-notch data scientist from an average one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and an exceptional 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.
Item type: كتاب
Tags from this library: No tags from this library for this title.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Call number Status Notes Date due Barcode
كتاب كتاب Central Library المكتبة المركزية 006.312 V365 (Browse shelf(Opens below)) Available قاعة الكتب 48055

"November 2023: First Edition"--Title page verso.

Includes index.

Data analytics techniques -- So what? Creating value with data science -- Metrics design -- Growth decompositions: understanding tailwinds and headwinds -- 2x2 designs -- Building business cases -- What's in a lift? -- Narratives -- Datavis: choosing the right plot to deliver a message -- Machine learning -- Simulation and bootstrapping -- Linear regression : going back to basics -- Data leakage -- Productionizing models -- Storytelling in machine learning -- From prediction to decisions -- Incrementality: the holy grail of data science? -- A/B tests -- Large language models and the practice of data science.

This hands-on guide offers a set of techniques and best practices that are often missed in conventional data engineering and data science education. A common misconception is that great data scientists are experts in the "bit themes" of the discipline, namely ML and programming. But most of the time, these tools can only take us so far. In reality, it's the nuances within these large themes, and the ability to impact the business, that truly distinguish a top-notch data scientist from an average one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and an exceptional 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.