Modeling Techniques in Predictive Analytics with Python and R

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 013389214X
Format: PDF, Kindle
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Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each 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. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business 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 code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Marketing Data Science

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133887340
Format: PDF, Docs
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Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

Web and Network Data Science

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133887642
Format: PDF
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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 with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Modeling Techniques in Predictive Analytics

Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133886190
Format: PDF, Mobi
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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: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Sports Analytics and Data Science

Author: Thomas Miller
Publisher: Pearson FT Press
ISBN: 9780133886436
Format: PDF
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This is a complete, practical guide to sports data science and modeling, with 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 Python and R. Sports Analytics and Data Science will be an invaluable resource for everyone who wants to seriously investigate and more accurately predict athletic performance, including students, teachers, sports analysts, sports fans, physiologists, coaches, and managers of sports teams. It will also be valuable to all students of analytics who want to build their skills through familiar and accessible sports applications.

Handbook of Research on Big Data Storage and Visualization Techniques

Author: Segall, Richard S.
Publisher: IGI Global
ISBN: 1522531432
Format: PDF
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The digital age has presented an exponential growth in the amount of data available to individuals looking to draw conclusions based on given or collected information across industries. Challenges associated with the analysis, security, sharing, storage, and visualization of large and complex data sets continue to plague data scientists and analysts alike as traditional data processing applications struggle to adequately manage big data. The Handbook of Research on Big Data Storage and Visualization Techniques is a critical scholarly resource that explores big data analytics and technologies and their role in developing a broad understanding of issues pertaining to the use of big data in multidisciplinary fields. Featuring coverage on a broad range of topics, such as architecture patterns, programing systems, and computational energy, this publication is geared towards professionals, researchers, and students seeking current research and application topics on the subject.

Sports Performance Measurement and Analytics

Author: Lorena Martin
Publisher: FT Press
ISBN: 0134193881
Format: PDF
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A PRACTICAL, REAL-WORLD GUIDE TO ANALYTICS FOR THE 5 MAJOR SPORTS: FOOTBALL, BASKETBALL, BASEBALL, SOCCER, AND TENNIS GAIN A COMPETITIVE EDGE! This is the first real-world guide to building and using analytical models for measuring and assessing performance in the five major sports: football, basketball, baseball, soccer, and tennis. Unlike books that focus strictly on theory, this book brings together sports measurement and statistical analyses, demonstrating how to examine differences across sports as well as between player positions. This book will provide you with the tools for cutting-edge approaches you can extend to the sport of your choice. Expert Northwestern University data scientist, UC San Diego researcher, and competitive athlete, Lorena Martin shows how to use measures and apply statistical models to evaluate players, reduce injuries, and improve sports performance. You’ll learn how to leverage a deep understanding of each sport’s principles, rules, attributes, measures, and performance outcomes. Sports Performance Measurement and Analytics will be an indispensable resource for anyone who wants to bring analytical rigor to athletic competition: students, professors, analysts, fans, physiologists, coaches, managers, and sports executives alike. All data sets, extensive code, and additional examples are available for download at http://www.ftpress.com/martin/ What are the qualities a person must have to become a world-class athlete? This question and many more can be answered through research, measurement, statistics, and analytics. This book gives athletes, trainers, coaches, and managers a better understanding of measurement and analytics as they relate to sports performance. To develop accurate measures, we need to know what we want to measure and why. There is great power in accurate measures and statistics. Research findings can show us how to prevent injuries, evaluate strengths and weaknesses, improve team cohesion, and optimize sports performance. This book serves many readers. People involved with sports will gain an appreciation for performance measures and analytics. People involved with analytics will gain new insights into quantified values representing physical, physiological, and psychological components of sports performance. And students eager to learn about sports analytics will have a practical introduction to the field. This is a thorough introduction to performance measurement and analytics for five of the world’s leading sports. The only book of its kind, it offers a complete overview of the most important concepts, rules, measurements, and statistics for each sport, while demonstrating applications of real-world analytics. You’ll find practical, state-of-the-art guidance on predicting future outcomes, evaluating an athlete’s market value, and more.

Data Visualization and Text Principles and Practices

Author: Thomas Miller
Publisher: Pearson FT Press
ISBN: 9780134308913
Format: PDF
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Data visualization is increasingly central to predictive analytics and data science. The book focuses on all three application areas of data visualization: exploratory data analysis, model diagnostics, and presentation graphics. Built on the same structure and approach as other books in Thomas W. Miller's popular Modeling Techniques series, it has been carefully designed to serve multiple audiences: business managers, analysts, programmers, and students. Miller begins with core principles, revealing why some data visualizations effectively present information and others don't. He reviews the science of human perception and cognition, proven principles of graphic design, and the growing role of visualization throughout data science -- including examples such as the visualization of time, networks, and maps. Drawing on his pioneering experience teaching data visualization, Miller begins each chapter by stating a real business problem. He explains why the problem is important, describes a relevant dataset, and guides you through solving it with leading open-source tools such as R, Python, D3, and Gephi. (All R and Python code is set apart, so managers or analysts who aren't interested in programming can easily skip it.) Like other books in this series, Data Visualization Principles and Practices draws realistic examples from key application areas, including marketing, finance, sports analytics, web and network data science, text analytics, and social network analysis. Examples include cross-sectional data, time series, network, and spatial data. Readers will discover advanced methods for constructing static and interactive graphics, building web-browser-based presentations, and even creating "information art.""