000			03344cam a22004098i 4500
001			21897128
003			OSt
005			20220405162043.0
008			210129s2021 flu 001 0 eng
010			_a 2021001775
020			_a9780367609504 _q(hardback)
020			_z9781003102656 _q(ebook)
040			_aBLR _beng _erda _cDLC _dIIHS
042			_apcc
082	0	0	_a001.42 HUA _223 _b017995
100	1		_aHuang, Shuai _c(Industrial engineer), _eauthor.
245	1	0	_aData analytics : _ba small data approach / _cShuai Huang & Houtao Deng.
250			_aFirst edition.
263			_a2105
264		1	_aBoca Raton : _bCRC Press, _c2021.
300			_axiv, 257 pages : _billustrations (some color) ; _c28 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aIncludes index.
505	0		_aAbstraction -- Recognition -- Resonance -- Learning (I) -- Diagnosis -- Learning (II) -- Scalability : LASSO & PCA -- Pragmatism -- Synthesis : architecture & pipeline.
520			_a"Data Analytics: A Small Data Approach is suitable for an introductory data analytics course to help students understand some main statistical learning models. It has many small datasets to guide students to work out pencil solutions of the models and then compare with results obtained from established R packages. Also, as data science practice is a process that should be told as a story, in this book there are many course materials about exploratory data analysis, residual analysis, and flowcharts to develop and validate models and data pipelines. The main models covered in this book include linear regression, logistic regression, tree models and random forests, ensemble learning, sparse learning, principal component analysis, kernel methods including the support vector machine and kernel regression, and deep learning. Each chapter introduces two or three techniques. For each technique, the book highlights the intuition and rationale first, then shows how mathematics is used to articulate the intuition and formulate the learning problem. R is used to implement the techniques on both simulated and real-world dataset. Python code is also available at the book's website: http://dataanalyticsbook.info. Shuai Huang is an associate professor at the department of industrial & systems engineering at the university of Washington. He conducts interdisciplinary research in machine learning, data analytics, and applied operations research with applications on healthcare, manufacturing, and transportation areas. Houtao Deng is a data science researcher and practitioner. He developed several new decision tree methods such as inTrees. He has built data-driven products for forecasting, scheduling, pricing, recommendation, fraud detection, and image recognition"--
650		0	_aQuantitative research.
650		0	_aQuantitative research _xData processing.
650		0	_aR (Computer program language)
650		0	_aPython (Computer program language)
700	1		_aDeng, Houtao, _eauthor.
776	0	8	_iOnline version: _aHuang, Shuai (Industrial engineer). _tData analytics _bFirst edition. _dBoca Raton : CRC Press, 2021 _z9781003102656 _w(DLC) 2021001776
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c18716 _d18716