Mike Patetta
Principal Analytical Training Consultant, SAS
Michael Patetta was a statistical instructor for SAS from 1994 until his retirement in 2021. He taught a variety of courses including Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio, Predictive Modeling Using Logistic Regression, Introduction to Data Science Statistical Methods, and Regression Methods Using SAS Viya. Before coming to SAS, Michael worked in the North Carolina State Health Department for 10 years as a health statistician and program manager. He has authored or co-authored 10 published papers since 1983. Michael has a BA from the University of Notre Dame and a MA from the University of North Carolina at Chapel Hill. In his spare time, he loves to hike in National Parks.
By This Author
Introduction to Statistical and Machine Learning Methods for Data Science
By Carlos Andre Reis Pinheiro and Mike Patetta
This book provides a comprehensive overview of the statistical and machine learning techniques associated with data science initiatives. You will learn the most important techniques and methods related to data science and when to apply them for different business problems.
"A comprehensive and nifty book that takes the reader through the analytics lifecycle, discussing many data science methods, statistical approaches and machine learning techniques. The variety of case studies and tips along the way enhances your knowledge and capability, forming a practical understanding of data science".
Michelle Homes, Metacoda
Australia
Featured Authors
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Ron Klimberg, PhD, is a professor at the Haub School of Business at Saint Joseph's University in Philadelphia, PA.
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Todd Case has been working in the pharmaceutical industry as a statistical programmer for over 20 years. He completed his MS in Epidemiology from Columbia University and has worked at Forest Laboratories (now Allergan), Genentech, Bristol-Myers Squibb, and Biogen. He currently works at Vertex Pharmaceuticals.
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Robert N. Rodriguez retired from SAS in 2018 after serving as a senior director in research and development. Bob received a PhD in statistics from the University of North Carolina at Chapel Hill and was a research statistician at General Motors Research Laboratories before joining SAS.
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Terisa Roberts is a director and Global Solution lead for Risk Modeling and Decisioning at SAS. She has extensive experience in quantitative risk management, regulatory compliance, and model governance and validation. She has worked in financial services, telecommunications, government, energy, and retail sectors.