Advertisement
| business analytics penn state: Learning Spaces Diana Oblinger, 2006 El espacio, ya sea físico o virtual, puede tener un impacto significativo en el aprendizaje. Learning Spaces se centra en la forma en que las expectativas de los alumnos influyen en dichos espacios, en los principios y actividades que facilitan el aprendizaje y en el papel de la tecnología desde la perspectiva de quienes crean los entornos de aprendizaje: profesores, tecnólogos del aprendizaje, bibliotecarios y administradores. La tecnología de la información ha aportado capacidades únicas a los espacios de aprendizaje, ya sea estimulando una mayor interacción mediante el uso de herramientas de colaboración, videoconferencias con expertos internacionales o abriendo mundos virtuales para la exploración. Este libro representa una exploración continua a medida que unimos el espacio, la tecnología y la pedagogía para asegurar el éxito de los estudiantes. |
| business analytics penn state: A User's Guide to Business Analytics Ayanendranath Basu, Srabashi Basu, 2016-08-19 A User's Guide to Business Analytics provides a comprehensive discussion of statistical methods useful to the business analyst. Methods are developed from a fairly basic level to accommodate readers who have limited training in the theory of statistics. A substantial number of case studies and numerical illustrations using the R-software package are provided for the benefit of motivated beginners who want to get a head start in analytics as well as for experts on the job who will benefit by using this text as a reference book. The book is comprised of 12 chapters. The first chapter focuses on business analytics, along with its emergence and application, and sets up a context for the whole book. The next three chapters introduce R and provide a comprehensive discussion on descriptive analytics, including numerical data summarization and visual analytics. Chapters five through seven discuss set theory, definitions and counting rules, probability, random variables, and probability distributions, with a number of business scenario examples. These chapters lay down the foundation for predictive analytics and model building. Chapter eight deals with statistical inference and discusses the most common testing procedures. Chapters nine through twelve deal entirely with predictive analytics. The chapter on regression is quite extensive, dealing with model development and model complexity from a user’s perspective. A short chapter on tree-based methods puts forth the main application areas succinctly. The chapter on data mining is a good introduction to the most common machine learning algorithms. The last chapter highlights the role of different time series models in analytics. In all the chapters, the authors showcase a number of examples and case studies and provide guidelines to users in the analytics field. |
| business analytics penn state: 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. |
| business analytics penn state: Business Analytics with Management Science Models and Methods Arben Asllani, 2015 This book is about prescriptive analytics. It provides business practitioners and students with a selected set of management science and optimization techniques and discusses the fundamental concepts, methods, and models needed to understand and implement these techniques in the era of Big Data. A large number of management science models exist in the body of literature today. These models include optimization techniques or heuristics, static or dynamic programming, and deterministic or stochastic modeling. The topics selected in this book, mathematical programming and simulation modeling, are believed to be among the most popular management science tools, as they can be used to solve a majority of business optimization problems. Over the years, these techniques have become the weapon of choice for decision makers and practitioners when dealing with complex business systems. |
| business analytics penn state: Principles of Database Management Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens, 2018-07-12 Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science. |
| business analytics penn state: 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. |
| business analytics penn state: The Future of the Office Peter Cappelli, 2021-08-10 A GLOBE & MAIL BEST BUSINESS BOOK OF 2021 The COVID-19 pandemic forced an unprecedented experiment that reshaped white-collar work and turned remote work into a kind of new normal. Now comes the hard part. Many employees want to continue that normal and keep working remotely, and most at least want the ability to work occasionally from home. But for employers, the benefits of employees working from home or hybrid approaches are not so obvious. What should both groups do? In a prescient new book, The Future of the Office: Work from Home, Remote Work, and the Hard Choices We All Face, Wharton professor Peter Cappelli lays out the facts in an effort to provide both employees and employers with a vision of their futures. Cappelli unveils the surprising tradeoffs both may have to accept to get what they want. Cappelli illustrates the challenges we face by in drawing lessons from the pandemic