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| cornell university business analytics: Encyclopedia of Business Analytics and Optimization Wang, John, 2014-02-28 As the age of Big Data emerges, it becomes necessary to take the five dimensions of Big Data- volume, variety, velocity, volatility, and veracity- and focus these dimensions towards one critical emphasis - value. The Encyclopedia of Business Analytics and Optimization confronts the challenges of information retrieval in the age of Big Data by exploring recent advances in the areas of knowledge management, data visualization, interdisciplinary communication, and others. Through its critical approach and practical application, this book will be a must-have reference for any professional, leader, analyst, or manager interested in making the most of the knowledge resources at their disposal. |
| cornell university business analytics: Business Analytics, Volume II Amar Sahay, 2019-11-08 This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes. It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together. The first three chapters provide an introduction to BA, importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics. |
| cornell university business analytics: Business Analytics Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, 2020-03-10 Present the full range of analytics -- from descriptive and predictive to prescriptive analytics -- with Camm/Cochran/Fry/Ohlmann's market-leading BUSINESS ANALYTICS, 4E. Clear, step-by-step instructions teach students how to use Excel, Tableau, R and JMP Pro to solve more advanced analytics concepts. As instructor, you have the flexibility to choose your preferred software for teaching concepts. Extensive solutions to problems and cases save grading time, while providing students with critical practice. This edition covers topics beyond the traditional quantitative concepts, such as data visualization and data mining, which are increasingly important in today's analytical problem solving. In addition, MindTap and WebAssign customizable digital course solutions offer an interactive eBook, auto-graded exercises from the printed book, algorithmic practice problems with solutions and Exploring Analytics visualizations to strengthen students' understanding of course concepts. |
| cornell university business analytics: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-09-26 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. |
| cornell university business analytics: Predictive Business Analytics Lawrence Maisel, Gary Cokins, 2013-10-07 Discover the breakthrough tool your company can use to make winning decisions This forward-thinking book addresses the emergence of predictive business analytics, how it can help redefine the way your organization operates, and many of the misconceptions that impede the adoption of this new management capability. Filled with case examples, Predictive Business Analytics defines ways in which specific industries have applied these techniques and tools and how predictive business analytics can complement other financial applications such as budgeting, forecasting, and performance reporting. Examines how predictive business analytics can help your organization understand its various drivers of performance, their relationship to future outcomes, and improve managerial decision-making Looks at how to develop new insights and understand business performance based on extensive use of data, statistical and quantitative analysis, and explanatory and predictive modeling Written for senior financial professionals, as well as general and divisional senior management Visionary and effective, Predictive Business Analytics reveals how you can use your business's skills, technologies, tools, and processes for continuous analysis of past business performance to gain forward-looking insight and drive business decisions and actions. |
| cornell university business analytics: Sales Growth McKinsey & Company Inc., Thomas Baumgartner, Homayoun Hatami, Maria Valdivieso de Uster, 2016-04-08 The challenges facing today's sales executives and their organizations continue to grow, but so do the expectations that they will find ways to overcome them and drive consistent sales growth. There are no simple solutions to this situation, but in this thoroughly updated Second Edition of Sales Growth, experts from McKinsey & Company build on their practical blueprint for achieving this goal and explore what world-class sales executives are doing right now to find growth and capture it—as well as how they are creating the capabilities to keep growing in the future. Based on discussions with more than 200 of today's most successful global sales leaders from a wide array of organizations and industries, Sales Growth puts the experiences of these professionals in perspective and offers real-life examples of how they've overcome the challenges encountered