Böcker i Pearson Business Analytics Series-serien

This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don't. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You'll learn how to: Develop complex, testable theories for understanding individual and social behavior in web products Think like a social scientist and contextualize individual behavior in today's social environments Build more effective metrics and KPIs for any web product or system Conduct more informative and actionable A/B tests Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation Alter user behavior in a complex web product Understand how relevant human behaviors develop, and the prerequisites for changing them Choose the right statistical techniques for common tasks such as multistate and uplift modeling Use advanced statistical techniques to model multidimensional systems Do all of this in R (with sample code available in a separate code manual) Build better theories and metrics, and drive more of the behaviors you want Model, understand, and alter customer behavior to increase revenue and retention Construct better frameworks for examining why your customers do what they do Develop core metrics for user analytics, and conduct more effective A/B tests Master key techniques that most books ignore, including statistical matching and uplift modeling Use R and this book's many R examples to implement these techniques yourselfUse data science and social science to generate real changes in customer behavior Build better theories and metrics, and drive more of the behaviors you want Model, understand, and alter customer behavior to increase revenue and retention Construct better frameworks for examining why your customers do what they do Develop core metrics for user analytics, and conduct more effective A/B tests Master key techniques that most books ignore, including statistical matching and uplift modeling Use R and this book's many R examples to implement these techniques yourself

In Artificial Intelligence for Business, author Doug Rose offers today's most accessible and useful introduction to AI and ML technologies ? and what they can and can't do. Rose begins by tracing AI's evolution from the early 1950s to the present, illuminating core ideas that still drive its development. Next, he explores recent innovations that have reinvigorated the field by providing the ?big data? that makes machine learning so powerful ? innovations such as GPS, social media and electronic transactions. Finally, he explains how today's machines learn by combining powerful processing, advanced algorithms, and artificial neural networks that mimic the human brain.Throughout, he illustrates key concepts with practical examples that help students connect AI, ML, and neural networks to specific problems and solutions. Step by step, he systematically demystifies these powerful technologies, removing the fear, bewilderment, and advanced math ? so students can understand the new possibilities they create, and start using them.

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.