000			02065nam a22003017i 4500
005			20250813154223.0
008			250805s2024 flua|||fr|||| 001 0 eng d
020			_a9780367755386
040			_aEG-GaU‬‬ _cEG-GaU‬‬ _dEG-GaU‬‬ _erda
082	0	4	_223 _a006.3 _bT.D.D
100	1		_aTruong, Dothang, _eauthor. _962633
245	1	0	_aData Science and Machine Learning for Non‑Programmers: _bUsing SAS Enterprise Miner / _cDothang Truong.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2024.
300			_a577 pages : _billustrations ; _c26 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references 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.:contentReference[oaicite:2]{index=2}
520			_aA hands-on introduction to data science and machine learning for non-programmers, guiding readers through practical projects using two large datasets and SAS Enterprise Miner—with no programming required. Covers full data mining workflow, result reporting, and stakeholder communication, aimed at students and professionals across diverse non-technical fields.
650		0	_aData mining _xStudy and teaching. _962634
650		0	_aMachine learning _xStudy and teaching. _960492
650		0	_aNon‑programmers _xEducation. _962635
650		0	_aSAS Enterprise Miner. _962636
942			_2ddc _cBK
999			_c11895 _d11895