Data Mining for Business Analytics

Concepts, Techniques, and Applications with JMP Pro

Author: Galit Shmueli,Peter C. Bruce,Mia L. Stephens,Nitin R. Patel

Publisher: John Wiley & Sons

ISBN: 1118877527

Category: Mathematics

Page: 464

View: 5689

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Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.

Data Mining for Business Analytics

Concepts, Techniques, and Applications in R

Author: Galit Shmueli,Peter C. Bruce,Inbal Yahav,Nitin R. Patel,Kenneth C. Lichtendahl, Jr.

Publisher: John Wiley & Sons

ISBN: 1118879333

Category: Mathematics

Page: 574

View: 892

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Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students • More than a dozen case studies demonstrating applications for the data mining techniques described • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 publications including books. Peter C. Bruce is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly). Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. Kenneth C. Lichtendahl, Jr., PhD, is Associate Professor at the University of Virginia. He is the Eleanor F. and Phillip G. Rust Professor of Business Administration and teaches MBA courses in decision analysis, data analysis and optimization, and managerial quantitative analysis. He also teaches executive education courses in strategic analysis and decision-making, and managing the corporate aviation function.

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

Author: Galit Shmueli,Peter C. Bruce,Nitin R. Patel

Publisher: John Wiley & Sons

ISBN: 1118729242

Category: Mathematics

Page: 552

View: 9373

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Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.

Data Mining for Business Analytics

Concepts, Techniques, and Applications with JMP Pro

Author: Galit Shmueli,Peter C. Bruce,Mia L. Stephens,Nitin R. Patel

Publisher: John Wiley & Sons

ISBN: 1118956621

Category: Mathematics

Page: 464

View: 4068

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Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.

Fundamentals of Predictive Analytics with JMP, Second Edition

Author: Ron Klimberg,B. D. McCullough

Publisher: SAS Institute

ISBN: 1629608017

Category: Computers

Page: 406

View: 8437

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Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. This book is part of the SAS Press program.

Building Better Models with JMP Pro

Author: Jim Grayson,Sam Gardner,Mia Stephens

Publisher: SAS Institute

ISBN: 1629599565

Category: Mathematics

Page: 352

View: 7641

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This book provides an example-based introduction to business analytics, with a proven process that guides you in the application of modeling tools and concepts. It gives you the "what, why, and how" of using JMP Pro for building and applying analytic models. It will greatly benefit faculty members who teach any of the following subjects at the lower to upper graduate level: predictive modeling, data mining, and business analytics. Novice to advanced users in business statistics, business analytics, and predictive modeling will find that it provides a peek inside the black box of algorithms and the methods used. Topics include: regression, logistic regression, classification and regression trees, neural networks, model cross-validation, model comparison and selection, and data reduction techniques. --

Introductory Statistics and Analytics

A Resampling Perspective

Author: Peter C. Bruce

Publisher: John Wiley & Sons

ISBN: 1118881338

Category: Mathematics

Page: 312

View: 1707

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Concise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas. The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes: Over 300 “Try It Yourself” exercises and intermittent practice questions, which challenge readers at multiple levels to investigate and explore key statistical concepts Numerous interactive links designed to provide solutions to exercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing field of data science Multiple discussions of various software systems, such as Microsoft Office Excel®, StatCrunch, and R, to develop and analyze data Areas of concern and/or contrasting points-of-view indicated through the use of “Caution” icons Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.

Customer Relationship Management

Concept, Strategy, and Tools

Author: V. Kumar,Werner Reinartz

Publisher: Springer Science & Business Media

ISBN: 3642201091

Category: Business & Economics

Page: 379

View: 670

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Customer relationship management (CRM) as a strategy and as a technology has gone through an amazing evolutionary journey. The initial technological approach was followed by many disappointing initiatives only to see the maturing of the underlying concepts and applications in recent years. Today, CRM represents a strategy, a set of tactics, and a technology that have become indispensible in the modern economy. This book presents an extensive treatment of the strategic and tactical aspects of customer relationship management as we know it today. It stresses developing an understanding of economic customer value as the guiding concept for marketing decisions. The goal of the book is to serve as a comprehensive and up-to-date learning companion for advanced undergraduate students, master's degree students, and executives who want a detailed and conceptually sound insight into the field of CRM.

