A Practical Guide To Data Mining For Business And Industry
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A Practical Guide To Data Mining For Business And Industry
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Author : Andrea Ahlemeyer-Stubbe
language : en
Publisher: John Wiley & Sons
Release Date : 2014-05-12
A Practical Guide To Data Mining For Business And Industry written by Andrea Ahlemeyer-Stubbe 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 2014-05-12 with Mathematics categories.
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
A Practical Guide To Data Mining For Business And Industry
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Author : Andrea Ahlemeyer-Stubbe
language : en
Publisher: John Wiley & Sons
Release Date : 2014-03-31
A Practical Guide To Data Mining For Business And Industry written by Andrea Ahlemeyer-Stubbe 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 2014-03-31 with Mathematics categories.
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
Applied Data Mining
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Author : Paolo Giudici
language : en
Publisher: John Wiley & Sons
Release Date : 2005-09-27
Applied Data Mining written by Paolo Giudici 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 2005-09-27 with Computers categories.
Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing availability of data in the currentinformation society has led to the need for valid tools for itsmodelling and analysis. Data mining and applied statistical methodsare the appropriate tools to extract such knowledge from data.Applications occur in many different fields, including statistics,computer science, machine learning, economics, marketing andfinance. This book is the first to describe applied data mining methodsin a consistent statistical framework, and then show how they canbe applied in practice. All the methods described are eithercomputational, or of a statistical modelling nature. Complexprobabilistic models and mathematical tools are not used, so thebook is accessible to a wide audience of students and industryprofessionals. The second half of the book consists of nine casestudies, taken from the author's own work in industry, thatdemonstrate how the methods described can be applied to realproblems. Provides a solid introduction to applied data mining methods ina consistent statistical framework Includes coverage of classical, multivariate and Bayesianstatistical methodology Includes many recent developments such as web mining,sequential Bayesian analysis and memory based reasoning Each statistical method described is illustrated with real lifeapplications Features a number of detailed case studies based on appliedprojects within industry Incorporates discussion on software used in data mining, withparticular emphasis on SAS Supported by a website featuring data sets, software andadditional material Includes an extensive bibliography and pointers to furtherreading within the text Author has many years experience teaching introductory andmultivariate statistics and data mining, and working on appliedprojects within industry A valuable resource for advanced undergraduate and graduatestudents of applied statistics, data mining, computer science andeconomics, as well as for professionals working in industry onprojects involving large volumes of data - such as in marketing orfinancial risk management.
Java Data Mining Strategy Standard And Practice
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Author : Mark F. Hornick
language : en
Publisher: Elsevier
Release Date : 2010-07-26
Java Data Mining Strategy Standard And Practice written by Mark F. Hornick and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-26 with Computers categories.
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard. - Data mining introduction - an overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems - JDM essentials - concepts, design approach and design issues, with detailed code examples in Java; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects - JDM in practice - the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure; a how-to guide for building applications that use the JDM API - Free, downloadable KJDM source code referenced in the book available here
Making Sense Of Data Set
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Author : Glenn J. Myatt
language : en
Publisher: Wiley
Release Date : 2009-05-26
Making Sense Of Data Set written by Glenn J. Myatt and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-05-26 with Mathematics categories.
Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining by Glenn J. Myatt (978-0-470-07471-8), Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications by Glenn J. Myatt and Wayne P. Johnson (978-0-470-22280-5), and Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations by Glenn J. Myatt and Wayne P. Johnson (978-0-470-53649-0)
Mining Your Own Business
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Author : Jeff Deal
language : en
Publisher:
Release Date : 2016-09-19
Mining Your Own Business written by Jeff Deal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-19 with categories.
Practical guide for organization leaders, top-level executives. Industry experts explain in clear, understandable English. What data mining and predictive analytics are
The Data Industry
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Author : Chunlei Tang
language : en
Publisher: John Wiley & Sons
Release Date : 2016-06-13
The Data Industry written by Chunlei Tang 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 2016-06-13 with Mathematics categories.
Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.
Practical Business Analytics Using Sas
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Author : Shailendra Kadre
language : en
Publisher: Apress
Release Date : 2015-02-07
Practical Business Analytics Using Sas written by Shailendra Kadre and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-02-07 with Computers categories.
Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios. The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations. The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life. Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes: More than 200 examples and exercises, including code and datasets for practice. Relevant examples for all industries. Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course. Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.
Data Mining For Business Applications
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Author : Carlos A. Mota Soares
language : en
Publisher: IOS Press
Release Date : 2010
Data Mining For Business Applications written by Carlos A. Mota Soares and has been published by IOS Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
Data mining is already incorporated into the business processes in sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications.
Data Science For Business
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Author : Foster Provost
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-07-27
Data Science For Business written by Foster Provost and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-27 with Computers categories.
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates