»» Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition

# Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition

Pdf Book Name: Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition
Author: Peter Bruce, Andrew Bruce, Peter Gedeck
Publisher: O’Reilly Media
ISBN-10, 13:
Year:
Pages:
Language: English
File size: 1 MB
File format: PDF,EPUB

## Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python 2nd Edition Pdf Book Description:

This book is aimed at the data scientist with some familiarity with the R and/or Python programming languages, and with some prior (perhaps spotty or ephemeral) exposure to statistics. Two of the authors came to the world of data science from the world of statistics, and have some appreciation of the contribution that statistics can make to the art of data science. At the same time, we are well aware of the limitations of traditional statistics instruction: statistics as a discipline is a century and a half old, and most statistics textbooks and courses are laden with the momentum and inertia of an ocean liner. All the methods in this book have some connection historical or methodological to the discipline of statistics. Methods that evolved mainly out of computer science, such as neural nets, are not included. In all cases, this book gives code examples first in R and then in Python. In order to avoid unnecessary repetition, we generally show only output and plots created by the R code. We also skip the code required to load the required packages and data sets. You can find the complete code as well as the data sets for download at https:// github.com/gedeck/practical-statistics-for-data-scientists.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require per mission. Traditional database tables have one or more columns designated as an index, essentially a row number. This can vastly improve the efficiency of certain database queries. In Python, with the pandas library, the basic rectangular data structure is a DataFrame object. By default, an automatic integer index is created for a DataFrame based on the order of the rows. In pandas, it is also possible to set multilevel/hierarchical indexes to improve the efficiency of certain operations. In R, the basic rectangular data structure is a data.frame object. A data.frame also has an implicit integer index based on the row order. The native R data.frame does not support user-specified or multilevel indexes, though a custom key can be created through the row.names attribute. To overcome this deficiency, two new packages are gaining widespread use: data.table and dplyr. Both support multilevel indexes and offer significant speedups in working with a data.frame.