Bøger af Dursun Delen

In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen's holistic approach covers all this, and more:Data mining processes, methods, and techniquesThe role and management of dataPredictive analytics tools and metricsTechniques for text and web mining, and for sentiment analysisIntegration with cutting-edge Big Data approachesThroughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.

For courses on Business Intelligence or Decision Support Systems. A managerial approach to understanding business intelligence systems. To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.

This book is written for both professionals who are interested in developing a holistic understanding about business analytics and especially about prescriptive analytics and college students, both at graduate and undergraduate levels, who are in need of a nicely balance book between theory and practice to explain prescriptive analytics as the top layer in business analytics continuum.• End-to-end, all-inclusive, holistic approach to prescriptive analytics—not only covering optimization and simulation, but also including multi-criteria decision-making methods along with inference- and heuristic-based decisioning techniques. • Enhanced with numerous conceptual illustrations, example problems and solutions, and motivational case and success stories. • Chapter 1 -- overview of business analytics, its longitudinal perspective and a simple taxonomy, and where prescriptive analytics fits into this big picture. • Chapter 2 -- introduces the topic of optimization and describes different types of optimization methods using simple yet practical examples and application cases. • Chapter 3 -- explains simulation, including Monte-Carlo simulation, discrete and continuous simulation, as a powerful tool to analyse complex systems to make better decisions. • Chapter 4 -- introduces multi-criteria decision making along with a simple taxonomy, and provides descriptions and examples of a variety of popular techniques used for multi-criteria problems commonly found in practice. • Chapter 5 -- about expert systems and case-based reasoning. • Chapter 6 -- introduces the latest techniques in analytics, namely Big Data, Deep Learning, and Cognitive Computing, as the leading edge for the next generation of automated decisioning and prescriptive analytics. Business analytics has been gaining popularity faster than any management trends we have seen in the recent history. As an attempt to unify the understanding of "what business analytics is,” the business community along with educational community have developed a simple taxonomy where they defined analytics using three progressive phases/echelons: descriptive/diagnostic analytics predictive analytics prescriptive analytics. This book is about prescriptive analytics, the highest echelon in the analytics continuum, and is the one closest to "making accurate and timely decisions.”

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. Part II describes and demonstrates basic data mining algorithms. Part III focuses on business applications of data mining.