000			03782dam a2200325Ii 4500
001			0000372243
003			0001
008			221213s2020 sz# ob 001 0 eng d
020			_a9783030575915 (paperback) _q(electronic bk.)
020			_a3030575926 _q(electronic bk.)
020			_z3030575918
020			_z9783030575915
035			_a(OCoLC)1205607630 _z(OCoLC)1225563876 _z(OCoLC)1227334462
040			_aYDX _beng _erda _cYDX _dSFB _dUAB _dOCLCF _dEBLCP _dGW5XE _dOCLCO
082	0	4	_a005.74 _223
084			_a005.74 _bBAD-S _223
100	1		_aBadia, Antonio, _0http://id.loc.gov/authorities/names/nb2009013244 _eauthor
245	1	0	_aSQL for data science _hBook : _bdata cleaning, wrangling and analytics with relational databases / _cAntonio Badia.
300			_axi, 285 pages : _c23 cm.
365			_a01 _b11,347.18
440		0	_aData-centric systems and applications. _0http://id.loc.gov/authorities/names/no2003128521 _x2197-9723
490	1		_aData-centric systems and applications, _x2197-9723
500			_aIncludes references and index
520			_aThis 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, but no 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
521			_aAll.
650		0	_aDatabase management. _0http://id.loc.gov/authorities/subjects/sh85035848
650		0	_aBig data. _0http://id.loc.gov/authorities/subjects/sh2012003227
650		0	_aSQL (Computer program language) _0http://id.loc.gov/authorities/subjects/sh86006628
852			_p10001000062606 _911347.18 _vAllied Book Company _dBooks
999			_c64119 _d64119