and deciding what to do moving forward. Do we allow some workers to be permanently remote? Do we let others choose when to work from home? Do we get rid of their offices? What else has to change, depending on the approach we choose? His research reveals there is no consensus among business leaders. Even the most high-profile and forward-thinking companies are taking divergent approaches: --Facebook, Twitter, and other tech companies say many employees can work remotely on a permanent basis. --Goldman Sachs, JP Morgan, and others say it is important for everyone to come back to the office. --Ford is redoing its office space so that most employees can work from home at least part of the time, and --GM is planning to let local managers work out arrangements on an ad-hoc basis. As Cappelli examines, earlier research on other types of remote work, including telecommuting offers some guidance as to what to expect when some people will be in the office and others work at home, and also what happened when employers tried to take back offices. Neither worked as expected. In a call to action for both employers and employees, Cappelli explores how we should think about the choices going forward as well as who wins and who loses. As he implores, we have to choose soon. |
| business analytics penn state: Business Process Management Akhil Kumar, 2018-02-02 This book introduces students to business process management, an approach that aims to align the organization’s business processes with the demands of the marketplace. Processes serve as a coordination mechanism, and the aim of business process management is to improve the organization’s effectiveness and efficiency in adapting to change, and maintaining competitive advantage. In Business Process Management, Kumar argues for the value of looking at businesses as a collection of processes that cut across departments, and for breaking down functional silos. The book provides an overview of the basic concepts in this field before moving on to more advanced topics such as process verification, flexible processes, process security and evaluation, resource assignment, and social networks. The book concludes with an examination of the future directions of the discipline. Blending a strong grounding in current research with a focus on concepts and tools, Business Process Management is an accessible textbook full of practical examples and cases that will appeal to upper level students. |
| business analytics penn state: Data Analysis Using SQL and Excel Gordon S. Linoff, 2010-09-16 Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like. |
| business analytics penn state: Instructional Design for Teachers Alison A. Carr-Chellman, 2015-06-26 Instructional Design for Teachers, Second Edition focuses on the instructional design (ID) process specifically for K-12 teachers. The first edition introduced a new, common-sense model of instructional design to take K-12 teachers through the ID process step by step, with a special emphasis on preparing, motivating, and encouraging new and ongoing use of ID principles. This second edition includes new material on design in gaming, cybercharters, online classrooms, and flipped classrooms, as well as special considerations for the Common Core. Each chapter contains framing questions, common errors, easy-to-use rules of thumb, clearly stated outcomes, and examples showing ID in action. The basic model and its application within constructivism and user-design will help teachers adapt from a behavioral approach to a more open, student-centered design approach. Combining basics with strategies to implement this model in the most advanced instructional approaches, this book empowers teachers and learners to use good instructional design with the most recent research-based approaches to learning. Instructional Design for Teachers shows how ID principles can impact instructional moments in positive and practical ways. The book can be used for basic ID courses and introductory curriculum courses, and is accessible to in-service as well as pre-service teachers. |
| business analytics penn state: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| business analytics penn state: Sprawlball Kirk Patrick Goldsberry, 2019 Beautifully illustrated and sharply written, SprawlBall is both a celebration and a critique of the 3-point shot. If you want to understand how the modern NBA came to be, you'll need to read this book. --Nate Silver, editor of fivethirtyeight.com From the leading expert in the exploding field of basketball analytics, a stunning infographic decoding of the modern NBA: who shoots where, and how. The field of basketball analytics has leaped to overdrive thanks to Kirk Goldsberry, whose visual maps of players, teams, and positions have helped teams understand who really is the most valuable player at any position. SprawlBall combines stunning visuals, in-depth analysis, fun, behind-the-scenes stories and gee-whiz facts to chart a modern revolution. From the introduction of the 3-point line to today, the game has changed drastically . . . Now, players like Steph Curry and Draymond Green are leading the charge. In chapters like The Geography of the NBA, The Interior Minister (Lebron James), The Evolution of Steph Curry, and The Investor (James Harden), Goldsberry explains why today's on-court product--with its emphasis on shooting, passing, and spacing--has never been prettier or more democratic. And it's never been more popular. For fans of Bill Simmons and FreeDarko, SprawlBall is a bold new vision of the game, presenting an innovative, cutting-edge look at the sport based on the latest research, as well as a visual and infographic feast for fans. |
