000			01742nam a22002417a 4500
005			20240530104847.0
008			171014t2013 xxua|||gr|||| 001 0 eng d
020			_a9781449361327
082	0	4	_223 _a006.312 _bP969d
100	1	0	_aProvost, Foster _92254
245	1	0	_aData sciences for business / _cFoster Provost, Tom Fawcett.
260			_aSebastopol: _bO'Reilly Media, _c2013.
300			_a386 páginas: _bilustraciones.
490			_aData science / business
500			_aIncluye índice de materias.
505	2		_a1. Introduction: Data-Analytic Thinking.--2. Business Problems anda Data Science Solutions.--3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation.-- 4. Fitting and Model ti Data.-- 5. Overfitting and Its Avoidance.-- 6. Similarity, Neighbors, and Clusters.--7. Decision Analytic Thinking I: What Is a Good Model?.-- 8. Visualizing Model Performance.-- 9. Evidence and Probabilities.-- 10. Representing and Mining Text.-- 11. Decision Analytic Thinking II: Toward Analytical Engineering.-- 12.Other Data Science Task and Techniques.-- 13. Data Science and Business Strategy.-- 14. Conclusion.
520			_aThis broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowlegde and business value from the data you collect. By learning data science principles, you will understand the many data mining techniques in use today.
650	1	4	_aMinería de datos _92226
650	2	4	_aAnálisis de datos _92232
650	2	4	_aProcesamiento de datos _92256 _xNegocios
700	1		_aFawcett, Tom (Autor) _92259
942			_2ddc _cBK _h006.312 _kP969d
999			_c4898 _d4898