in the quest for growth. The book, broken down into five overarching strategies for successful sales growth, shares valuable lessons on everything from how to beat the competition by looking forward, to turning deep insights into simple messages for the front line. Page by page, you'll learn how sales executives are digging deeper than ever to find untapped growth, maximizing emerging markets opportunities, and powering growth through digital sales. You'll also discover what it takes to find big growth in big data, develop the right sales DNA in your organization, and improve channel performance. Three new chapters look at why presales deserve more attention, how to get the most out of marketing, and how technology and outsourcing could entirely reshape the sales function. Twenty new standalone interviews have been added to those from the first edition, so there are now in-depth insights from sales leaders at Adidas, Alcoa, Allianz, American Express, BMW, Cargill, Caterpillar, Cisco, Coca-Cola Enterprises, Deutsche Bank, EMC, Essent, Google, Grainger, Hewlett Packard Enterprise, Intesa Sanpaolo, Itaú Unibanco, Lattice Engines, Mars, Merck, Nissan, P&G, Pioneer Hi-Bred, Salesforce, Samsung, Schneider Electric, Siemens, SWIFT, UPS, VimpelCom, Vodafone, and Würth. Their stories, as well as numerous case studies, touch on some of the most essential elements of sales, from adapting channels to meet changing customer needs to optimizing sales operations and technology, developing sales talent and capabilities, and effectively leading the way to sales growth. Engaging and informative, this timely book details proven approaches to tangible top-line growth and an improved bottom line. Created specifically for sales executives, it will put you in a better position to drive sales growth in today's competitive market. |
| cornell university business analytics: Computational Business Analytics Subrata Das, 2013-12-14 This book presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. The author first covers core descriptive and inferential statistics for analytics and then enhances numerical statistical techniques with symbolic artificial intelligence and machine learning techniques for richer predictive and prescriptive analytics. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies. |
| cornell university business analytics: Applied Sport Business Analytics Christopher Atwater, Robert E. Baker, Ted Kwartler, 2022-03-17 This book addresses the fundamental use of analytical metrics to inform sport managers, framing sport analytics for practical use within organizations. The book is organized to present the background of sport analytics, why it is useful, selected techniques and tools employed, and its applications in sport organizations. The text guides the reader in selecting and communicating information in a useable format, and the translation of metrics in informing managers, guiding decisions, and maximizing efficiency in achieving desired outcomes-- |
| cornell university business analytics: Analytics Across the Enterprise Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton, 2014-05-15 How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics |
| cornell university business analytics: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| cornell university business analytics: How Charts Lie: Getting Smarter about Visual Information Alberto Cairo, 2019-10-15 A leading data visualization expert explores the negative—and positive—influences that charts have on our perception of truth. Today, public conversations are increasingly driven by numbers. While charts, infographics, and diagrams can make us smarter, they can also deceive—intentionally or unintentionally. To be informed citizens, we must all be able to decode and use the visual information that politicians, journalists, and even our employers present us with each day. Demystifying an essential new literacy for our data-driven world, How Charts Lie examines contemporary examples ranging from election result infographics to global GDP maps and box office record charts, as well as an updated afterword on the graphics of the COVID-19 pandemic. |
| cornell university business analytics: Business Analytics Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson, 2018-03-08 Build valuable skills that are in high demand in today’s businesses with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' market-leading BUSINESS ANALYTICS, 3E. Readers master the full range of analytics while strengthening descriptive, predictive and prescriptive analytic skills. Real-world examples and visuals help illustrate data and results for each topic. Clear, step-by-step instructions guide readers through using various software programs, including Microsoft Excel, Analytic Solver, and JMP Pro, to perform the analyses discussed. Practical, relevant problems at all levels of difficulty reinforce and teach readers to apply the concepts learned. