9781491952962-1491952962-Practical Statistics for Data Scientists: 50 Essential Concepts

Practical Statistics for Data Scientists: 50 Essential Concepts

ISBN-13: 9781491952962
ISBN-10: 1491952962
Edition: 1
Author: Peter Bruce, Andrew Bruce
Publication date: 2017
Publisher: O'Reilly Media
Format: Paperback 318 pages
Category: Statistics
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Book details

ISBN-13: 9781491952962
ISBN-10: 1491952962
Edition: 1
Author: Peter Bruce, Andrew Bruce
Publication date: 2017
Publisher: O'Reilly Media
Format: Paperback 318 pages
Category: Statistics

Summary

Acknowledged authors Peter Bruce , Andrew Bruce wrote Practical Statistics for Data Scientists: 50 Essential Concepts comprising 318 pages back in 2017. Textbook and eTextbook are published under ISBN 1491952962 and 9781491952962. Since then Practical Statistics for Data Scientists: 50 Essential Concepts textbook was available to sell back to BooksRun online for the top buyback price of $ 19.71 or rent at the marketplace.

Description

Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide 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 science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, 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
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