Daniel T. Larose's Books

Discovering Knowledge in Data

An Introduction to Data Mining

By: Daniel T. Larose,Chantal D. Larose

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Discovering Knowledge in Data

An Introduction to Data Mining

By: Daniel T. Larose,Chantal D. Larose

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

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Page Count

336

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118873572

ISBN 13

9781118873571

Data Science Using Python and R

By: Chantal D. Larose,Daniel T. Larose

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Data Science Using Python and R

By: Chantal D. Larose,Daniel T. Larose

Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.

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256

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119526841

ISBN 13

9781119526841

Data Mining and Predictive Analytics

By: Daniel T. Larose

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Data Mining and Predictive Analytics

By: Daniel T. Larose

Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

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Page Count

827

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118868676

ISBN 13

9781118868676

Data Mining the Web

Uncovering Patterns in Web Content, Structure, and Usage

By: Zdravko Markov,Daniel T. Larose

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Data Mining the Web

Uncovering Patterns in Web Content, Structure, and Usage

By: Zdravko Markov,Daniel T. Larose

This book introduces the reader to methods of data mining on the web, including uncovering patterns in web content (classification, clustering, language processing), structure (graphs, hubs, metrics), and usage (modeling, sequence analysis, performance).

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Page Count

236

Publisher

John Wiley & Sons

Published Date

ISBN 10

0470108088

ISBN 13

9780470108086

Data Mining Methods and Models

By: Daniel T. Larose

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Data Mining Methods and Models

By: Daniel T. Larose

Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.

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Page Count

340

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471756474

ISBN 13

9780471756477

Discovering Statistics Brief Version Student Solutions Manual

By: Daniel T. Larose

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Discovering Statistics Brief Version Student Solutions Manual

By: Daniel T. Larose

The Student Solutions Manual offers detailed solutions for key exercises from each section of Discovering Statistics Brief Version.

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Page Count

247

Publisher

Macmillan

Published Date

ISBN 10

1429245115

ISBN 13

9781429245111

Student's Solutions Manual for Discovering Statistics

By: Daniel T. Larose

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Student's Solutions Manual for Discovering Statistics

By: Daniel T. Larose

The Student Solutions Manual offers detailed solutions for key exercises from each section of Discovering Statistics.

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340

Publisher

Macmillan

Published Date

ISBN 10

1429227532

ISBN 13

9781429227537

Data Mining Methods and Models

By: Daniel T. Larose

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Data Mining Methods and Models

By: Daniel T. Larose

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340

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John Wiley & Sons

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ISBN 10

0471756474

ISBN 13

9780471756477

Discovering Statistics Brief Version

By: Daniel T. Larose

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Discovering Statistics Brief Version

By: Daniel T. Larose

Based on Daniel Larose’s more comprehensive Discovering Statistics, this new text provides a briefer, less expensive option for the traditional computational introduction to statistics. It is an ideal text for instructors who want their students to understand computational methods, while also developing statistical reasoning and critical thinking skills.

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Page Count

681

Publisher

Macmillan Higher Education

Published Date

ISBN 10

1429293497

ISBN 13

9781429293495

Discovering the Fundamentals of Statistics

By: Daniel T. Larose

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Discovering the Fundamentals of Statistics

By: Daniel T. Larose

Discovering the Fundamentals of Statistics by Dan Larose is the ideal brief introductory statistics text that balances the teaching of computational skills with conceptual understanding. Written in a concise, accessible style, Discovering the Fundamentals of Statistics helps students develop the quantitative and analytical tools needed to understand statistics in today’s data-saturated world. Dan Larose presents statistical concepts the way instructors teach and the way students learn.

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Page Count

718

Publisher

Macmillan Higher Education

Published Date

ISBN 10

1464143129

ISBN 13

9781464143120

Discovering Statistics

By: Daniel T. Larose

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Discovering Statistics

By: Daniel T. Larose

Discovering Statistics balances computational methods and data interpretation. By emphasizing the relevance of statistics in today’s world, Larose helps students think critically about statistics and develop their own statistical sense. The text offers high interest and relevant examples and exercises with a large variety of contemporary applications. This popular text includes a number of features to build critical thinking skills and emphasize active learning (an important recommendation in the GAISE guidelines). Student motivation remains the guiding principle of the Second Edition. It provides the topic coverage and innovative pedagogy that support teaching and learning objectives while capturing students’ attention through his friendly, conversational writing style.

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869

Publisher

Macmillan Higher Education

Published Date

ISBN 10

1464118167

ISBN 13

9781464118166

Discovering Statistics

By: Daniel T. Larose

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Discovering Statistics

By: Daniel T. Larose

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869

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Macmillan Higher Education

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ISBN 10

1464118167

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9781464118166

Discovering Knowledge in Data

An Introduction to Data Mining

By: Daniel T. Larose

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Discovering Knowledge in Data

An Introduction to Data Mining

By: Daniel T. Larose

Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

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Page Count

240

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471687537

ISBN 13

9780471687535

Data Mining and Predictive Analytics

By: Daniel T. Larose

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Data Mining and Predictive Analytics

By: Daniel T. Larose

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Page Count

826

Publisher

John Wiley & Sons

Published Date

ISBN 10

1118868706

ISBN 13

9781118868706

Data Science Using Python and R

By: Chantal D. Larose,Daniel T. Larose

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Data Science Using Python and R

By: Chantal D. Larose,Daniel T. Larose

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Solve jigsaw puzzle

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256

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119526841

ISBN 13

9781119526841

Book's by Daniel T. Larose

Discovering Knowledge in Data

Discovering Knowledge in Data

Data Science Using Python and R

Data Science Using Python and R

Data Mining and Predictive Analytics

Data Mining and Predictive Analytics

Data Mining the Web

Data Mining the Web

Data Mining Methods and Models

Data Mining Methods and Models

Discovering Statistics Brief Version Student Solutions Manual

Discovering Statistics Brief Version Student Solutions Manual

Student's Solutions Manual for Discovering Statistics

Student's Solutions Manual for Discovering Statistics

Data Mining Methods and Models

Data Mining Methods and Models

Discovering Statistics Brief Version

Discovering Statistics Brief Version

Discovering the Fundamentals of Statistics

Discovering the Fundamentals of Statistics

Discovering Statistics

Discovering Statistics

Discovering Statistics

Discovering Statistics

Discovering Knowledge in Data

Discovering Knowledge in Data

Data Mining and Predictive Analytics

Data Mining and Predictive Analytics

Data Science Using Python and R

Data Science Using Python and R

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