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Practical Statistics for Data Scientists - 2nd Edition by  Peter Bruce & Andrew Bruce & Peter Gedeck (Paperback) - 1 of 1

Practical Statistics for Data Scientists - 2nd Edition by Peter Bruce & Andrew Bruce & Peter Gedeck (Paperback)

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Highlights

  • Statistical methods are a key part of data science, yet few data scientists have formal statistical training.
  • About the Author: Peter Bruce is the Founder and Chief Academic Officer of the Institute for Statistics Education at Statistics.com, which offers about 80 courses in statistics and analytics, roughly half of which are aimed at data scientists.
  • 360 Pages
  • Computers + Internet,

Description



About the Book



Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.



Book Synopsis



Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.

Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.

With this book, you'll learn:

  • Why exploratory data analysis is a key preliminary step in data science
  • How random sampling can reduce bias and yield a higher-quality dataset, even with big data
  • How the principles of experimental design yield definitive answers to questions
  • How to use regression to estimate outcomes and detect anomalies
  • Key classification techniques for predicting which categories a record belongs to
  • Statistical machine learning methods that "learn" from data
  • Unsupervised learning methods for extracting meaning from unlabeled data



About the Author



Peter Bruce is the Founder and Chief Academic Officer of the Institute for Statistics Education at Statistics.com, which offers about 80 courses in statistics and analytics, roughly half of which are aimed at data scientists. He has authored or co-authored several books in statistics and analytics, and he earned his Bachelor's degree at Princeton, and Masters degrees at Harvard and the University of Maryland.

Andrew Bruce, Principal Research Scientist at Amazon, has over 30 years of experience in statistics and data science in academia, government and business. The co-author of Applied Wavelet Analysis with S-PLUS, he earned his bachelor's degree at Princeton, and PhD in statistics at the University of Washington

Peter Gedeck, Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Co-author of Data Mining for Business Analytics, he earned PhD's in Chemistry from the University of Erlangen-Nürnberg in Germany and Mathematics from Fernuniversität Hagen, Germany

Dimensions (Overall): 9.1 Inches (H) x 7.0 Inches (W) x .9 Inches (D)
Weight: 1.3 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 360
Genre: Computers + Internet
Publisher: O'Reilly Media
Format: Paperback
Author: Peter Bruce & Andrew Bruce & Peter Gedeck
Language: English
Street Date: June 16, 2020
TCIN: 83300395
UPC: 9781492072942
Item Number (DPCI): 247-56-8078
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 0.9 inches length x 7 inches width x 9.1 inches height
Estimated ship weight: 1.3 pounds
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Q: What statistical topics does the book address?

submitted by AI Shopping Assistant - 1 day ago
  • A: It covers exploratory data analysis, regression, classification, statistical machine learning, and unsupervised learning among others.

    submitted byAI Shopping Assistant - 1 day ago
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Q: How does this book help avoid misuse of statistical methods?

submitted by AI Shopping Assistant - 1 day ago
  • A: It provides practical guidance on applying statistical methods correctly and highlights important concepts to focus on.

    submitted byAI Shopping Assistant - 1 day ago
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Q: Who are the authors of this book?

submitted by AI Shopping Assistant - 1 day ago
  • A: The book is authored by Peter Bruce, Andrew Bruce, and Peter Gedeck, each with extensive backgrounds in statistics and data science.

    submitted byAI Shopping Assistant - 1 day ago
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Q: What programming languages are covered in this book?

submitted by AI Shopping Assistant - 1 day ago
  • A: The book includes examples in both Python and R, catering to data scientists familiar with these languages.

    submitted byAI Shopping Assistant - 1 day ago
    Ai generated

Q: What is the target audience for this book?

submitted by AI Shopping Assistant - 1 day ago
  • A: The book is aimed at data scientists with some exposure to statistics, particularly those familiar with R or Python.

    submitted byAI Shopping Assistant - 1 day ago
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