![[Review] Data Science for Business (Foster Provost) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2671723/c1a-085k3-255n1vj2h604-84jtq6.jpg)
Show Notes
Data Science for Business (Foster Provost)
- Amazon USA Store: https://www.amazon.com/dp/B08VL5K5ZX?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Science-for-Business-Foster-Provost.html
- Apple Books: https://books.apple.com/us/audiobook/data-science-for-business-what-you-need-to-know-about/id1573417412?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Science+for+Business+Foster+Provost+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/B08VL5K5ZX/
#Dataanalyticthinking #Supervisedandunsupervisedlearning #Overfittingandgeneralization #Confusionmatrixandexpectedvalue #Dataminingprocess #DataScienceforBusiness
Data Science for Business is a nontechnical business and management guide to the principles behind data mining and data-analytic thinking. Written by Foster Provost and Tom Fawcett, it grew from material designed for MBA education and addresses the gap between executives who must make decisions and specialists who build analytical systems. Its purpose is not to teach programming or provide a catalog of algorithms. Instead, it explains how organizations can frame decisions as data problems, judge whether available evidence can support a solution, and evaluate whether a model produces economic value. The book treats data as an asset whose value depends on investment in collection, preparation, analysis, deployment, and organizational capability. It introduces common predictive and descriptive tasks, the logic of supervised and unsupervised learning, and safeguards against misleading results. Throughout, the emphasis is on informed participation: business leaders should be able to ask better questions, interpret model performance, understand tradeoffs, and work productively with data science teams.
- Amazon USA Store: https://www.amazon.com/dp/B08VL5K5ZX?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Science-for-Business-Foster-Provost.html
- Apple Books: https://books.apple.com/us/audiobook/data-science-for-business-what-you-need-to-know-about/id1573417412?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Science+for+Business+Foster+Provost+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/B08VL5K5ZX/
#Dataanalyticthinking #Supervisedandunsupervisedlearning #Overfittingandgeneralization #Confusionmatrixandexpectedvalue #Dataminingprocess #DataScienceforBusiness
Data Science for Business is a nontechnical business and management guide to the principles behind data mining and data-analytic thinking. Written by Foster Provost and Tom Fawcett, it grew from material designed for MBA education and addresses the gap between executives who must make decisions and specialists who build analytical systems. Its purpose is not to teach programming or provide a catalog of algorithms. Instead, it explains how organizations can frame decisions as data problems, judge whether available evidence can support a solution, and evaluate whether a model produces economic value. The book treats data as an asset whose value depends on investment in collection, preparation, analysis, deployment, and organizational capability. It introduces common predictive and descriptive tasks, the logic of supervised and unsupervised learning, and safeguards against misleading results. Throughout, the emphasis is on informed participation: business leaders should be able to ask better questions, interpret model performance, understand tradeoffs, and work productively with data science teams.
