This conceptual introduction to data mining within the context of business and marketing research provides an eclectic approach to the field. Using worked examples and business case studies, the volume answers the four questions: why is data mining important to business and marketing research; how is data mining different from other types of research; what do we learn from data mining; and how do we do data mining? The book explains data mining, traditional methods, data-adaptive methods and applications in business and marketing with business cases and provides lists of tables, figures and exhibits. For Managers and Analysts.

Data and Text Mining: A Business Applications Approach

Business & Money

Miller, Thomas W.

Marketing & Sales

Pearson

Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).

Modeling Techniques in Predictive Analytics: Business Problems and Solutions With R

Education & Reference

Ft Pr

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: <ul>
<li> Advertising and promotion </li> <li> Consumer preference and choice </li> <li> Market baskets and related purchases </li> <li> Economic forecasting </li> <li> Operations management </li> <li> Unstructured text and language </li> <li> Customer sentiment </li> <li> Brand and price </li> <li> Sports team performance </li> <li> And much more </li> </ul>

Modeling Techniques in Predictive Analytics: Business Problems and Solutions with R

Management & Leadership

TO BUILD WINNING TEAMS AND SUCCESSFUL SPORTS BUSINESSES, GUIDE YOUR DECISIONS WITH DATA This up-to-the-minute reference will help you master all three facets of sports analytics – and use it to win! Sports Analytics and Data Science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science. You’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. You’ll master the discipline through realistic sports vignettes and intuitive data visualizations—not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R and Python code, interpreting your results, and more. Every chapter focuses on one key sports analytics application. Miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. Step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business. Whether you’re a team executive, coach, fan, fantasy player, or data scientist, this guide will be a powerful source of competitive advantage… in any sport, by any measure. All data sets, extensive R and Python code, and additional examples available for download at http://www.ftpress.com/miller/ This exceptionally complete and practical guide to sports data science and modeling teaches through realistic examples from sports industry economics, marketing, management, performance measurement, and competitive analysis. Thomas W. Miller, faculty director of Northwestern University’s pioneering Predictive Analytics program, shows how to use advanced measures of individual and team performance to judge the competitive position of both individual athletes and teams, and to make more accurate predictions about their future performance. Miller’s modeling techniques draw on methods from economics, accounting, finance, classical and Bayesian statistics, machine learning, simulation, and mathematical programming. Miller illustrates them through realistic case studies, with fully worked examples in both R and Python. Sports Analytics and Data Science will be an invaluable resource for everyone who wants to seriously investigate and more accurately predict player, team, and sports business performance, including students, teachers, sports analysts, sports fans, trainers, coaches, and team and sports business managers. It will also be valuable to all students of analytics and data science who want to build their skills through familiar and accessible sports applications Gain powerful, actionable insights for: <ul>
<li> Understanding sports markets </li>
<li> Assessing players </li>
<li> Ranking teams </li>
<li> Predicting scores </li>
<li> Making game day decisions </li>
<li> Crafting marketing messages </li>
<li> Promoting brands and products </li>
<li> Growing revenues </li>
<li> Managing finances </li>
<li> Playing what-if games </li>
<li> And much more </li> </ul>

Sports Analytics and Data Science: Winning the Game With Methods and Models

Finance

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University's prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage w

Web and Network Data Science: Modeling Techniques in Predictive Analytics (FT Press Analytics)

Pearson FT Press

Derivatives: Valuation and Risk Management, by David A. Dubofsky and Thomas W. Miller, Jr., enables students to acquire a strong working knowledge and thorough understanding of the rapidly growing field of financial derivatives. Students will learn essential risk management skills, such as how markets in these securities can be used to shift risk away from or toward the user.

Derivatives: Valuation and Risk Management

Dubofsky, David A.

Economics

Oxford University Press

This second edition is an extensive modernization of the bestselling introduction to the subject of adaptive array sensor systems. With the number of applications of adaptive array sensor systems growing each year, this look at the principles and fundamental techniques that are critical to these systems is more important than ever before. Introduction to Adaptive Arrays, 2nd Edition is organized as a tutorial, taking the reader by the hand and leading them through the maze of jargon that often surrounds this highly technical subject. It is easy to read and easy to follow, as fundamental concepts are introduced with examples before more current developments and techniques are introduced. Problems at the end of each chapter serve both instructors and professional readers by illustrating and extending the material presented in the text. Both students and practicing engineers will easily gain familiarity with the modern contribution that adaptive arrays have to offer practical signal reception systems.

Introduction to Adaptive Arrays

Engineering & Transportation

Monzingo, Robert A.

Transportation

John Wiley&Sons, Inc.

This book focuses on the problem of child abuse and neglect from the victimization to the courtroom, from the impact at the time of the abusive incident to adulthood, from methods helpful in reducing stress for children called upon to testify in court to strategies helpful for the clinician in handling courtroom testimony more constructively. Characteristics seen in these dysfunctional families are reviewed along with the behavioral and medical indicators of abuse. Issues discussed include parental denial, child placement, as well as the adult non-survivor of childhood victimization. Special emphasis is placed on the clinical and medical assessment of the child victim. For example, a detailed discussion on the use of drawings, story telling, fables, and anatomical dolls gives a step by step understanding on using these techniques. In addition, a chapter is devoted to the medical assessment. Finally, the chapter on courtroom testimony focuses on the child as a witness, the sources of stress and methods to reduce stress for the child, and helpful strategies for the expert witness including the writing of the court report. This book is designed for the mental health practitioner, pediatrician, family physician, psychiatrist, police, and the child protective service specialist.

Clinical Handbook of Child Abuse and Neglect

Politics & Social Sciences

Veltkamp, Lane J.

Sociology

Intl Universities Pr Inc

Miller, Thomas W., Veltkamp, Lane J.

Clinical Handbook of Adult Exploitation and Abuse

Health, Fitness & Dieting

Walker, Lenore E.

Mental Health

Looks at the state of the marketing research industry through the eyes of some of its key contributors with discussion of trends in technology, customer relations and expectations, and economics in the industry. This book includes the results of three opinion surveys and an analysis of market share information and economic data from the industry.

Marketing Research Information Services: 2003 Industry Report

James, Dana H.

Research Publishers, LLC

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Books by Miller, Thomas W.