Price: $79.99 - $48.99
(as of Sep 27, 2024 05:01:09 UTC – Details)
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 scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that “learn” from dataUnsupervised learning methods for extracting meaning from unlabeled data.
From the brand
Explore further ‘R’ resources
Sharing the knowledge of experts
O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 2nd edition (June 16, 2020)
Language : English
Paperback : 360 pages
ISBN-10 : 149207294X
ISBN-13 : 978-1492072942
Item Weight : 1.35 pounds
Dimensions : 7 x 0.9 x 9.1 inches
Customers say
Customers find the content amazing, excellent, and comprehensive. Opinions are mixed on the explanation quality, with some finding it easy to read and sound, while others say it’s poor at breaking down concepts in depth.
AI-generated from the text of customer reviews
