Data Mining And Market Intelligence
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Data Mining And Market Intelligence For Optimal Marketing Returns
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Author : Susan Chiu
language : en
Publisher: Routledge
Release Date : 2008
Data Mining And Market Intelligence For Optimal Marketing Returns written by Susan Chiu and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.
Shows how marketing research and data mining techniques will boost return on investment.
Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-14
Data Mining For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Mathematics categories.
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described 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, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R
Data Mining And Market Intelligence
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Author : Mustapha Akinkunmi
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-04-30
Data Mining And Market Intelligence written by Mustapha Akinkunmi and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-30 with Computers categories.
This book is written to address the issues relating to data gathering, data warehousing, and data analysis, all of which are useful when working with large amounts of data. Using practical examples of market intelligence, this book is designed to inspire and inform readers on how to conduct market intelligence by leveraging data and technology, supporting smart decision making. The book explains some suitable methodologies for data analysis that are based on robust statistical methods. For illustrative purposes, the author uses real-life data for all the examples in this book. In addition, the book discusses the concepts, techniques, and applications of digital media and mobile data mining. Hence, this book is a guide tool for policy makers, academics, and practitioners whose areas of interest are statistical inference, applied statistics, applied mathematics, business mathematics, quantitative techniques, and economic and social statistics.
Business Intelligence And Data Mining
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Author : Anil K. Maheshwari
language : en
Publisher:
Release Date : 2014-12-30
Business Intelligence And Data Mining written by Anil K. Maheshwari and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-30 with Business & Economics categories.
Data Analytics and Data-based Decision-making are hot topics now. Big Data has entered the common parlance. Many kinds of data are generated by business, social media, machines, and more. Organizations have a choice: they can be buried under the avalanche of data, or they can do something with it to increase competitive advantage. A new field of Data Science is born, and Data Scientist has been called the sexiest job of the decade. Students across a variety of academic departments, including business, computer science, statistics, and engineering are attracted to the idea of discovering new insights and ideas from data. This is a proposal for a short and lucid book on this whole area. It is designed to provide a student with the intuition behind this evolving area, along with a solid toolset of the major data mining techniques and platforms, all within a single semester- or quarter-long course.
Data Mining For Business Applications
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Author : Longbing Cao
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-10-03
Data Mining For Business Applications written by Longbing Cao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-03 with Computers categories.
Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.
Microsoft Data Mining
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Author : Barry de Ville
language : en
Publisher: Digital Press
Release Date : 2001-05
Microsoft Data Mining written by Barry de Ville and has been published by Digital Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-05 with Computers categories.
This guide teaches data mining from the perspective of IT professionals using Microsoft data management and e-commerce technologies. The book explains major new data mining capabilities in the forthcoming SQL Server 2000, Microsoft Commerce Server, and other products, and details the new Microsoft standard, "OLE DB for Data Mining".
Data Mining And Market Intelligence For Optimal Marketing Returns
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Author : Susan Chiu
language : en
Publisher: Routledge
Release Date : 2008-06-20
Data Mining And Market Intelligence For Optimal Marketing Returns written by Susan Chiu and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-20 with Business & Economics categories.
The authors present a practical and highly informative perspective on the elements that are crucial to the success of a marketing campaign. Unlike books that are either too theoretical to be of practical use to practitioners, or too soft to serve as solid and measurable implementation guidelines, this book focuses on the integration of established quantitative techniques into real life case studies that are immediately relevant to marketing practitioners.
Data Mining For Business Intelligence
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-28
Data Mining For Business Intelligence written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-28 with Mathematics categories.
Praise for the First 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 Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Data Mining With Computational Intelligence
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Author : Lipo Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-08
Data Mining With Computational Intelligence written by Lipo Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-08 with Computers categories.
Finding information hidden in data is as theoretically difficult as it is practically important. With the objective of discovering unknown patterns from data, the methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application areas such as bioinformatics, banking, retail, and many others. Wang and Fu present in detail the state of the art on how to utilize fuzzy neural networks, multilayer perceptron neural networks, radial basis function neural networks, genetic algorithms, and support vector machines in such applications. They focus on three main data mining tasks: data dimensionality reduction, classification, and rule extraction. The book is targeted at researchers in both academia and industry, while graduate students and developers of data mining systems will also profit from the detailed algorithmic descriptions.