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| cornell university business analytics: Digital Economy, Business Analytics, and Big Data Analytics Applications Saad G. Yaseen, 2022-09-26 This book is about turning data into smart decisions, knowledge into wisdom and business into business intelligence and insight. It explores diverse paradigms, methodologies, models, tools and techniques of the emerging knowledge domain of digitalized business analytics applications. The book covers almost every crucial aspect of applied artificial intelligence in business, smart mobile and digital services in business administration, marketing, accounting, logistics, finance and IT management. This book aids researchers, practitioners and decisions makers to gain enough knowledge and insight on how to effectively leverage data into competitive intelligence. |
| cornell university business analytics: Data Mining for Business Analytics Galit Shmueli, Peter C. Bruce, Nitin R. Patel, 2016-04-18 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. |
| cornell university business analytics: Actionable Intelligence Keith B. Carter, 2014-09-02 Building an analysis ecosystem for a smarter approach to intelligence Keith Carter's Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast! is the comprehensive guide to achieving the dream that business intelligence practitioners have been chasing since the concept itself came into being. Written by an IT visionary with extensive global supply chain experience and insight, this book describes what happens when team members have accurate, reliable, usable, and timely information at their fingertips. With a focus on leveraging big data, the book provides expert guidance on developing an analytical ecosystem to effectively manage, use the internal and external information to deliver business results. This book is written by an author who's been in the trenches for people who are in the trenches. It's for practitioners in the real world, who know delivering results is easier said than done – fraught with failure, and difficult politics. A landscape where reason and passion are needed to make a real difference. This book lays out the appropriate way to establish a culture of fact-based decision making, innovation, forward looking measurements, and appropriate high-speed governance. Readers will enable their organization to: Answer strategic questions faster Reduce data acquisition time and increase analysis time to improve outcomes Shift the focus to positive results rather than past failures Expand opportunities by more effectively and thoughtfully leveraging information Big data makes big promises, but it cannot deliver without the right recipe of people, processes and technology in place. It's about choosing the right people, giving them the right tools, and taking a thoughtful—rather than formulaic--approach. Actionable Intelligence provides expert guidance toward envisioning, budgeting, implementing, and delivering real benefits. |
| cornell university business analytics: Behold the Dreamers Imbolo Mbue, 2016-08-23 A compulsively readable debut novel about marriage, immigration, class, race, and the trapdoors in the American Dream—the unforgettable story of a young Cameroonian couple making a new life in New York just as the Great Recession upends the economy New York Times Bestseller • Winner of the PEN/Faulkner Award • Longlisted for the PEN/Open Book Award • An ALA Notable Book NAMED ONE OF THE BEST BOOKS OF THE YEAR BY NPR • The New York Times Book Review • San Francisco Chronicle • The Guardian • St. Louis Post-Dispatch • Chicago Public Library • BookPage • Refinery29 • Kirkus Reviews Jende Jonga, a Cameroonian immigrant living in Harlem, has come to the United States to provide a better life for himself, his wife, Neni, and their six-year-old son. In the fall of 2007, Jende can hardly believe his luck when he lands a job as a chauffeur for Clark Edwards, a senior executive at Lehman Brothers. Clark demands punctuality, discretion, and loyalty—and Jende is eager to please. Clark’s wife, Cindy, even offers Neni temporary work at the Edwardses’ summer home in the Hamptons. With these opportunities, Jende and Neni can at last gain a foothold in America and imagine a brighter future. However, the world of great power and privilege conceals troubling secrets, and soon Jende and Neni notice cracks in their employers’ façades. When the financial world is rocked by the collapse of Lehman Brothers, the Jongas are desperate to keep Jende’s job—even as their marriage threatens to fall apart. As all four lives are dramatically upended, Jende and Neni are forced to make an impossible choice. Praise for Behold the Dreamers “A debut novel by a young woman from Cameroon that illuminates the immigrant experience in America with the tenderhearted wisdom so lacking in our political discourse . . . Mbue is a bright and captivating storyteller.”