Handbook of Statistical Analysis
Subtitle
AI and ML Applications
Edited by Robert A. Nisbet
This book presents basic theory of predictive analytics operations, and includes a number of tutorials on how to use the KNIME Analytics Platform to build machine learning models in many scientific and business data domains.
Bio
Bob Nisbet, PhD, is a Data Scientist, trained initially in Ecosystems Analysis at Arizona State University (PhD, 1972).
Dr. Nisbet taught formerly at Malone University in Canton, OH, in the Predictive Analytics Certificate Program at UC-Irvine, and in Environmental Science at UC-Santa Barbara. His research experience at UC-Santa Barbara included forest growth modeling under projected global warming conditions in forests of the US, Canada, Costa Rica, and Siberia. His industrial experience includes predictive modeling at AT&T, NCR, and FICO. He has worked also in Insurance, Credit, membership organizations (e.g. AAA), Education, and Health Care industries. He retired as an Assistant Vice President of Santa Barbara Bank & Trust, Technical Services, in charge of business intelligence reporting and l customer relationship management (CRM) modeling.
He has co-authored 3 books in Data Science:
- The Prose award-winning “Handbook of Statistical Analysis & Data Mining Applications” (Academic Press, 2009, 2nd Ed. 2017, 3rd Ed. 2024).
- The Prose Award-winning "Practical Text Mining" (Academic Press, 2012)
- “Practical Predictive Analytics and Decisioning Systems in Medicine” (Academic Press, 2015). The 2nd edition was published in 2021.
His current research activities include research in cancer prediction from non-invasive machine learning analysis of volatile organic compounds in blood plasm and breath, and failure analysis in machines by machine learning analysis of acoustic resonance spectroscopy measurements.
