Data Mining for Business Analytics

Author: Galit Shmueli
Publisher: John Wiley & Sons
ISBN: 1118729242
Format: PDF, Docs
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Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also 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.

Data Mining for Business Intelligence

Author: Galit Shmueli
Publisher: John Wiley & Sons
ISBN: 0470084855
Format: PDF, ePub, Mobi
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Learn how to develop models for classification, prediction, and customer segmentation with the help of Data Mining for Business Intelligence In today's world, businesses are becoming more capable of accessing their ideal consumers, and an understanding of data mining contributes to this success. Data Mining for Business Intelligence, which was developed from a course taught at the Massachusetts Institute of Technology's Sloan School of Management, and the University of Maryland's Smith School of Business, uses real data and actual cases to illustrate the applicability of data mining intelligence to the development of successful business models. Featuring XLMiner, the Microsoft Office Excel add-in, this book allows readers to follow along and implement algorithms at their own speed, with a minimal learning curve. In addition, students and practitioners of data mining techniques are presented with hands-on, business-oriented applications. An abundant amount of exercises and examples are provided to motivate learning and understanding. Data Mining for Business Intelligence: Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis Features a business decision-making context for these key methods Illustrates the application and interpretation of these methods using real business cases and data This book helps readers understand the beneficial relationship that can be established between data mining and smart business practices, and is an excellent learning tool for creating valuable strategies and making wiser business decisions.

Real Time Data Mining

Author: Florian Stompe
Publisher: Diplomica Verlag
ISBN: 3836678799
Format: PDF, Docs
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Data Mining ist ein inzwischen etabliertes, erfolgreiches Werkzeug zur Extraktion von neuem, bislang unbekanntem Wissen aus Daten. In mittlerweile fast allen gr eren Unternehmen wird es genutzt um Mehrwerte f r Kunden zu generieren, den Erfolg von Marketingkampagnen zu erh hen, Betrugsverdacht aufzudecken oder beispielsweise durch Segmentierung unterschiedliche Kundengruppen zu identifizieren. Ein Grundproblem der intelligenten Datenanalyse besteht darin, dass Daten oftmals in rasanter Geschwindigkeit neu entstehen. Eink ufe im Supermarkt, Telefonverbindungen oder der ffentliche Verkehr erzeugen t glich eine neue Flut an Daten, in denen potentiell wertvolles Wissen steckt. Die versteckten Zusammenh nge und Muster k nnen sich im Zeitverlauf mehr oder weniger stark ver ndern. Datenmodellierung findet in der Regel aber noch immer einmalig bzw. sporadisch auf dem Snapshot einer Datenbank statt. Einmal erkannte Muster oder Zusammenh nge werden auch dann noch angenommen, wenn diese l ngst nicht mehr bestehen. Gerade in dynamischen Umgebungen wie zum Beispiel einem Internet-Shop sind Data Mining Modelle daher schnell veraltet. Betrugsversuche k nnen dann unter Umst nden nicht mehr erkannt, Absatzpotentiale nicht mehr genutzt werden oder Produktempfehlungen basieren auf veralteten Warenk rben. Um dauerhaft Wettbewerbsvorteile erzielen zu k nnen, muss das Wissen ber Daten aber m glichst aktuell und von ausgezeichneter Qualit t sein. Der Inhalt dieses Buches skizziert Methoden und Vorgehensweisen von Data Mining in Echtzeit.

big data work

Author: Thomas H. Davenport
Publisher: Vahlen
ISBN: 3800648156
Format: PDF, Kindle
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Big Data in Unternehmen. Dieses neue Buch gibt Managern ein umfassendes Verständnis dafür, welche Bedeutung Big Data für Unternehmen zukünftig haben wird und wie Big Data tatsächlich genutzt werden kann. Am Ende jedes Kapitels aktivieren Fragen, selbst nach Lösungen für eine erfolgreiche Implementierung und Nutzung von Big Data im eigenen Unternehmen zu suchen. Die Schwerpunkte - Warum Big Data für Sie und Ihr Unternehmen wichtig ist - Wie Big Data Ihre Arbeit, Ihr Unternehmen und Ihre Branche verändern - - wird - Entwicklung einer Big Data-Strategie - Der menschliche Aspekt von Big Data - Technologien für Big Data - Wie Sie erfolgreich mit Big Data arbeiten - Was Sie von Start-ups und Online-Unternehmen lernen können - Was Sie von großen Unternehmen lernen können: Big Data und Analytics 3.0 Der Experte Thomas H. Davenport ist Professor für Informationstechnologie und -management am Babson College und Forschungswissenschaftler am MIT Center for Digital Business. Zudem ist er Mitbegründer und Forschungsdirektor am International Institute for Analytics und Senior Berater von Deloitte Analytics.

Getting Started with Business Analytics

Author: David Roi Hardoon
Publisher: CRC Press
ISBN: 1439896542
Format: PDF, ePub, Docs
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Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and the various requirements for integrating business with analytics. The second part presents the technologies underlying business analytics: data mining and data analytics. The book helps you understand the key concepts and ideas behind data mining and shows how data mining has expanded into data analytics when considering new types of data such as network and text data. The third part explores business analytics in depth, covering customer, social, and operational analytics. Each chapter in this part incorporates hands-on projects based on publicly available data. Helping you make sound decisions based on hard data, this self-contained guide provides an integrated framework for data mining in business analytics. It takes you on a journey through this data-rich world, showing you how to deploy business analytics solutions in your organization.

Modeling Online Auctions

Author: Wolfgang Jank
Publisher: John Wiley & Sons
ISBN: 9781118031865
Format: PDF, ePub, Mobi
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Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data. The authors draw upon their extensive experience to introduce the latest methods for extracting new knowledge from online auction data. Rather than approach the topic from the traditional game-theoretic perspective, the book treats the online auction mechanism as a data generator, outlining methods to collect, explore, model, and forecast data. Topics covered include: Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems Throughout the book, R and MATLAB software are used for illustrating the discussed techniques. In addition, a related Web site features many of the book's datasets and R and MATLAB code that allow readers to replicate the analyses and learn new methods to apply to their own research. Modeling Online Auctions is a valuable book for graduate-level courses on data mining and applied regression analysis. It is also a one-of-a-kind reference for researchers in the fields of statistics, information systems, business, and marketing who work with electronic data and are looking for new approaches for understanding online auctions and processes. Visit this book's companion website by clicking here