Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

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: <ul>
<li>Provides both a theoretical and practical understanding of the key methods of classification, prediction, reduction, exploration, and affinity analysis</li> <li>Features a business decision-making context for these key methods</li> <li>Illustrates the application and interpretation of these methods using real business cases and data</li> </ul>
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.

Science & Math

Bruce, Peter C.

Shmueli, Galit

Patel, Nitin R.

Mathematics

Wiley-Interscience

This groundbreaking book introduces the application of statistical methodologies to e-Commerce data With the expanding presence of technology in today's economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity. This collection gathers over thirty researchers and practitioners from the fields of statistics, computer science, information systems, and marketing to discuss the growing use of statistical methods in e-Commerce research. From privacy protection to economic impact, the book first identifies the many obstacles that are encountered while collecting, cleaning, exploring, and analyzing e-Commerce data. Solutions to these problems are then suggested using established and newly developed statistical and data mining methods. Finally, a look into the future of this evolving area of study is provided through an in-depth discussion of the emerging methods for conducting e-Commerce research. Statistical Methods in e-Commerce Research successfully bridges the gap between statistics and e-Commerce, introducing a statistical approach to solving challenges that arise in the context of online transactions, while also introducing a wide range of e-Commerce applications and problems where novel statistical methodology is warranted. It is an ideal text for courses on e-Commerce at the upper-undergraduate and graduate levels and also serves as a valuable reference for researchers and analysts across a wide array of subject areas, including economics, marketing, and information systems who would like to gain a deeper understanding of the use of statistics in their work.

Statistical Methods in e-Commerce Research

Jank, Wolfgang

This set contains: 9780470084854 Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel(R) with XL Miner(TM) by Galit Shmueli, Nitin R. Patel, Peter C. Bruce and 9780470074718 Making Sense of Data by Glenn J. Myatt

Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel With Xlminer + Making Sense of Data Set

Computers & Technology

Myatt, Glenn J.

Software

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: <ul>
<li>Data collection methods for online auctions and related issues that arise in drawing data samples from a Web site</li> <li>Models for bidder and bid arrivals, treating the different approaches for exploring bidder-seller networks</li> <li>Data exploration, such as integration of time series and cross-sectional information; curve clustering; semi-continuous data structures; and data hierarchies</li> <li>The use of functional regression as well as functional differential equation models, spatial models, and stochastic models for capturing relationships in auction data</li> <li>Specialized methods and models for forecasting auction prices and their applications in automated bidding decision rule systems</li> </ul>
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

Modeling Online Auctions

Business & Money

Industries

wiley

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: 
<ul>
<li>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</li>
<li>A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice</li>
<li>Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods</li>
<li>Summaries at the start of each chapter that supply an outline of key topics</li>
</ul>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 for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel With XLMiner

Management & Leadership

John Wiley&Sons, Inc.

PRACTICAL TIME SERIES FORECASTING WITH R is a hands-on introduction to quantitative forecasting of time series. Quantitative forecasting is an important component of decision making in a wide range of areas and across many business functions including economic forecasting, workload projections, sales forecasts, and transportation demand. Forecasting is widely used also outside of business, such as in demography and climatology. The book introduces readers to the most popular statistical models and data mining algorithms used in practice. It covers issues relating to different steps of the forecasting process, from goal definition through data collection, visualization, pre-processing, modeling, performance evaluation to implementation and communication. PRACTICAL TIME SERIES FORECASTING WITH R is suitable for courses on forecasting at the upper-undergraduate and graduate levels. It offers clear explanations, examples, end-of-chapter problems and cases. Methods are illustrated using the free and open-source software R.

Practical Time Series Forecasting with R: A Hands-On Guide

Lichtendahl, Jr Kenneth C.

