Applying Predictive Analytics : Finding Value in Data

ISBN-13: 9783030140373 / Angielski / Twarda / 2019 / 205 str.

Richard McCarthy; Mary McCarthy; Wendy Ceccucci

Spis treści

Introduction to Predictive Analytics.- Know Your Data – Data Preparation.- What do Descriptive Statistics Tell Us.- The First of the Big Three – Regression.- The Second of the Big Three – Decision Trees.- The Third of the Big Three - Neural Networks.- Model Comparisons and Scoring.- Appendix A.- Data Dictionary for the Automobile Insurance Claim Fraud Data Example.- Conclusion.

O autorze

Richard V. McCarthy (DBA, Nova Southeastern University, MBA, Western New England College) is a professor of Computer Information Systems at the School of Business, Quinnipiac University. He also serves as the director for the Master of Science in Business Analytics program. Prior to this, Dr. McCarthy was an associate professor of management information systems at Central Connecticut State University. He has twenty years of experience within the insurance industry and has held a Charter Property Casualty Underwriter (CPCU) designation since 1991. He has authored numerous research articles and contributed to several textbooks. He has served as the associate dean of the School of Business as well as the MBA director. He served as a member of the board of the International Association for Computer Information Systems and is currently a member of the board of the EABOK project.

Wendy Ceccucci (PhD and MBA, Virginia Polytechnic University) is a Professor and Chair of Computer Information Systems at Quinnipiac University.  Her teaching areas include business analytics and programming. She is the past president of the Education Special Interest Group (EDSIG) of the Association for Information Technology Professionals (AITP) and past Associate Editor of the Information Systems Education Journal (ISEDJ). Her research interests lies in Information Systems Pedagogy.

Leila Halawi (DBA, Nova Southeastern University, MBA and BS, Lebanese American University) is an associate professor in the department of Technology Management in the College of Business, Worldwide Campus. Dr Halawi serves as the MIS Discipline Chair, Chair of the Research Committee for the Senate and the Director of Research for the College of Business.

She developed many of the courses within the MMIS program. She holds a Certification by Sloan C Consortium in Online Teaching, and a certificate from Quality Matters on Applying the QM Rubrics, (APPQMR). She is an advisory board member and a  reviewer for the Enterprise Architecture Body of Knowledge (EABOK), Mitre Corporation. She is a Reviewer for the Journal of Computer Information Systems (JCIS) and the International Association for computer information systems (IACIS). She is also a program committee member and reviewer for the European Conference on Social Media, (ECSM), the European Conference on Knowledge Management (ECKM) and the International Conference on Intellectual Capital and Knowledge Management (ICIKM), the Federated Conference on computer science and information systems (FEDCSIS). She is also part of the IT editorial board of the multimedia educational resource for learning and online teaching (MERLOT).

Mary McCarthy (DBA, Nova Southeastern University, MBA, University of Connecticut) a professor of Accounting, Central Connecticut State. She has twenty years of financial reporting experience and has served as the controller for a Fortune 50 industry organization. She holds a CPA, CFA and CMA designation.  She has authored numerous research articles.

Blurb

This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes.

• Focuses on how to use predictive analytic techniques to analyze historical data for the purpose of predicting future results;
  
• Takes an applied approach and focus on solving business problems using predictive analytics and features case studies and a variety of examples;
  
• Uses examples in SAS Enterprise Miner, one of world’s leading analytics software tools.