Dr. Eric Siegel: Founder of the Predictive Analytics World conference series, Siegel is a recognized figure in the field. His book, “Predictive Analytics,” has been influential in providing a practical guide to the subject.
Dr. Dean Abbott: Co-founder of SmarterHQ, Abbott has over three decades of experience in data mining and predictive analytics. His contributions lie in the development of anomaly detection algorithms and time series forecasting.
Prof. Usama Fayyad: As a former Chief Data Officer of Yahoo! and a veteran in the data analytics space, Fayyad’s insights into large-scale data strategy and predictive modeling have shaped industries.
Dr. Kira Radinsky: Radinsky is the Chairwoman & CTO of Diagnostic Robotics, where predictive analytics plays a central role. Known for her work on predicting global events using large-scale data, she was cited in the “30 Under 30” list by Forbes.
Prof. Robert Nisbet: A leading educator in the domain, Nisbet’s writings, particularly the “Handbook of Statistical Analysis & Data Mining Applications,” are considered foundational texts in predictive analytics.
Dr. John Elder: Founder of Elder Research, a data science consultancy, Elder’s work on data mining and investment strategies demonstrates the real-world impact of predictive analytics.
Dr. Hilary Mason: The founder of Fast Forward Labs (now Cloudera Fast Forward), Mason’s expertise lies in the practical implications and applications of big data and predictive modeling in diverse sectors.
Prof. Nathaniel Dean: As a researcher at Texas State University, Dean’s work focuses on network structures, algorithms, and their implications for predictive analytics.
Dr. Bernard Marr: A bestselling author and keynote speaker, Marr’s insights into big data, artificial intelligence, and analytics have made him a sought-after expert on how predictive analytics is transforming businesses.
Prof. Trevor Hastie: Based at Stanford University, Hastie’s contributions, particularly his book “The Elements of Statistical Learning,” are seminal in the domain of statistical modeling and its applications in prediction.