Axelrod Schnall Publishers

Practical Time Series Forecasting: A Hands-On Guide, Third Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the Excel® add-in XLMiner® to develop effective forecasting solutions that extract business value from time-series data. Featuring improved organization and new material, the Third Edition also includes: <ul>
<li>Popular forecasting methods including smoothing algorithms, regression models, and neural networks</li>
<li>A practical approach to evaluating the performance of forecasting solutions</li>
<li>A business-analytics exposition focused on linking time-series forecasting to business goals</li>
<li>Guided cases for integrating the acquired knowledge using real data</li>
<li>End-of-chapter problems to facilitate active learning</li>
<li>A companion site with data sets, learning resources, and instructor materials (solutions to exercises, case studies, and slides)</li>
<li>Globally-available textbook, available in both softcover and Kindle formats </li>
</ul>
Practical Time Series Forecasting: A Hands-On Guide, Third Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management.For more information, visit forecastingbook.com

Practical Time Series Forecasting: A Hands-On Guide [3rd Edition]

New to the second edition: A section on Acceptance-on-Zero plans, additional screenshots from the newly-designed SQCOnline.com with several new calculators, and improved book design for enhanced readability.Practical Acceptance Sampling is a hands-on introduction to the inspection of products and services for quality assurance using statistically-based sampling plans.  In today’s era of global supply chains, the path from raw materials to final product often takes place over multiple companies and across multiple continents. Acceptance sampling is key in the 21st century environment. Acceptance sampling plans provide criteria and decision rules for determining whether to accept or reject a batch based on a sample. They are therefore widely used by manufacturers, suppliers, contractors and subcontractors, and service providers in a wide range of industries.  The book introduces readers to the most popular sampling plans, including Military Standards and civilian ISO and ANSI/ASQC/BS standards. It covers the design, choice and performance evaluation of different types of plans, including single- and double-stage plans, rectifying and non-rectifying plans, plans for pass/fail and continuous measurements, continuous sampling plans, and more.  Practical Acceptance Sampling is suitable for courses on quality control and for quality practitioners with basic knowledge of statistics. It offers clear explanations, examples, end-of-chapter problems, and illustrations of state-of-the-art online resources. Methods are illustrated using Microsoft Excel, online calculators, and SQCOnline.com. However, any statistical software can be used with the book.  A companion website to the book is available at www.SamplingBook.com

Practical Acceptance Sampling: A Hands-On Guide [2nd Edition] (Practical Analytics)

Projects are investments of resources for achieving a particular objective or set of objectives. Resources include time, money, manpower, and sometimes lives. Objectives include financial gain, social and health benefits, national goals, educational and scientific achievements, and reduction of suffering, among many others. Projects are undertaken by large and small organizations, by governments, non-profit organizations, private businesses, and by individuals. Determining whether to execute a project, or which project to execute among a set of possibilities is often a challenge with high stakes. Assessing the potential outcomes of a project can therefore be detrimental, leading to the importance of making informative decisions.Practical Risk Analysis for Project Planning is a hands-on introduction to integrating numerical data and domain knowledge into popular spreadsheet software such as Microsoft Excel or Google Spreadsheets, to arrive at informed project-planning decisions. The focus of the book is on formalizing domain expertise into numerical data, providing tools for assessing potential project performance, and evaluating performance under realistic uncertainty.The book introduces basic principles for assessing potential project performance and risk under different scenarios, by addressing uncertainty that arises at different levels. It describes measures of expected performance and risk, presents approaches such as scenario building and Monte Carlo simulation for addressing uncertainty, and introduces methods for comparing competing projects and reducing risk via project portfolios.No special software is required except Excel or another spreadsheet software. While the book assumes no knowledge of statistics, operations research, or management science, it does rely on basic familiarity with Excel. Chapter exercises and examples of real projects are aimed at hands-on learning.For more information visit RiskAnalysisBook.com

Practical Risk Analysis for Project Planning: A Hands-On Guide using Excel (Practical Analytics)

Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data. Featuring improved organization and new material, the Second Edition also includes:
<ul>
<li>Popular forecasting methods including smoothing algorithms, regression models, and neural networks</li>
<li>A practical approach to evaluating the performance of forecasting solutions</li>
<li>A business-analytics exposition focused on linking time-series forecasting to business goals</li>
<li>Guided cases for integrating the acquired knowledge using real data</li>
<li> End-of-chapter problems to facilitate active learning</li>
<li>A companion site with data sets, R code, learning resources, and instructor materials (solutions to exercises, case studies)Globally-available textbook, available in both softcover and Kindle formats </li>
</ul>
Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition is the perfect textbook for upper-undergraduate, graduate and MBA-level courses as well as professional programs in data science and business analytics. The book is also designed for practitioners in the fields of operations research, supply chain management, marketing, economics, finance and management.For more information, visit forecastingbook.com

Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics)

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