David B. Speights, Daniel M. Downs, Adi Raz
Essentials of Modeling and Analytics
Retail Risk Management and Asset Protection
David B. Speights, Daniel M. Downs, Adi Raz
Essentials of Modeling and Analytics
Retail Risk Management and Asset Protection
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Essentials of Modeling and Analytics illustrates how and why analytics can be used effectively by loss prevention staff.
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Essentials of Modeling and Analytics illustrates how and why analytics can be used effectively by loss prevention staff.
Produktdetails
- Produktdetails
- Verlag: Routledge
- Seitenzahl: 342
- Erscheinungstermin: 12. Dezember 2019
- Englisch
- Abmessung: 254mm x 178mm x 18mm
- Gewicht: 645g
- ISBN-13: 9780367878801
- ISBN-10: 0367878801
- Artikelnr.: 58483359
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
- Verlag: Routledge
- Seitenzahl: 342
- Erscheinungstermin: 12. Dezember 2019
- Englisch
- Abmessung: 254mm x 178mm x 18mm
- Gewicht: 645g
- ISBN-13: 9780367878801
- ISBN-10: 0367878801
- Artikelnr.: 58483359
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
David B. Speights, Ph.D., is the Chief Data Scientist for Appriss, which provides proprietary data and analytics solutions to address risk, fraud, security, safety, health, and compliance issues. He has 20 years of experience developing and deploying analytical solutions for some of the world's largest corporations, retailers, and government agencies. Speights was formerly first vice president of Mortgage Credit Risk Modeling at Washington Mutual and chief statistician at HNC Software. He holds a Ph.D. in Biostatistics from the University of California, Los Angeles, and has several patents. Daniel M. Downs, Ph.D., is the Senior Statistical Criminologist at Appriss and has spent the last 5 years focusing on predictive modeling. He is an author, presenter, and researcher, and has a vast background in loss prevention and criminology. Downs was a research coordinator for the Loss Prevention Research Council, where he engaged in fact-based research to develop loss control solutions to positively affect the retail industry. He received his Ph.D. in Criminology from the University of Illinois and his M.A. in Experimental Psychology from California State University, San Bernardino. Adi Raz, MBA, is the Senior Director of Data Sciences and Modeling at Appriss and has over 15 years of experience developing analytics solutions and modeling for the retail and financial services industries. Raz has spent many years developing predictive and analytical solutions for over 30 national retailers. She manages a data sciences and modeling team and is also responsible for analytical research and development. She received her B.S. in Economics and Statistics from James Madison University and her MBA from Pepperdine University, and is currently a Business Administration doctoral candidate.
Chapter 1: Introduction
Chapter 2: Analytics in Loss Prevention Today
Chapter 3: Tools, Staffing, and Training Considerations for Loss Prevention
Analytics
Chapter 4: Data Exploration
Chapter 5: Analytical Methods for Loss Prevention
Chapter 6: Creating a Business Case with Analytics
Chapter 7: Trends in Future Loss Prevention Analytics and Closing Thoughts
Chapter 2: Analytics in Loss Prevention Today
Chapter 3: Tools, Staffing, and Training Considerations for Loss Prevention
Analytics
Chapter 4: Data Exploration
Chapter 5: Analytical Methods for Loss Prevention
Chapter 6: Creating a Business Case with Analytics
Chapter 7: Trends in Future Loss Prevention Analytics and Closing Thoughts
Chapter 1: Introduction
Chapter 2: Analytics in Loss Prevention Today
Chapter 3: Tools, Staffing, and Training Considerations for Loss Prevention
Analytics
Chapter 4: Data Exploration
Chapter 5: Analytical Methods for Loss Prevention
Chapter 6: Creating a Business Case with Analytics
Chapter 7: Trends in Future Loss Prevention Analytics and Closing Thoughts
Chapter 2: Analytics in Loss Prevention Today
Chapter 3: Tools, Staffing, and Training Considerations for Loss Prevention
Analytics
Chapter 4: Data Exploration
Chapter 5: Analytical Methods for Loss Prevention
Chapter 6: Creating a Business Case with Analytics
Chapter 7: Trends in Future Loss Prevention Analytics and Closing Thoughts