—The Washington Post “A capacious, big-hearted novel.”—The New York Times Book Review “Behold the Dreamers’ heart . . . belongs to the struggles and small triumphs of the Jongas, which Mbue traces in clean, quick-moving paragraphs.”—Entertainment Weekly “Mbue’s writing is warm and captivating.”—People (book of the week) “[Mbue’s] book isn’t the first work of fiction to grapple with the global financial crisis of 2007–2008, but it’s surely one of the best. . . . It’s a novel that depicts a country both blessed and doomed, on top of the world, but always at risk of losing its balance. It is, in other words, quintessentially American.”—NPR “This story is one that needs to be told.”—Bust “Behold the Dreamers challenges us all to consider what it takes to make us genuinely content, and how long is too long to live with our dreams deferred.”—O: The Oprah Magazine “[A] beautiful, empathetic novel.”—The Boston Globe “A witty, compassionate, swiftly paced novel that takes on race, immigration, family and the dangers of capitalist excess.”—St. Louis Post-Dispatch “Mbue [is] a deft, often lyrical observer. . . . [Her] meticulous storytelling announces a writer in command of her gifts.”—Minneapolis Star Tribune |
| cornell university business analytics: Investing in Financial Research Cheryl Strauss Einhorn, 2019-03-15 Finalist in the Business/Personal Finance category of the 2019 International Book Awards Every day, people around the world make financial decisions. They choose to invest in a stock, sell their holdings in a mutual fund or buy a condominium. These decisions are complex and financially tricky—even for financial professionals. But the literature available on financial research is dated and narrowly focused without any real practical application. Until now there's been a gap in the literature: a book that shows you how to conduct a step by step comprehensive financial investigation that ends in a decision. This book gives you that how. Investing in Financial Research is a guidebook for conducting financial investigations and lays out Cheryl Strauss Einhorn's AREA Method—a research and decision-making system that uniquely controls for bias, focuses on the incentives of others and expands knowledge while improving judgement—and applies it to investigating financial situations. AREA is applicable to all sorts of financial sleuthing, whether for investment analysis or investigative journalism. It allows you to be the expert in your own life. The AREA Method provides you with: *Defined tasks that guide and focus your research on your vision of success; *A structure that isolates your sources, giving you insight into their perspectives, biases and incentives; *Investigative resources, tips and techniques to upgrade your research and analysis beyond document-based sources; *Exercises to foster creativity and originality in your thinking; *A sequence and framework that brings your disparate pieces of research together to build your confidence and conviction about your financial decision. |
| cornell university business analytics: Data Mining for Business Analytics Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel, 2019-10-14 Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. 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: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining 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 Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python 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 |
| cornell university business analytics: Computing Predictive Analytics, Business Intelligence, and Economics Cyrus F. Nourani, 2019-06-26 This volume brings together research and system designs that address the scientific basis and the practical systems design issues that support areas ranging from intelligent business interfaces and predictive analytics to economics modeling. Applications for management science and IT have been of interest areas for business schools and computing experts during recent years. Among the areas that are being treated are modern analytics, heterogeneous computing, business intelligence, ERP (enterprise resource planning), and decision science. Consumers have been pledging their love for data visualizations for a while now, and data is the area being explored, such as B2B and EC (E-commerce), E-business and the Intelligent Web, CRM (customer relationship management), infrastructures, and more. The digitization implications of these many new applications are described and explored in this informative volume. |
| cornell university business analytics: Development Research in Practice Kristoffer Bjärkefur, Luíza Cardoso de Andrade, Benjamin Daniels, Maria Ruth Jones, 2021-07-16 Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University |
