Machine Learning for Business Analytics: Concepts, Techniques and Applications in Rapidminer (Hardcover)

Machine Learning for Business Analytics

Machine learning--also known as data mining or data 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.

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation 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.

This is the seventh edition of Machine Learning for Business Analytics, and the first using RapidMiner software. This edition also includes:

• A new co-author, Amit Deokar, who brings experience teaching business analytics courses using RapidMiner
• Integrated use of RapidMiner, an open-source machine learning platform that has become commercially popular in recent years
• An expanded chapter focused on discussion of deep learning techniques
• A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning
• A new chapter on responsible data science
• Updates 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
• A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques
• End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
• A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions

This 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.

商業分析的機器學習

機器學習，也被稱為資料探勘或資料分析，是資料科學的基礎部分。組織在各種領域中使用它將原始數據轉化為可行動的信息。

商業分析的機器學習：概念、技術和在RapidMiner中的應用 提供了全面的介紹和概述這種方法論。這本暢銷教材涵蓋了用於預測、分類、可視化、維度降低、規則探勘、推薦、聚類、文本探勘、實驗和網絡分析的統計和機器學習算法。除了實踐練習和實際案例研究外，它還討論了機器學習技術的負責任使用的管理和倫理問題。

這是《商業分析的機器學習》的第七版，也是第一版使用RapidMiner軟件。本版還包括：

• 新的合著者Amit Deokar，他有使用RapidMiner教授商業分析課程的經驗
• 整合使用RapidMiner，這是一個開源的機器學習平台，在近年來在商業上非常受歡迎
• 擴展的章節專注於深度學習技術的討論
• 新的實驗反饋技術章節，包括A/B測試、提升建模和強化學習
• 新的負責任數據科學章節
• 根據教授MBA、商業分析碩士和相關課程、本科、文憑和執行課程以及學生的反饋進行的更新和新材料
• 一個完整的章節專門介紹了超過十幾個案例，展示了機器學習技術的應用
• 章節結尾的練習，幫助讀者評估和擴展他們對所呈現材料的理解和能力
• 附帶網站，提供超過兩打的數據集，以及包括練習解答、幻燈片和案例解答在內的教材

這本教材是高年級本科和研究生級別的資料科學、預測分析和商業分析課程的理想資源。對於在管理、金融、市場營銷、運營管理、信息系統、計算機科學和信息技術等領域使用量化數據的分析師、研究人員和數據科學從業人員來說，它也是一本優秀的參考書。

Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science, College of Technology Management. 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.

Peter C. Bruce, is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc.

Amit V. Deokar, PhD, is Associate Dean of Undergraduate Programs and an Associate Professor of Management Information Systems at the Manning School of Business at University of Massachusetts Lowell. Since 2006, he has developed and taught courses in business analytics, with expertise in using the RapidMiner platform. He is an Association for Information Systems Distinguished Member Cum Laude.

Nitin R. Patel, PhD, 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.

Galit Shmueli, 博士，是國立清華大學服務科學研究所科技管理學院的傑出教授。她自2004年起在馬里蘭大學、Statistics.com、印度商學院和國立清華大學等地設計並教授商業分析課程。

Peter C. Bruce 是Statistics.com的統計教育研究所創辦人，也是Elder Research, Inc.的首席學習官。

Amit V. Deokar, 博士，是麻省大學洛厄爾分校曼寧商學院的本科課程副院長和管理信息系副教授。自2006年以來，他一直在商業分析領域開發和教授課程，專精於使用RapidMiner平台。他是信息系統協會的優秀會員。

Nitin R. Patel, 博士，是Cytel Inc.的聯合創始人和首席研究員，也是塔塔諮詢服務公司的聯合創始人。作為美國統計協會的會士，Patel博士曾擔任麻省理工學院和哈佛大學的訪問教授。他是印度計算機學會的會士，並在印度艾哈邁德巴德的印度管理學院擔任教授長達15年。