Practical Statistics for Data Scientists

Practical Statistics for Data Scientists 50+ Essential Concepts Using R and Python

Second edition

Paperback (24 Jun 2020)

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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

Book information

ISBN: 9781492072942
Publisher: O'Reilly Media
Imprint: O'Reilly
Pub date:
Edition: Second edition
DEWEY: 001.422
DEWEY edition: 23
Language: English
Number of pages: 368
Weight: 620g
Height: 178mm
Width: 233mm
Spine width: 21mm