| business analytics penn state: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| business analytics penn state: Principles of Marketing Engineering, 2nd Edition Gary L. Lilien, Arvind Rangaswamy, Arnaud De Bruyn, 2013 The 21st century business environment demands more analysis and rigor in marketing decision making. Increasingly, marketing decision making resembles design engineering-putting together concepts, data, analyses, and simulations to learn about the marketplace and to design effective marketing plans. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering (that is, combining art and science to solve specific problems). Marketing Engineering is the systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process. (For more information on Excel-based models that support these concepts, visit DecisionPro.biz.) We have designed this book primarily for the business school student or marketing manager, who, with minimal background and technical training, must understand and employ the basic tools and models associated with Marketing Engineering. We offer an accessible overview of the most widely used marketing engineering concepts and tools and show how they drive the collection of the right data and information to perform the right analyses to make better marketing plans, better product designs, and better marketing decisions. What's New In the 2nd Edition While much has changed in the nearly five years since the first edition of Principles of Marketing Engineering was published, much has remained the same. Hence, we have not changed the basic structure or contents of the book. We have, however Updated the examples and references. Added new content on customer lifetime value and customer valuation methods. Added several new pricing models. Added new material on reverse perceptual mapping to describe some exciting enhancements to our Marketing Engineering for Excel software. Provided some new perspectives on the future of Marketing Engineering. Provided better alignment between the content of the text and both the software and cases available with Marketing Engineering for Excel 2.0. |
| business analytics penn state: Statements of Resolve Roseanne W. McManus, 2017-07-25 This book analyzes the conditions under which leaders can use resolved statements to effectively coerce foreign adversaries. |
| business analytics penn state: The Applied Business Analytics Casebook Matthew J. Drake, 2014 The first collection of cases on big data analytics for supply chain, operations research, and operations management, this reference puts readers in the position of the analytics professional and decision-maker. Perfect for students, practitioners, and certification candidates in SCM, OM, and OR, these short, focused, to-the-point case studies illustrate the entire decision-making process. They provide realistic opportunities to perform analyses, interpret output, and recommend an optimal course of action. Contributed by leading big data experts, the cases in The Applied Business Analytics Casebook covers: Forecasting and statistical analysis: time series forecasting models, regression models, data visualization, and hypothesis testing Optimization and simulation: linear, integer, and nonlinear programming; Monte Carlo simulation and risk analysis; and stochastic optimization Decision analysis: decision making under uncertainty; expected value of perfect information; decision trees; game theory models; AHP; and multi-criteria decision making Advanced business analytics: data warehousing/mining; text mining; neural networks; financial analytics; CRM analytics; and revenue management models |
| business analytics penn state: The Tolls of Uncertainty Sarah Damaske, 2021-05-25 An indispensable investigation into the American unemployment system and the ways gender and class affect the lives of those looking for work Through the intimate stories of those seeking work, The Tolls of Uncertainty offers a startling look at the nation’s unemployment system—who it helps, who it hurts, and what, if anything, we can do to make it fair. Drawing on interviews with one hundred men and women who have lost jobs across Pennsylvania, Sarah Damaske examines the ways unemployment shapes families, finances, health, and the job hunt. Damaske demonstrates that commonly held views of unemployment are either incomplete or just plain wrong. Shaped by a person’s gender and class, unemployment generates new inequalities that cast uncertainties on the search for work and on life chances beyond the world of work, threatening opportunity in America. Following in depth the lives of four individuals over the course of their unemployment experiences, Damaske offers insights into how the unemployed perceive their relationship to work. She reveals the high levels of blame that women who have lost jobs place on themselves, leading them to put their families’ needs above their own, sacrifice their health, and take on more tasks inside the home. This “guilt gap” illustrates how unemployment all too often exacerbates existing differences between men and women. Class privilege, too, gives some an advantage, while leaving others at the mercy of an underfunded unemployment system. Middle-class men are generally able to create the time and space to search for good work, but many others are bogged down by the challenges of poverty-level unemployment benefits and family pressures and fall further behind. Timely and engaging, The Tolls of Uncertainty posits that a new path must be taken if the nation’s unemployed are to find real relief. |
