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. Authors Peter Bruce and Andrew Bruce show how many data science resources incorporate statistical methods but lack a deeper statistical perspective.

<ASIN:1491952962>

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.

Author: Peter Bruce and Andrew Bruce
Publisher: O'Reilly
Date: June 2017
Pages: 320
ISBN: 978-1491952962
Print: 1491952962
Kindle: B071NVDFD6
Audience: Data Scientists
Level: Intermediate
Category: Data Science

• Learn why exploratory data analysis is a key preliminary step in data science
• Discover how random sampling can reduce bias and yield a higher quality dataset, even with big data
• Learn how the principles of experimental design yield definitive answers to questions
• Find out how to use regression to estimate outcomes and detect anomalies
• Discover key classification techniques for predicting which categories a record belongs to.
• Explore statistical machine learning methods that "learn" from data
• Learn unsupervised learning methods for extracting meaning from unlabeled data

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