{"product_id":"machine-learning-for-business-analytics-isbn-9781119829836","title":"Machine Learning for Business Analytics","description":"\u003cb\u003eMACHINE LEARNING \u003csmall\u003eFOR\u003c\/small\u003e BUSINESS ANALYTICS\u003c\/b\u003e \u003cp\u003eMachine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining\u003c\/i\u003e provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.\u003c\/p\u003e \u003cp\u003eThis fourth edition of Machine Learning for Business Analytics also includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn expanded chapter on deep learning\u003c\/li\u003e \u003cli\u003eA new chapter on experimental feedback techniques, including A\/B testing, uplift modeling, and reinforcement learning\u003c\/li\u003e \u003cli\u003eA new chapter on responsible data science\u003c\/li\u003e \u003cli\u003eUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students\u003c\/li\u003e \u003cli\u003eA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques\u003c\/li\u003e \u003cli\u003eEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c\/li\u003e \u003cli\u003eA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.\u003c\/p\u003e \u003cp\u003eForeword xix\u003c\/p\u003e \u003cp\u003ePreface to the Fourth Edition xxi\u003c\/p\u003e \u003cp\u003eAcknowledgments xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I PRELIMINARIES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 1 Introduction 3\u003c\/p\u003e \u003cp\u003eCHAPTER 2 Overview of the Machine Learning Process 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II DATA EXPLORATION AND DIMENSION REDUCTION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 3 Data Visualization 59\u003c\/p\u003e \u003cp\u003eCHAPTER 4 Dimension Reduction 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III PERFORMANCE EVALUATION\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 5 Evaluating Predictive Performance 115\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV PREDICTION AND CLASSIFICATION METHODS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 6 Multiple Linear Regression 151\u003c\/p\u003e \u003cp\u003eCHAPTER 7 k-Nearest-Neighbors (k-NN) 169\u003c\/p\u003e \u003cp\u003eCHAPTER 8 The Naive Bayes Classifier 181\u003c\/p\u003e \u003cp\u003eCHAPTER 9 Classification and Regression Trees 197\u003c\/p\u003e \u003cp\u003eCHAPTER 10 Logistic Regression 229\u003c\/p\u003e \u003cp\u003eCHAPTER 11 Neural Nets 257\u003c\/p\u003e \u003cp\u003eCHAPTER 12 Discriminant Analysis 283\u003c\/p\u003e \u003cp\u003eCHAPTER 13 Generating, Comparing, and Combining Multiple Models 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART V INTERVENTION AND USER FEEDBACK\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 14 Experiments, Uplift Modeling, and Reinforcement Learning 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VI MINING RELATIONSHIPS AMONG RECORDS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 15 Association Rules and Collaborative Filtering 341\u003c\/p\u003e \u003cp\u003eCHAPTER 16 Cluster Analysis 369\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VII FORECASTING TIME SERIES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 17 Handling Time Series 401\u003c\/p\u003e \u003cp\u003eCHAPTER 18 Regression-Based Forecasting 415\u003c\/p\u003e \u003cp\u003eCHAPTER 19 Smoothing Methods 445\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART VIII DATA ANALYTICS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 20 Social Network Analytics 467\u003c\/p\u003e \u003cp\u003eCHAPTER 21 Text Mining 487\u003c\/p\u003e \u003cp\u003eCHAPTER 22 Responsible Data Science 507\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IX CASES\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCHAPTER 23 Cases 537\u003c\/p\u003e \u003cp\u003eReferences 575\u003c\/p\u003e \u003cp\u003eData Files Used in the Book 577\u003c\/p\u003e \u003cp\u003eIndex 579\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eGalit Shmueli, PhD,\u003c\/b\u003e is Distinguished Professor and Institute Director at National Tsing Hua University’s Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePeter C. Bruce,\u003c\/b\u003e is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKuber R. Deokar,\u003c\/b\u003e is the Data Science Team Lead at UpThink Experts, India. He is also a faculty member at Statistics.com. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eNitin R. Patel, PhD,\u003c\/b\u003e is cofounder and lead researcher at Cytel Inc. He was also a co-founder of Tata Consultancy Services. A Fellow of the American Statistical Association, Dr. Patel has served as a visiting professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years.   \u003c\/p\u003e\u003cp\u003eMachine learning—also known as data mining or predictive analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMachine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining\u003c\/i\u003e provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.\u003c\/p\u003e \u003cp\u003eThis fourth edition of Machine Learning for Business Analytics also includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn expanded chapter on deep learning\u003c\/li\u003e \u003cli\u003eA new chapter on experimental feedback techniques, including A\/B testing, uplift modeling, and reinforcement learning\u003c\/li\u003e \u003cli\u003eA new chapter on responsible data science\u003c\/li\u003e \u003cli\u003eUpdates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students\u003c\/li\u003e \u003cli\u003eA full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques\u003c\/li\u003e \u003cli\u003eEnd-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented\u003c\/li\u003e \u003cli\u003eA companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989548155109,"sku":"NP9781119829836","price":111.5,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119829836.jpg?v=1761784551","url":"https:\/\/k12savings.com\/es\/products\/machine-learning-for-business-analytics-isbn-9781119829836","provider":"K12savings","version":"1.0","type":"link"}