BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Practical Statistics for Data Scientists: 50 Essential Concepts

Practical Statistics for Data Scientists: 50 Essential Concepts

Practical Statistics for Data Scientists: 50 Essential Concepts
Stock photo: cover may vary

Practical Statistics for Data Scientists: 50 Essential Concepts Paperback - 2017

by Bruce, Andrew

Add to wish list
  • Used
  • Paperback
  • first
Used: Good

Description

O'Reilly, 2017-06-27. 1. paperback. Used: Good. 6.75x0.50x9.00. Buy with confidence. Excellent Customer Service & Return policy.
Ask the seller a question Add to wish list
A$28.76
A$21.78 Delivery within USA
Standard delivery: 12 to 14 days
More delivery options
Dropship order
Ships from Ergodebooks (Texas, United States)

Details

About Ergodebooks Texas, United States

Biblio member since 2005

Our goal is to provide best customer service and good condition books for the lowest possible price. We are always honest about condition of book. We list book only by ISBN # and hence exact book is guaranteed.

Terms of Sale:

We have 30 day return policy.

Browse books from Ergodebooks

Reader reviews for Practical Statistics for Data Scientists: 50 Essential Concepts

From the publisher

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

About the author

Peter Bruce founded and grew the Institute for Statistics Education at Statistics.com, which now offers about 100 courses in statistics, roughly a third of which are aimed at the data scientist. In recruiting top authors as instructors and forging a marketing strategy to reach professional data scientists, Peter has developed both a broad view of the target market, and his own expertise to reach it.

Andrew Bruce has over 30 years of experience in statistics and data science in academia, government and business. He has a Ph.D. in statistics from the University of Washington and published numerous papers in refereed journals. He has developed statistical-based solutions to a wide range of problems faced by a variety of industries, from established financial firms to internet startups, and offers a deep understanding the practice of data science.

tracking-