000			03173nam a2200301 i 4500
001			EBL1323973
003			AU-PeEL
005			20200221101120.0
008			170802s2013 ch a||||s|||| 001 | eng d
020			_a9781449374297
040			_aMIUC _beng _cMIUC
082	0		_a658.4038
082	0		_a006.312
100	1		_93213 _aProvost, Foster
245	1	0	_aData science for business : _b[what you need to know about data mining and data-analytic thinking] _h[electronic resource] / _cFoster Provost and Tom Fawcett.
260			_aBeijing, etc. : _bO'Reilly, _c2013.
300			_a1 online resource (xxi, 386 p.) : _bill. b&w and col.
336			_2rdacontent _atext
504			_aIncludes bibliographical references and index.
505	0		_aCh. 1. Introduction: Data-analytic thinking -- Ch. 2. Business problems and data science solutions -- Ch. 3. Introduction to predictive modelling: from correlation to supervised segmentation -- Ch. 4. Fitting a model to data -- Ch. 5. Overfitting and its avoidance -- Ch. 6. Similarity, neighbours, and clusters -- Ch. 7. Decision analytic thinking I: What is a good model? -- Ch. 8. Visualizing model performance -- Ch. 9. Evidence and probabilities -- Ch. 10. Representing and mining text -- Ch. 11. Decision analytic thinking II: Toward analytical engineering -- Ch. 12. Other data science tasks and techniques -- Ch. 13. Data science and business strategy -- Ch. 14. Conclusion -- Appendix A. Proposal review guide -- Appendix B. Another sample proposal.
520			_aWritten by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You'll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company's data science projects. You'll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization and how you can use it for competitive advantage; Treat data as a business asset that requires careful investment if you're to gain real value; Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way; Learn general concepts for actually extracting knowledge from data; Apply data science principles when interviewing data science job candidates.
650		0	_91863 _aBig data
650		0	_9794 _aData mining
650		0	_948 _aBusiness _xData processing
700	1		_93214 _aFawcett, Tom
856	4	0	_uhttp://ebookcentral.proquest.com/lib/miu/detail.action?docID=1323973 _zClick here to view
942			_2ddc _cELEC