| business analytics penn state: Readings in Strategic Marketing Barton A. Weitz, Robin Wensley, 1988-01 |
| business analytics penn state: Introduction to Engineering Design Edsgn, 2008 |
| business analytics penn state: Advances in Service Science Hui Yang, Robin Qiu, 2018-12-28 This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas. It showcases emerging technology and applications in fields including healthcare, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users' both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. The papers in this volume highlight ways to approach such technical challenges in service science and are based on submissions from the 2018 INFORMS International Conference on Service Science. |
| business analytics penn state: The Bakhtin Circle David G. Shepherd, Craig Brandist, Galin Tihanov, 2004 This book is a collection of essays on the most important figures associated with the Bakhtin Circle. It offers new biographical material, valuable translations of important Russian texts, a timeline and extensive bibliographical references. |
| business analytics penn state: Best Practices for Online Procurement Auctions Parente, Diane H., 2007-12-31 Offers a systematic approach to the examination of online procurement auctions. Growth in online auctions reinforces the need for understanding the factors important in auctions and the caveats that both researchers and practitioners need to know in order to effectively study and use the auction tool. |
| business analytics penn state: Successful Business Intelligence: Secrets to Making BI a Killer App Cindi Howson, 2007-12-17 Praise for Successful Business Intelligence If you want to be an analytical competitor, you've got to go well beyond business intelligence technology. Cindi Howson has wrapped up the needed advice on technology, organization, strategy, and even culture in a neat package. It's required reading for quantitatively oriented strategists and the technologists who support them. --Thomas H. Davenport, President's Distinguished Professor, Babson College and co-author, Competing on Analytics When used strategically, business intelligence can help companies transform their organization to be more agile, more competitive, and more profitable. Successful Business Intelligence offers valuable guidance for companies looking to embark upon their first BI project as well as those hoping to maximize their current deployments. --John Schwarz, CEO, Business Objects A thoughtful, clearly written, and carefully researched examination of all facets of business intelligence that your organization needs to know to run its business more intelligently and exploit information to its fullest extent. --Wayne Eckerson, Director, TDWI Research Using real-world examples, Cindi Howson shows you how to use business intelligence to improve the performance, and the quality, of your company. --Bill Baker, Distinguished Engineer & GM, Business Intelligence Applications, Microsoft Corporation This book outlines the key steps to make BI an integral part of your company's culture and demonstrates how your company can use BI as a competitive differentiator. --Robert VanHees, CFO, Corporate Express Given the trend to expand the business analytics user base, organizations are faced with a number of challenges that affect the success rate of these projects. This insightful book provides practical advice on improving that success rate. --Dan Vesset, Vice President, Business Analytics Solution Research, IDC |
| business analytics penn state: Predictive Analytics For Business Using R Russell R Barton, 2024-07-16 The fields of mathematical statistics, statistical graphics, computer science and operations research have created the rich set of methods now called Analytics. Often analytics is characterized along three poles: descriptive analytics (what do data tell us), predictive analytics (what can be forecast based on the data, and with what certainty), and prescriptive analytics (how can the data inform changes to improve system performance).This book focuses on the second pole, predictive analytics. The areas of predicting a number, a class, and dynamic behavior are distinct, with different methods. This text has three parts based on these areas. Topics in predicting a number include simple and multiple linear regression, transformation of variables, analysis of observational data via cross-validation, the generalized linear model, designed experiments, and