Testing 1-2-3

Experimental Design with Applications in Marketing and Service Operations

Author: Johannes Ledolter,Arthur J. Swersey

Publisher: Stanford University Press

ISBN: 9780804756129

Category: Business & Economics

Page: 300

View: 8834

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This book gives students, practitioners, and managers a set of practical and valuable tools for designing and analyzing experiments, emphasizing applications in marketing and service operations such as website design, direct mail campaigns, and in-store tests.

Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications

Author: Gary Miner

Publisher: Academic Press

ISBN: 012386979X

Category: Mathematics

Page: 1053

View: 2820

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The world contains an unimaginably vast amount of digital information which is getting ever vaster ever more rapidly. This makes it possible to do many things that previously could not be done: spot business trends, prevent diseases, combat crime and so on. Managed well, the textual data can be used to unlock new sources of economic value, provide fresh insights into science and hold governments to account. As the Internet expands and our natural capacity to process the unstructured text that it contains diminishes, the value of text mining for information retrieval and search will increase dramatically. This comprehensive professional reference brings together all the information, tools and methods a professional will need to efficiently use text mining applications and statistical analysis. The Handbook of Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications presents a comprehensive how- to reference that shows the user how to conduct text mining and statistically analyze results. In addition to providing an in-depth examination of core text mining and link detection tools, methods and operations, the book examines advanced preprocessing techniques, knowledge representation considerations, and visualization approaches. Finally, the book explores current real-world, mission-critical applications of text mining and link detection using real world example tutorials in such varied fields as corporate, finance, business intelligence, genomics research, and counterterrorism activities. -Extensive case studies, most in a tutorial format, allow the reader to 'click through' the example using a software program, thus learning to conduct text mining analyses in the most rapid manner of learning possible -Numerous examples, tutorials, power points and datasets available via companion website on Elsevierdirect.com -Glossary of text mining terms provided in the appendix

Advertising Creative

Strategy, Copy, and Design

Author: Tom Altstiel,Jean Grow

Publisher: SAGE Publications

ISBN: 1506315402

Category: Language Arts & Disciplines

Page: 488

View: 6451

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Advertising Creative is the first “postdigital” creative strategy and copywriting textbook in which digital technology is woven throughout every chapter. The book gets right to the point of advertising by stressing key principles and practical information students and working professionals can use to communicate effectively in this postdigital age. Drawing on personal experience as award-winning experts in creative advertising, Tom Altstiel and Jean Grow offer real-world insights on cutting-edge topics, including global, social media, business-to-business, in-house, and small agency advertising. In this Fourth Edition, Altstiel and Grow take a deeper dive into the exploration of digital technology and its implications for the industry, as they expose the pervasive changes experienced across the global advertising landscape. Their most important revelation of all is the identification of the three qualities that will define the future leaders of this industry: Be a risk taker. Understand technology. Live for ideas.

Problem Solving for New Engineers

What Every Engineering Manager Wants You to Know

Author: Melisa Buie

Publisher: CRC Press

ISBN: 1351996436

Category: Business & Economics

Page: 286

View: 1498

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This book brings a fresh new approach to practical problem solving in engineering. It covers the critical concepts and ideas that engineers must understand to solve engineering problems. When engineers graduate, they enter the work force with only one part of what’s needed to effectively solve problems -- Problem solving requires not just subject matter expertise but an additional knowledge of strategy. With the combination of both knowledge of subject matter and knowledge of strategy, engineering problems can be attacked efficiently. The book focuses on developing a strategy for minimizing, eliminating, and finally controlling variation such that the intentional variation is truly is representative of the variables of interest.

Visual Six Sigma

Making Data Analysis Lean

Author: Ian Cox,Marie A. Gaudard,Philip J. Ramsey,Mia L. Stephens,Leo Wright

Publisher: John Wiley & Sons

ISBN: 1118905687

Category: Business & Economics

Page: 576

View: 7309

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Visual Six Sigma Second Edition will include a new chapter on data quality and its preparation for analysis, which is perhaps the greatest challenge to analysts. It consists of six case studies that will be updated and streamlined to include new features available in JMP 11 and JMP 11 Pro. All screen captures will reflect the new JMP interface and to improve their quality and presentation.

The R Book

Author: Michael J. Crawley

Publisher: John Wiley & Sons

ISBN: 1118448960

Category: Mathematics

Page: 1080

View: 6437

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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)

JMP Start Statistics

A Guide to Statistics and Data Analysis Using JMP, Sixth Edition

Author: John Sall,Mia L. Stephens,Ann Lehman,Sheila Loring

Publisher: SAS Institute

ISBN: 1629608769

Category: Computers

Page: 660

View: 3433

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This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises. Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including: The redesigned Formula Editor. New and improved ways to create formulas in JMP directly from the data table or dialogs. Interface updates, including improved menu layout. Updates and enhancements in many analysis platforms. New ways to get data into JMP and to save and share JMP results. Many new features that make it easier to use JMP.

