000			03838cam a2200601 i 4500
001			9781351132916
003			FlBoTFG
005			20240213122828.0
006			m o d
007			cr cnu|||unuuu
008			230509s2023 flu ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9781351132916 _q(electronic bk.)
020			_a1351132911 _q(electronic bk.)
020			_z9780815354390
020			_a9781351132909 _q(electronic bk. : PDF)
020			_a1351132903 _q(electronic bk. : PDF)
020			_a9781351132893 _q(electronic bk. : EPUB)
020			_a135113289X _q(electronic bk. : EPUB)
020			_a9781351132886 _q(electronic bk. : Mobipocket)
020			_a1351132881 _q(electronic bk. : Mobipocket)
020			_z9780815354475
024	7		_a10.1201/9781351132916 _2doi
035			_a(OCoLC)1378643708
035			_a(OCoLC-P)1378643708
050		4	_aQA76.9.B45
072		7	_aBUS _x061000 _2bisacsh
072		7	_aCOM _x021030 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
072		7	_aUN _2bicssc
082	0	4	_a005.7 _223/eng/20230517
100	1		_aLin, Hui _c(Quantitative researcher), _eauthor.
245	1	0	_aPractitioner's guide to data science / _cHui Lin and Ming Li.
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press, _c2023.
300			_a1 online resource
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	0		_aChapman & Hall/CRC Data Science Series
505	0		_aSoft skills for data scientists -- Introduction to the data -- Big data cloud platform -- Data pre-processing -- Data wrangling -- Model tuning strategy -- Measuring performance -- Regression models -- Regularization methods -- Tree-based methods -- Deep learning.
520			_a"This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes"-- _cProvided by publisher.
588			_aOCLC-licensed vendor bibliographic record.
650		7	_aBUSINESS & ECONOMICS / Statistics _2bisacsh
650		7	_aCOMPUTERS / Database Management / Data Mining _2bisacsh
650		7	_aMATHEMATICS / Probability & Statistics / General _2bisacsh
650		0	_aBig data.
650		0	_aData mining.
650		0	_aDatabase management.
700	1		_aLi, Ming _c(Research science manager), _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781351132916
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c5378 _d5378