Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (Paperback)

Before placing an order, please note:

  • You'll receive a confirmation email once your order is complete and ready for pickup. 
  • If you have a membership, please make a note of this in the order comments and we'll apply your discount.
  • Online orders are nonrefundable and cannot be exchanged.
  • If you place a pre-order to be shipped in the same order as currently available titles, an additional shipping fee will be added to your order.  
  • Women & Children First is not responsible for lost or stolen packages.
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python By Peter Bruce, Andrew Bruce, Peter Gedeck Cover Image
$79.99
Unavailable

Description


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
Product Details
ISBN: 9781492072942
ISBN-10: 149207294X
Publisher: O'Reilly Media
Publication Date: June 16th, 2020
Pages: 360
Language: English