| cornell university business analytics: Intelligent Optimization Techniques for Business Analytics Bansal, Sanjeev, Kumar, Nitendra, Agarwal, Priyanka, 2024-04-15 Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making. |
| cornell university business analytics: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| cornell university business analytics: The Comstocks of Cornell Anna Botsford Comstock, 2019-03-15 The Comstocks of Cornell is the autobiography written by naturalist educator Anna Botsford Comstock about her life and her husband's, entomologist John Henry Comstock—both prominent figures in the scientific community and in Cornell University history. A first edition was published in 1953, but it omitted key Cornellians, historical anecdotes, and personal insights. Karen Penders St. Clair's twenty-first century edition returns Mrs. Comstock's voice to her book by rekeying her entire manuscript as she wrote it, and preserving the memories of the personal and professional lives of the Comstocks that she had originally intended to share. The book includes a complete epilogue of the Comstocks' last years and fills in gaps from the 1953 edition. Described as serious legacy work, the book is an essential part of Cornell University history and an important piece of Cornell University Press history. |
| cornell university business analytics: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| cornell university business analytics: Integration Challenges for Analytics, Business Intelligence, and Data Mining Azevedo, Ana, Santos, Manuel Filipe, 2020-12-11 As technology continues to advance, it is critical for businesses to implement systems that can support the transformation of data into information that is crucial for the success of the company. Without the integration of data (both structured and unstructured) mining in business intelligence systems, invaluable knowledge is lost. However, there are currently many different models and approaches that must be explored to determine the best method of integration. Integration Challenges for Analytics, Business Intelligence, and Data Mining is a relevant academic book that provides empirical research findings on increasing the understanding of using data mining in the context of business intelligence and analytics systems. Covering topics that include big data, artificial intelligence, and decision making, this book is an ideal reference source for professionals working in the areas of data mining, business intelligence, and analytics; data scientists; IT specialists; managers; researchers; academicians; practitioners; and graduate students. |
| cornell university business analytics: Problem Solved Cheryl Strauss Einhorn, 2017-04-17 *International Book Awards Finalist It can be messy and overwhelming to figure out how to solve thorny problems. Where do you start? How do you know where to look for information and evaluate its quality and bias? How can you feel confident that you are making a careful and thoroughly researched decision? Whether you are deciding between colleges, navigating a career decision, helping your aging parents find the right housing, or expanding your business, Problem Solved will show you how to use the powerful AREA Method to make complex personal and professional decisions with confidence and conviction. Cheryl’s AREA Method coaches you to make smarter, better decisions because it: Recognizes that research is a fundamental part of decision making and breaks down the process into a series of easy-to-follow steps. Solves for problematic mental shortcuts such as bias, judgment, and assumptions. Builds in strategic stops that help you chunk your learning, stay focused, and make your work work for you. Provides a flexible and repeatable process that acts as a feedback loop. Life is filled with uncertainty, but that uncertainty needn’t hobble us. Problem Solved offers a proactive way to work with, and work through, ambiguity to make thoughtful, confident decisions despite our uncertain and volatile world. |
| cornell university business analytics: Handbook of Causal Analysis for Social Research Stephen L. Morgan, 2013-04-22 What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development. |
| cornell university business analytics: Strategic Engineering for Cloud Computing and Big Data Analytics Amin Hosseinian-Far, Muthu Ramachandran, Dilshad Sarwar, 2017-02-13 This book demonstrates the use of a wide range of strategic engineering concepts, theories and applied case studies to improve the safety, security and sustainability of complex and large-scale engineering and computer systems. It first details the concepts of system design, life cycle, impact assessment and security to show how these ideas can be brought to bear on the modeling, analysis and design of information systems with a focused view on cloud-computing systems and big data analytics. This informative book is a valuable resource for graduate students, researchers and industry-based practitioners working in engineering, information and business systems as well as strategy. |
