000			03688cam a2200433 i 4500
001			23579326
003			OSt
005			20250830095446.0
008			240225s2024 flua b 001 0 eng
010			_a 2023037553
020			_a9781032346748 _q(hardback)
020			_a9781032350424 _q(paperback)
020			_z9781003324997 _q(ebook)
035			_a23579326
040			_aDLC _beng _erda _cTUPM _dDLC
042			_apcc
050	0	0	_aQA 76.9 _bS54 2024
082	0	0	_a519.50285/5133 _223/eng/20240229
100	1		_aShea, John M. _eauthor.
245	1	0	_aFoundations of data science with Python / _cJohn M. Shea.
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c2024.
300			_a488 pages : _billustrations (some color) ; _c27 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aChapman & Hall/CRC data science series
504			_aIncludes bibliographical references and index.
505	0		_aFirst simulations, visualizations, and statistical tests -- First visualizations and statistical tests with real data -- Introduction to probability -- Null hypothesis tests -- Conditional probability, dependence, and independence -- Introduction to Bayesian methods -- Random variables -- Expected value, parameter estimation, and hypothesis tests on sample means -- Decision making with observations from continuous distributions -- Categorical data, tests for dependence, and goodness of fit for discrete distributions -- Multidimensional data : vector moments and linear regression -- Working with dependent data in multiple dimensions.
520			_a"Foundations of Data Science with Python introduces readers to the fundamentals of data science, including data manipulation and visualization, probability, statistics, and dimensionality reduction. This book is targeted toward engineers and scientists, but it should be readily understandable to anyone who knows basic calculus and the essentials of computer programming. It uses a computational-first approach to data science: the reader will learn how to use Python and the associated data-science libraries to visualize, transform, and model data, as well as how to conduct statistical tests using real data sets. Rather than relying on obscure formulas that only apply to very specific statistical tests, this book teaches readers how to perform statistical tests via resampling; this is a simple and general approach to conducting statistical tests using simulations that draw samples from the data being analyzed. The statistical techniques and tools are explained and demonstrated using a diverse collection of data sets to conduct statistical tests related to contemporary topics, from the effects of socioeconomic factors on the spread of the COVID-19 virus to the impact of state laws on firearms mortality. This book can be used as an undergraduate textbook for an Introduction to Data Science course or to provide a more contemporary approach in courses like Engineering Statistics. However, it is also intended to be accessible to practicing engineers and scientists who need to gain foundational knowledge of data science"-- _cProvided by publisher.
590			_aShea, J. M. (2024). Foundations of data science with Python (1st ed.). CRC Press.
650		0	_aStatistics _xData processing,
650		0	_aProbabilities _xData processing.
650		0	_aInformation visualization.
650		0	_aPython (Computer program language)
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c30685 _d30685