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 techniques * The role and management of data * Predictive analytics tools and metrics * Techniques for text and web mining, and for…mehr
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 techniques * The role and management of data * Predictive analytics tools and metrics * Techniques for text and web mining, and for sentiment analysis * Integration with cutting-edge Big Data approaches Throughout, 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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Dursun Delen is an internationally renowned expert in business analytics, data science, and machine learning. He is often invited to national and international conferences to deliver keynote presentations on topics related to data/text mining, business intelligence, decision support systems, business analytics, data science, and knowledge management. Prior to his appointment as a professor at Oklahoma State University in 2001, Dr. Delen worked for industry for more than 10 years, developing and delivering business analytics solutions to companies. His most recent industrial work was at a privately owned applied research and consulting company, Knowledge Based Systems, Inc. (KBSI), in College Station, Texas, as a research scientist. During his five years at KBSI, Dr. Delen led a number of projects related to decision support systems, enterprise engineering, information systems development, and advanced business analytics that were funded by private industry and federal agencies, including several branches of the Department of Defense, NASA, National Science Foundation, National Institute for Standards and Technology, and the Department of Energy. Today, in addition to his academic endeavors, Dr. Delen provides professional education and consulting services to businesses in assessing their analytics, data science, and information system needs and helping them develop state-of-the-art computerized decision support systems. In his current academic position, Dr. Delen holds the William S. Spears Endowed Chair in Business Administration and the Patterson Family Endowed Chair in Business Analytics, and he is the director of research for the Center for Health Systems Innovation and regents' professor of management science and information systems in the Spears School of Business at Oklahoma State University. He has published more than 150 peer-reviewed research articles that have appeared in major journals, including Journal of Business Research, Journal of Business Analytics, Decision Sciences Journal, Decision Support Systems, Communications of the ACM, Computers & Operations Research, Annals of Operations Research, Computers in Industry, Journal of Production Operations Management, Artificial Intelligence in Medicine, Journal of the American Medical Informatics Association, Expert Systems with Applications, Renewable and Sustainable Energy Reviews, Energy, and Renewable Energy, among others. He has also authored and coauthored 11 books and textbooks in the broad area of business analytics, data science, and business intelligence. Dr. Delen regularly chairs tracks and minitracks at various business analytics and information systems conferences. Currently, he is the editor-in-chief for the Journal of Business Analytics and AI in Business (in Frontiers in Artificial Intelligence), senior editor for the Journal of Decision Support Systems, Decision Sciences, and Journal of Business Research, associate editor for Decision Analytics, International Journal of Information and Knowledge Management, and International Journal of RF Technologies, and is on the editorial boards of several other academic journals. He has been the recipient of several research and teaching awards, including the prestigious Fulbright scholar, regents' distinguished teacher and researcher, president's outstanding researcher, and Big Data mentor awards.
Inhaltsangabe
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 BiasVariance 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
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 BiasVariance 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
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