| cornell university business analytics: Fintech with Artificial Intelligence, Big Data, and Blockchain Paul Moon Sub Choi, Seth H. Huang, 2021-03-08 This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain—all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths. |
| cornell university business analytics: Business Intelligence: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2015-12-29 Data analysis is an important part of modern business administration, as efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Business Intelligence: Concepts, Methodologies, Tools, and Applications presents a comprehensive examination of business data analytics along with case studies and practical applications for businesses in a variety of fields and corporate arenas. Focusing on topics and issues such as critical success factors, technology adaptation, agile development approaches, fuzzy logic tools, and best practices in business process management, this multivolume reference is of particular use to business analysts, investors, corporate managers, and entrepreneurs in a variety of prominent industries. |
| cornell university business analytics: Big Data Science in Finance Irene Aldridge, Marco Avellaneda, 2021-01-08 Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners. |
| cornell university business analytics: Applied Conjoint Analysis Vithala R. Rao, 2014-02-20 Conjoint analysis is probably the most significant development in marketing research in the past few decades. It can be described as a set of techniques ideally suited to studying customers’ decision-making processes and determining tradeoffs. Though this book is oriented towards methods and applications of conjoint analysis in marketing, conjoint methods are also applicable for other business and social sciences. After an introduction to the basic ideas of conjoint analysis the book describes the steps involved in designing a ratings-based conjoint study, it covers various methods for estimating partworth functions from preference ratings data, and dedicates a chapter on methods of design and analysis of conjoint-based choice experiments, where choice is measured directly. Chapter 5 describes several methods for handling a large number of attributes. Chapters 6 through 8 discuss the use of conjoint analysis for specific applications like product and service design or product line decisions, product positioning and market segmentation decisions, and pricing decisions. Chapter 9 collates miscellaneous applications of marketing mix including marketing resource allocation or store location decisions. Finally, Chapter 10 reviews more recent developments in experimental design and data analysis and presents an assessment of future developments. |
| cornell university business analytics: Dynamic Data Analysis James Ramsay, Giles Hooker, 2017-06-27 This text focuses on the use of smoothing methods for developing and estimating differential equations following recent developments in functional data analysis and building on techniques described in Ramsay and Silverman (2005) Functional Data Analysis. The central concept of a dynamical system as a buffer that translates sudden changes in input into smooth controlled output responses has led to applications of previously analyzed data, opening up entirely new opportunities for dynamical systems. The technical level has been kept low so that those with little or no exposure to differential equations as modeling objects can be brought into this data analysis landscape. There are already many texts on the mathematical properties of ordinary differential equations, or dynamic models, and there is a large literature distributed over many fields on models for real world processes consisting of differential equations. However, a researcher interested in fitting such a model to data, or a statistician interested in the properties of differential equations estimated from data will find rather less to work with. This book fills that gap. |
| cornell university business analytics: A Sarong for Clio Maurizio Peleggi, 2018-08-06 A Sarong for Clio testifies to an ongoing intellectual dialogue between its ten contributors and Craig J. Reynolds, who inspired these essays. Conceived as a tribute to an innovative scholar, dedicated teacher, and generous colleague, it is this volume's ambition to make a concerted intervention on Thai historiography—and Thai studies more generally—by pursuing in new directions ideas that figure prominently in Reynolds's scholarship. The writings gathered here revolve around two prominent themes in Reynolds's scholarship: the nexus of historiography and power, and Thai political and business cultures—often so intertwined as to be difficult to separate. The chapters examine different types of historical texts, Thai political discourse and political culture, and the media production of consumer culture. Contributors: Chris Baker; Patrick Jory, University of Queensland, Brisbane; Tamara Loos, Cornell University; Yoshinori Nishizaki, National University of Singapore; James Ockey, University of Canterbury; Maurizio Peleggi, National University of Singapore; Pasuk Phongpaichit, Chulalongkorn University, Bangkok; Kasian Tejapir, Thammasat University, Bangkok; Villa Vilaithong, Chulalongkorn University, Bangkok; Thongchai Winichakul, University of Wisconsin–Madison |
