SQL for Data Science (Record no. 174517)

MARC details
000 -LEADER
fixed length control field 04229nam a22005055i 4500
001 - CONTROL NUMBER
control field 978-3-030-57592-2
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125126.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030575922
-- 978-3-030-57592-2
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-030-57592-2
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D3
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM021000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UN
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Badia, Antonio.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title SQL for Data Science
Medium [electronic resource] :
Remainder of title Data Cleaning, Wrangling and Analytics with Relational Databases /
Statement of responsibility, etc by Antonio Badia.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2020.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2020.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 285 p. 16 illus.
Other physical details online resource.
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-- online resource
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490 1# - SERIES STATEMENT
Series statement Data-Centric Systems and Applications,
International Standard Serial Number 2197-974X
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1. The Data Life Cycle -- 2. Relational Data -- 3. Data Cleaning and Pre-processing -- 4. Introduction to Data Analysis -- 5. More SQL -- 6. Databases and Other Tools.
520 ## - SUMMARY, ETC.
Summary, etc This textbook explains SQL within the context of data science and introduces the different parts of SQL as they are needed for the tasks usually carried out during data analysis. Using the framework of the data life cycle, it focuses on the steps that are very often given the short shift in traditional textbooks, like data loading, cleaning and pre-processing. The book is organized as follows. Chapter 1 describes the data life cycle, i.e. the sequence of stages from data acquisition to archiving, that data goes through as it is prepared and then actually analyzed, together with the different activities that take place at each stage. Chapter 2 gets into databases proper, explaining how relational databases organize data. Non-traditional data, like XML and text, are also covered. Chapter 3 introduces SQL queries, but unlike traditional textbooks, queries and their parts are described around typical data analysis tasks like data exploration, cleaning and transformation.Chapter 4 introduces some basic techniques for data analysis and shows how SQL can be used for some simple analyses without too much complication. Chapter 5 introduces additional SQL constructs that are important in a variety of situations and thus completes the coverage of SQL queries. Lastly, chapter 6 briefly explains how to use SQL from within R and from within Python programs. It focuses on how these languages can interact with a database, and how what has been learned about SQL can be leveraged to make life easier when using R or Python. All chapters contain a lot of examples and exercises on the way, and readers are encouraged to install the two open-source database systems (MySQL and Postgres) that are used throughout the book in order to practice and work on the exercises, because simply reading the book is much less useful than actually using it. This book is for anyone interested in data science and/or databases. It just demands a bit of computer fluency, butno specific background on databases or data analysis. All concepts are introduced intuitively and with a minimum of specialized jargon. After going through this book, readers should be able to profitably learn more about data mining, machine learning, and database management from more advanced textbooks and courses.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Database management.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Quantitative research.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Database Management.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Analysis and Big Data.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030575915
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030575939
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Data-Centric Systems and Applications,
-- 2197-974X
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-030-57592-2">https://doi.org/10.1007/978-3-030-57592-2</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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