Practical Statistics for Data Scientists : (Record no. 7456)

MARC details
000 -LEADER
fixed length control field 02017 a2200229 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251011132756.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9788194435006
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 001.4
Item number BruP
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Bruce, Peter
245 ## - TITLE STATEMENT
Title Practical Statistics for Data Scientists :
Remainder of title 50+ essential concepts using R and Python /
Statement of responsibility, etc Peter Bruce , Andrew Bruce and Peter Gedeck
250 ## - EDITION STATEMENT
Edition statement 2nd ed.
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher SPD :
Place of publication Navi Mumbai ,
Year of publication ©2020.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xvi, 342p.
520 ## - SUMMARY, ETC.
Summary, etc 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 formate<br/>With this book, you’ll learn:<br/>Why exploratory data analysis is a key preliminary step in data science<br/>How random sampling can reduce bias and yield a higher-quality dataset, even with big data<br/> How the principles of experimental design yield definitive answers to questions<br/> How to use regression to estimate outcomes and detect anomalies<br/> Key classification techniques for predicting which categories a record belongs to<br/> Statistical machine learning methods that "learn" from data<br/> Unsupervised learning methods for extracting meaning from unlabeled data
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Statistics--Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Python (Computer program language)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term R (Computer program language)
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Gedeck, Peter
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Bruce, Andrew
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Collection code Home library Current library Shelving location Date acquired Source of acquisition Purchase Price Bill number Full call number Accession Number Print Price Bill Date/Price effective from Koha item type
      Mathematics Indian Institute of Technology Tirupati Indian Institute of Technology Tirupati General Stacks 01/01/2025 Shah Book House 1032.50 SBH/28599 001.4 BruP (12189) 12189 1475.00 07/10/2025 Books