| cornell university business analytics: The History Of Marketing Science Russell S Winer, Scott A Neslin, 2014-06-27 The field of marketing science has a rich history of modeling marketing phenomena using the disciplines of economics, statistics, operations research, and other related fields. Since it is roughly 50 years from its origins, The History of Marketing Science is a timely review of the accomplishments of marketing scientists in a number of research areas.Different research areas of marketing science, such as Pricing, Internet Marketing, Diffusion Models, and Advertising, are treated to a highly readable and easy-to-digest historical analysis by the contributing authors. Each chapter provides a chronological timeline of key historical developments in the area of marketing science covered. Readers of other disciplinary backgrounds outside of economics, statistics, and operations research will be more than able to appreciate the development of marketing science as a field of research and its pioneers through the book. |
| cornell university business analytics: Analytics Phil Simon, 2017-07-05 For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends—and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors. |
| cornell university business analytics: Predictive Analytics for Business Strategy Jeff Prince, 2018 Reasoning with data -- Reasoning from sample to population -- The scientific method : the gold standard for establishing causality -- Linear regression as a fundamental descriptive tool -- Correlation vs. causality in regression analysis -- Basic methods for establishing causal inference -- Advanced methods for establishing causal inference -- Prediction for a dichotomous variable -- Identification and data assessment -- Applications data analysis critiques, write-ups, and projects -- Glossary |
| cornell university business analytics: Handbook of Pricing Research in Marketing Vithala R. Rao, 2009 Pricing is an essential aspect of the marketing mix for brands and products. Further, pricing research in marketing is interdisciplinary, utilizing economic and psychological concepts with special emphasis on measurement and estimation. This unique Handbook provides current knowledge of pricing in a single, authoritative volume and brings together new cutting-edge research by established marketing scholars on a range of topics in the area. The environment in which pricing decisions and transactions are implemented has changed dramatically, mainly due to the advent of the Internet and the practices of advance selling and yield management. Over the years, marketing scholars have incorporated developments in game theory and microeconomics, behavioral decision theory, psychological and social dimensions and newer market mechanisms of auctions in their contributions to pricing research. These chapters, specifically written for this Handbook, cover these various developments and concepts as applied to tackling pricing problems. Academics and doctoral students in marketing and applied economics, as well as pricing-focused business practitioners and consultants, will appreciate the state-of-the-art research herein. |
| cornell university business analytics: Revenue Management and Pricing Analytics Guillermo Gallego, Huseyin Topaloglu, 2019-08-14 “There is no strategic investment that has a higher return than investing in good pricing, and the text by Gallego and Topaloghu provides the best technical treatment of pricing strategy and tactics available.” Preston McAfee, the J. Stanley Johnson Professor, California Institute of Technology and Chief Economist and Corp VP, Microsoft. “The book by Gallego and Topaloglu provides a fresh, up-to-date and in depth treatment of revenue management and pricing. It fills an important gap as it covers not only traditional revenue management topics also new and important topics such as revenue management under customer choice as well as pricing under competition and online learning. The book can be used for different audiences that range from advanced undergraduate students to masters and PhD students. It provides an in-depth treatment covering recent state of the art topics in an interesting and innovative way. I highly recommend it. Professor Georgia Perakis, the William F. Pounds Professor of Operations Research and Operations Management at the Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts. “This book is an important and