Book Name: Practical Statistics for Data Scientists: 50 Essential Concepts 1st Edition
Author: Peter Bruce, Andrew Bruce
Publisher: O’Reilly Media; 1 edition
Pages: 318 pages
File size: 13 MB
File format: PDF
Practical Statistics for Data Scientists: 50 Essential Concepts 1st Edition Pdf Book Description:
This publication is geared toward the information scientist having some familiarity with all the programming language, also with a few prior (perhaps jagged or transient ) exposure to data. This may vastly improve the efficacy of particular SQLqueries. In Python, together with all the pandas library, the fundamental rectangular data structure is a DataFrame item. By default, an automated integer index is made for a DataFrame dependent on the arrangement of the rows. In pandas, it’s also possible to place multilevel/hierarchical indicators to enhance the efficacy of particular operations. In R, the most simple fundamental data structure is a data.frame item. A data.frame also comes with an implicit integer indicator depending on the row sequence.
Even though a custom key can be produced via the row.names feature, the native R data.frame doesn’t encourage user-specified or multilevel indicators. To overcome this lack, two new bundles are gaining widespread usage: data.table and dplyr. Both encourage multilevel indexes and extend substantial speedups in working using a data.frame. Statistical methods are an integral part of information science, yet hardly any information scientists possess some formal data training. Courses and publications on fundamental data seldom cover the subject from an data science standpoint. This practical guide describes how to use various statistical procedures to information science, tells you how you can prevent their abuse, and gives you information on what is important and what is not.Many data science tools incorporate statistical techniques but lack a deeper statistical outlook.
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