000			03459cam a2200433 i 4500
001			23532456
003			OSt
005			20250904105611.0
008			240123s2024 flua b 001 0 eng
010			_a 2023036913
020			_a9780367755386 _q(hbk.)
020			_a9780367751968 _q(pbk.)
020			_z9781003162872 _q(ebk.)
035			_a23532456
040			_aDLC _beng _erda _cDLC _dDLC
042			_apcc
050			_aQA 76.9 _bT78 2024
100	1		_aTruong, Dothang, _eauthor.
245	1	0	_aData science and machine learning for non-programmers : _busing SAS Enterprise miner / _cDothang Truong.
250			_aFirst edition.
264		1	_aBoca Raton : _bCRC Press/Taylor & Francis Group, _c2024.
300			_axii, 577 pages : _billustrations ; _c27 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references (pages 555-559) and index.
505			_aPart I: Introduction to Data Mining. 1. Introduction to Data Mining and Data Science. 2. Data Mining Processes, Methods, and Software. 3. Data Sampling and Partitioning. 4. Data Visualization and Exploration. 5. Data Modification. Part II: Data Mining Methods. 6. Model Evaluation. 7. Regression Methods. 8. Decision Trees. 9. Neural Networks. 10. Ensemble Modeling. 11. Presenting Results and Writing Data Mining Reports. 12. Principal Component Analysis. 13. Cluster Analysis. Part III: Advanced Data Mining Methods. 14. Random Forest. 15. Gradient Boosting. 16. Bayesian Networks.
520			_a"As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilise machine learning effectively. Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders. Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers and industry professionals from various backgrounds"-- _cProvided by publisher.
590			_aTruong, D. (2024). Data science and machine learning for non-programmers: Using SAS Enterprise miner (1st ed.). CRC Press/Taylor & Francis Group.
630	0	0	_aEnterprise miner _vTextbooks.
650		0	_aData mining _xComputer programs _vTextbooks.
650		0	_aData mining _xStatistical methods
650		0	_aMachine learning _xStudy and teaching
650		0	_aSAS (Computer program language)
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c30703 _d30703