000			03419cam a22005537i 4500
001			23168451
003			OSt
005			20250909142149.0
008			230602s2022 cc 001 0 eng d
010			_a 2023276388
015			_aGBC290257 _2bnb
016	7		_a020621576 _2Uk
020			_a9789355422743 _qpaperback
020			_qpaperback
035			_a23168451
035			_a(OCoLC)1328015167
040			_aUKMGB _beng _erda _cUKMGB _dFIE _dOCLCF _dUAP _dNVC _dJRZ _dOCL _dVNVGU _dDLC
042			_alccopycat
050	0	0	_aQA76.9.D343 _bN54 2022
082	0	4	_a006.31 NIE _223
092			_20
100	1		_aNield, Thomas _c(Computer programmer), _eauthor.
245	1	0	_aEssential math for data science [electronic resources] : _btake control of your data with fundamental linear algebra, probability, and statistics / _cThomas Nield.
250			_aFirst edition.
264		1	_aBeijing ; _aBoston : _bO'Reilly, _c2022.
300			_a1 digital resource (xiv, 332 pages) ; _c24 cm
336			_atext _btxt _2rdacontent
336			_astill image _bsti _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aIncludes index.
505	0		_aBasic math and calculus review -- Probability -- Descriptive and inferential statistics -- Linear algebra -- Linear regression -- Logistic regression and classification -- Neural networks -- Career advice and the path forward.
520			_aTo succeed in data science you need some math proficiency. But not just any math. This common-sense guide provides a clear, plain English survey of the math you'll need in data science, including probability, statistics, hypothesis testing, linear algebra, machine learning, and calculus. Practical examples with Python code will help you see how the math applies to the work you'll be doing, providing a clear understanding of how concepts work under the hood while connecting them to applications like machine learning. You'll get a solid foundation in the math essential for data science, but more importantly, you'll be able to use it to: Recognize the nuances and pitfalls of probability math Master statistics and hypothesis testing (and avoid common pitfalls) Discover practical applications of probability, statistics, calculus, and machine learning Intuitively understand linear algebra as a transformation of space, not just grids of numbers being multiplied and added Perform calculus derivatives and integrals completely from scratch in Python Apply what you've learned to machine learning, including linear regression, logistic regression, and neural networks -- _cProvided by publisher.
650		0	_aData mining _xMathematics.
650		0	_aMachine learning _xMathematics.
650		0	_aMathematical statistics.
650		0	_aProbabilities.
650		0	_aComputer science _xMathematics.
650		7	_aComputer science _xMathematics. _2fast _0(OCoLC)fst00872460
650		7	_aData mining _xMathematics. _2fast _0(OCoLC)fst02013374
650		7	_aMathematical statistics. _2fast _0(OCoLC)fst01012127
650		7	_aProbabilities. _2fast _0(OCoLC)fst01077737
655		7	_aHandbooks and manuals. _2fast _0(OCoLC)fst01423877
655		7	_aHandbooks and manuals. _2lcgft
887			_2CamTech Library
906			_a7 _bcbc _ccopycat _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK _n0
999			_c2415 _d2415