Foreword Chapter 1 Introduction to Analytics What's in a Name? Why the Sudden Popularity of Analytics and Data Science? The Application Areas of Analytics The Main Challenges of Analytics A Longitudinal View of Analytics A Simple Taxonomy for Analytics The Cutting Edge of Analytics: IBM Watson Summary References Chapter 2 Introduction to Predictive Analytics and Data Mining What Is Data Mining? What Data Mining Is Not The Most Common Data Mining Applications What Kinds of Patterns Can Data Mining Discover? Popular Data Mining Tools The Dark Side of Data Mining: Privacy Concerns Summary References Chapter 3 Standardized Processes for Predictive Analytics The Knowledge Discovery in Databases (KDD) Process Cross-Industry Standard Process for Data Mining (CRISP-DM) SEMMA SEMMA Versus CRISP-DM Six Sigma for Data Mining Which Methodology Is Best? Summary References Chapter 4 Data and Methods for Predictive Analytics The Nature of Data in Data Analytics Preprocessing of Data for Analytics Data Mining Methods Prediction Classification Decision Trees Cluster Analysis for Data Mining k-Means Clustering Algorithm Association Apriori Algorithm Data Mining and Predictive Analytics Misconceptions and Realities Summary References Chapter 5 Algorithms for Predictive Analytics Naive Bayes Nearest Neighbor Similarity Measure: The Distance Metric Artificial Neural Networks Support Vector Machines Linear Regression Logistic Regression Time-Series Forecasting Summary References Chapter 6 Advanced Topics in Predictive Modeling Model Ensembles Bias–Variance Trade-off in Predictive Analytics Imbalanced Data Problems in Predictive Analytics Explainability of Machine Learning Models for Predictive Analytics Summary References Chapter 7 Text Analytics, Topic Modeling, and Sentiment Analysis Natural Language Processing Text Mining Applications The Text Mining Process Text Mining Tools Topic Modeling Sentiment Analysis Summary References Chapter 8 Big Data for Predictive Analytics Where Does Big Data Come From? The Vs That Define Big Data Fundamental Concepts of Big Data The Business Problems That Big Data Analytics Addresses Big Data Technologies Data Scientists Big Data and Stream Analytics Data Stream Mining Summary References Chapter 9 Deep Learning and Cognitive Computing Introduction to Deep Learning Basics of “Shallow” Neural Networks Elements of an Artificial Neural Network Deep Neural Networks Convolutional Neural Networks Recurrent Networks and Long Short-Term Memory Networks Computer Frameworks for Implementation of Deep Learning Cognitive Computing Summary References Appendix A KNIME and the Landscape of Tools for Business Analytics and Data Science 9780136738510   TOC    11/12/2020

Use predictive analytics to uncover hidden patterns and correlations, and leverage them to improve all business decision-making An end-to-end, holistic guide to theory and practice — packed with conceptual illustrations, example problems and solutions, and case studiesPresents rich machine learning algorithms, the latest trends and methods, and plenty of hands-on tutorialsBy Dr. Dursun Delen, one of the world’s leading experts in advanced business analytics

Updated, enriched, and streamlined throughout, with:  New application cases in several chaptersA rich set of machine learning algorithms for data mining and predictive analytics (with application case studies and hands on tutorials)New coverage of the latest machine learning trends and methodsAn updated preview of the future of analytics, including cloud-based deployment, decision automation, and robotics