Practical ststistics for data scientist (Record no. 7616)

MARC details
000 -LEADER
fixed length control field nam a22 7a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20181029165345.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 181029b xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789352135653
040 ## - CATALOGING SOURCE
Transcribing agency AIKTC-KRRC
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title ENG
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Edition number DDC23
Classification number 001.422
Item number BRU/BRU
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 4953
Personal name Bruce, Peter
245 ## - TITLE STATEMENT
Title Practical ststistics for data scientist
Remainder of title : 50 essentials concepts
250 ## - EDITION STATEMENT
Edition statement 1st
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Navi Mumbai
Name of publisher, distributor, etc.
Date of publication, distribution, etc. 2017
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 298p.
Other physical details | Binding - Paperback |
Dimensions 23.5*18 cm
520 ## - SUMMARY, ETC.
Summary, etc. Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.<br/><br/>Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
Expansion of summary note <br/>With this book, you’ll learn:<br/><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 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
9 (RLIN) 4619
Topical term or geographic name entry element EXTC Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 4955
Personal name Bruce, Andrew
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Date last checked out Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Reference School of Engineering & Technology School of Engineering & Technology Reference Section 30/10/2018 2 740.00 6 001.422 BRU/BRU E14535 23/06/2025 21/06/2024 925.00 30/10/2018 Books
    Dewey Decimal Classification     Circulation School of Engineering & Technology School of Engineering & Technology General Stacks 02/11/2019 2 740.00 5 001.422 BRU/BRU E15036 17/09/2025 03/09/2025 925.00 02/11/2019 Books
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