000			03003nam a2200301Ia 4500
001			1581
008			230305s2013 xx 000 0 und d
020			_a9781449361327
040			_cTBS
041			_aeng
245		0	_aData science for business _b: what you need to know about data mining and data-analytic thinking _c/ Foster Provost and Tom Fawcett.
246			_aData science for business
250			_aFirst edition.
260			_bSebastopol, California : O'Reilly, 2013.
300			_axxi, 386 pages : illustrations ; 23 cm.
505			_aIntroduction : data-analytic thinking Business problems and data science solutions — Introduction to predictive modeling : from correlation to supervised segmentation — Fitting a model to data — Overfitting and its avoidance — Similarity, neighbors, and clusters — Decision analytic thinking I : what is a good model? — Visualizing model performance — Evidence and probabilities — Representing and mining text — Decision analytic thinking II : toward analytical engineering — Other data science tasks and techniques — Data science and business strategy — Conclusion.
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.
590			_bIncludes bibliographical references (pages 361-368) and index.
650		0	_aData mining _97979
650			_aBig data _95432
650		0	_aInformation science _98192
650		0	_aBusiness _xData processing _97722
650		0	_aCommerce _93749
653			_aBibliography B2 PBT Principles of Programming
700			_aProvost, Foster _eauthor _98195
700			_aFawcett, Tom _eauthor _98194
942			_2lcc
999			_c1591 _d1591