For predictive analytics, a major source of the value of big data comes from incorporating massive, fine-grained data. We see this in applications ranging from analyzing banking customers to fraud detection to online ad targeting to finding similar users on their mobile devices to predicting people’s personality traits from Facebook Likes…and more. I will dig into several of these applications revealing details and challenges.
Foster Provost is coauthor of the O’Reilly best-selling book, Data Science for Business (http://data-science-for-biz.com). He has designed data science solutions for businesses for over two decades, and has co-founded several successful companies focusing on data science for advertising (incl., Dstillery & Integral Ad Science). In his current job as Professor and NEC Faculty Fellow at the NYU Stern School of Business, Foster teaches in the MS in Data Science, MS in Business Analytics, MBA, and PhD programs. His data science research has won many awards and is broadly cited. He served as Program Chair for the ACM SIGKDD Conference and for many years as Editor-in-Chief for the journal Machine Learning.
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