timely addition to the pricing analytics literature by two authors who have made major contributions to the field. It covers traditional revenue management as well as assortment optimization and dynamic pricing. The comprehensive treatment of choice models in each application is particularly welcome. It is mathematically rigorous but accessible to students at the advanced undergraduate or graduate levels with a rich set of exercises at the end of each chapter. This book is highly recommended for Masters or PhD level courses on the topic and is a necessity for researchers with an interest in the field.” Robert L. Phillips, Director of Pricing Research at Amazon “At last, a serious and comprehensive treatment of modern revenue management and assortment optimization integrated with choice modeling. In this book, Gallego and Topaloglu provide the underlying model derivations together with a wide range of applications and examples; all of these facets will better equip students for handling real-world problems. For mathematically inclined researchers and practitioners, it will doubtless prove to be thought-provoking and an invaluable reference.” Richard Ratliff, Research Scientist at Sabre “This book, written by two of the leading researchers in the area, brings together in one place most of the recent research on revenue management and pricing analytics. New industries (ride sharing, cloud computing, restaurants) and new developments in the airline and hotel industries make this book very timely and relevant, and will serve as a critical reference for researchers.” Professor Kalyan Talluri, the Munjal Chair in Global Business and Operations, Imperial College, London, UK. |
| cornell university business analytics: Freer Markets, More Rules Steven K. Vogel, 2018-05-31 Over the past fifteen years, the United States, Western Europe, and Japan have transformed the relationship between governments and corporations. The changes are complex and the terms used to describe them often obscure the reality. In Freer Markets, More Rules, Steven K. Vogel dispenses with euphemisms and makes sense of this recent transformation. In defiance of conventional wisdom, Vogel contends that the deregulation revolution of the 1980s and 1990s never happened. The advanced industrial countries moved toward liberalization or freer markets at the same time that they imposed reregulation or more rules. Moreover, the countries involved did not converge in regulatory practice but combined liberalization and reregulation in markedly different ways. The state itself, far more than private interest groups, drove the process of regulatory reform. Thus, the story of deregulation is one rich in paradox: a movement aimed at reducing regulation increased it; a movement propelled by global forces reinforced national differences; and a movement that purported to reduce state power was led by the state itself. Vogel's astute and far-reaching analysis compares deregulation in Britain and Japan, with special attention to the telecommunication and financial services industries. He also considers such important sectors as broadcasting, transportation, and utilities in the United States, France, and Germany. |
在康奈尔大学 (Cornell University) 就读是种怎样的体验？ - 知乎
但这里就分享一个好玩的经历吧，这件事我觉得真心是Cornell这样的名校才能给我的，而且是我看完《阿拉伯的劳伦斯》后一直神往的地方，那就是我在读书期间获得了沙特阿拉伯政府全额奖 …
大家怎么看位于纽约市的 Cornell Tech（康奈尔科技校区）项目？
因为我在Cornell本部也读过，应该比较有发言权，我就来解释下这个事。Cornell一直因为它较偏僻的地理位置被诟病，所以Cornell长期以来都有在纽约的分校，而且分校和本部之间联系紧密。 …
硕士毕业论文是深度学习相关，需要自己做数据集，但我做出来的 …
盲审的话有两个点可以毙掉你的论文： （1）自己做的数据集。一般算法创新需要在公开数据集上测试效果，如果需要特殊数据集，应该先在公开数据集上证明自己方法的有效性，然后再在自 …
常春藤、25所新常春藤、公立常春藤都是哪些学校？ - 知乎
康奈尔大学（Cornell University）#18; 新常春藤（25所） 范德堡大学（Vanderbilt University）#14; 圣路易斯华盛顿大学（Washington University in St. Louis）#16; 莱斯大 …
如何评价英伟达发布的 Tesla V100 计算卡？ - 知乎
原文：Cornell University -> Cornell Virtual Workshop -> Understanding GPU Architecture -> GPU Example: Tesla V100. It's fine to have a general understanding of what graphics processing …
致久坐腰疼的年轻人——七年总结的办公久坐护腰指南
Oct 24, 2023 · 根据2:1的规律，每天仍有至少有6小时以上的坐姿时间，更何况996的老哥门，每天至少有8小时需要坐在椅子上。
在康奈尔大学 (Cornell University) 就读是种怎样的体验？ - 知乎
但这里就分享一个好玩的经历吧，这件事我觉得真心是Cornell这样的名校才能给我的，而且是我看完《阿拉伯的劳伦斯》后一直神往的地方，那就是我在读书期间获得了沙特阿拉伯政府全额奖 …
大家怎么看位于纽约市的 Cornell Tech（康奈尔科技校区）项目？
因为我在Cornell本部也读过，应该比较有发言权，我就来解释下这个事。Cornell一直因为它较偏僻的地理位置被诟病，所以Cornell长期以来都有在纽约的分校，而且分校和本部之间联系紧密。 …
硕士毕业论文是深度学习相关，需要自己做数据集，但我做出来的 …
盲审的话有两个点可以毙掉你的论文： （1）自己做的数据集。一般算法创新需要在公开数据集上测试效果，如果需要特殊数据集，应该先在公开数据集上证明自己方法的有效性，然后再在自 …
常春藤、25所新常春藤、公立常春藤都是哪些学校？ - 知乎
康奈尔大学（Cornell University）#18; 新常春藤（25所） 范德堡大学（Vanderbilt University）#14; 圣路易斯华盛顿大学（Washington University in St. Louis）#16; 莱斯大学（Rice …
如何评价英伟达发布的 Tesla V100 计算卡？ - 知乎
原文：Cornell University -> Cornell Virtual Workshop -> Understanding GPU Architecture -> GPU Example: Tesla V100. It's fine to have a general understanding of what graphics processing …
致久坐腰疼的年轻人——七年总结的办公久坐护腰指南
Oct 24, 2023 · 根据2:1的规律，每天仍有至少有6小时以上的坐姿时间，更何况996的老哥门，每天至少有8小时需要坐在椅子上。