Business Forecasting: Pearson New International Edition

Author: John E. Hanke,Dean Wichern

Publisher: Pearson Higher Ed

ISBN: 1292036184

Category: Business & Economics

Page: 512

View: 5375

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For undergraduate and graduate courses in Business Forecasting. Written in a simple, straightforward style, Business Forecasting 9/e presents basic statistical techniques using practical business examples to teach students how to predict long-term forecasts.

Finance for Executives: Managing for Value Creation

Author: Gabriel Hawawini,Claude Viallet

Publisher: Cengage Learning

ISBN: 0538751347

Category: Business & Economics

Page: 672

View: 891

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Ideal for both aspiring managers and experienced executives, the Fourth Edition of FINANCE FOR EXECUTIVES: MANAGING FOR VALUE CREATION illustrates the importance of financial information in maximizing firm value. Respected authors Gabriel Hawawini and Claude Viallet draw on their wealth of business and teaching experience to provide a concise, analytically sound introduction to financial management that is neither too simplistic nor too theoretical. In fact, the text masterfully balances a thorough exploration of modern finance principles with a strong practical focus on real-world applications and rigorous analysis, even while avoiding complicated formulas with little value for decision-making. Perfect for executive education courses, M.B.A. programs, or any class with an emphasis on translating theory into practice or learning through real-world cases, FINANCE FOR EXECUTIVES employs a strong problem-scenario approach to present key concepts within the context of realistic financial management issues that executives commonly face. In addition, a series of integrated case studies analyzes the same set of companies throughout the text to explore concepts in greater depth and reinforce learning. The new Fourth Edition maintains the text’s highly reader-friendly structure and presentation. Because each chapter is self-contained, instructors can enjoy great flexibility in structuring their courses, while students will find the text an invaluable reference and resource to use throughout their careers. In addition, the current edition features extensive updates incorporating the most recent financial data and latest references, as well as a new chapter devoted to managing corporate risk, an essential topic for success in today’s high-stakes business environment. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

The Data Warehouse Toolkit

The Definitive Guide to Dimensional Modeling

Author: Ralph Kimball,Margy Ross

Publisher: John Wiley & Sons

ISBN: 1118732286

Category: Computers

Page: 600

View: 5897

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Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence! The first edition of Ralph Kimball's The Data Warehouse Toolkit introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more. Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence Begins with fundamental design recommendations and progresses through increasingly complex scenarios Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more Design dimensional databases that are easy to understand and provide fast query response with The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition.

Confident Data Skills

Master the Fundamentals of Working with Data and Supercharge Your Career

Author: Kirill Eremenko

Publisher: Kogan Page Publishers

ISBN: 0749481552

Category: Computers

Page: 272

View: 674

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Data science is the most exciting skill you can master. Data has dramatically changed how our world works. From entertainment to politics, from technology to advertising and from science to the business world, data is integral and its only limit is our imagination. If you want to have a vibrant and valuable professional life, being skilled with data is the key to a cutting-edge career. Learning how to work with data may seem intimidating or difficult but with Confident Data Skills you will be able to master the fundamentals and supercharge your professional abilities. This essential book covers data mining, preparing data, analysing data, communicating data, financial modelling, visualizing insights and presenting data through film making and dynamic simulations. In-depth international case studies from a wide range of organizations, including Netflix, LinkedIn, Goodreads, Deep Blue, Alpha Go and Mike's Hard Lemonade Co. show successful data techniques in practice and inspire you to turn knowledge into innovation. Confident Data Skills also provides insightful guidance on how you can use data skills to enhance your employability and improve how your industry or company works through your data skills. Expert author and instructor, Kirill Eremenko, is committed to making the complex simple and inspiring you to have the confidence to develop an understanding, adeptness and love of data.

Data Mining for Business Intelligence

Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner

Author: Galit Shmueli,Nitin R. Patel,Peter C. Bruce

Publisher: John Wiley and Sons

ISBN: 1118126041

Category: Mathematics

Page: 428

View: 3080

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Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." —Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." —computingreviews.com 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: 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 A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topics 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.