Gaussian process and neural network regression. Classification methods include neural networks, logistic regression, k-nearest neighbor, and linear discriminant analysis. Methods for predicting dynamic behavior include trend analysis, time series analysis and discrete-event dynamic simulation.Characterizing prediction uncertainty is a key focus of this text. The text provides analytic methods appropriate to each area, with an explicit process for applying such methods. Case data with corresponding R code are used to illustrate each method.Predictive Analytics for Business using R is designed for a hybrid class structure. Class sessions can be a blend of lecture format and flipped classroom case analyses. In a two-meetings-per-week fifteen-week structure, one day per week would be devoted to explaining methodology and presenting a case study, with the second day focused on coaching. Given the case structure, the text does not contain homework problems. Instead, at the end of each chapter there are links to cases posted online. |
| business analytics penn state: Who Owns the Ice House? Gary G. Schoeniger, Clifton L. Taulbert, 2011-06 In the late 1950s, Glen Allan, Mississippi, was a poor cotton community. For many, it was a time and place where opportunities were limited by social and legal constraints that were beyond their control. It was a time and place where few dared to dream. Based on his own life experience, Pulitzer nominee Clifton Taulbert has teamed up with entrepreneur thought leader Gary Schoeniger to create a powerful and compelling story that captures the essence of an entrepreneurial mindset and the unlimited opportunities it can provide. Drawing on the entrepreneurial life lessons Taulbert learned from his Uncle Cleve, Who Owns the Ice house? chronicles Taulbert s journey from life in the Mississippi Delta at the height of legal segregation to being recognized by Time magazine as one of our nation s most outstanding emerging entrepreneurs. Who Owns The Ice House? reaches into the past to remind us of the timeless and universal principles that can empower anyone to succeed. |
| business analytics penn state: Advances in Artificial-Business Analytics and Quantum Machine Learning K. C. Santosh, |
| business analytics penn state: A Users Guide to Business Analytics Ayanendranath Basu, Srabashi Basu, 2016-03-15 This book provides a comprehensive discussion of statistical methods that are useful to the business analyst. The book includes a substantial number of case studies and numerical illustrations using the R software. A collection of basic techniques that analytics personnel require with detailed case studies, the book can help motivated young personnel to get a head-start in analytics, and serve as a comprehensive reference book for the experts on the job. |
| business analytics penn state: Handbook on Digital Platforms and Business Ecosystems in Manufacturing Sabine Baumann, 2024-03-14 This timely Handbook examines the rapidly expanding research area of digital platforms and business ecosystems in the context of manufacturing industries. Chapters analyze core topics such as business model transformation, ecosystem design, and governance, offering an up-to-date overview of crucial research. |
| business analytics penn state: Cost and Value Management in Projects Ray R. Venkataraman, Jeffrey K. Pinto, 2011-08-26 Cost and Value Management in Projects provides practicing managers with a thorough understanding of the various dimensions of cost and value in projects, along with the factors that impact them, and the managerial approaches that would be most effective for achieving cost efficiency and value optimization. This book addresses cost from a strategic perspective, offering thorough coverage of the various elements of value management such as value planning, value engineering and value analysis from the perspective of projects. |
| business analytics penn state: Planning Organization and Administration , 1965 |
| business analytics penn state: Introduction to Risk Management and Insurance Mark S. Dorfman, David A. Cather, 2013 For upper level undergraduate/graduate courses in Principles of Insurance and Risk Management. Drawing from the author's extensive teaching experience, this book introduces students to basic insurance concepts from the consumer's point of view and equips them with the tools to make intelligent, informed insurance purchasing decisions. The tenth edition has been reorganized and fully updated to highlight the increased importance of risk management and insurance in business and society. In particular, the tenth edition refocuses its attention on corporate risk management, reflecting its growing importance in today's economy. |
| business analytics penn state: Recent Advancements in Computational Finance and Business Analytics Rangan Gupta, |
| business analytics penn state: Applied Linear Regression for Business Analytics with R Daniel P. McGibney, 2023-07-04 Applied Linear Regression for Business Analytics with R introduces regression analysis to business students using the R programming language with a focus on illustrating and solving real-time, topical problems. Specifically, this book presents modern and relevant case studies from the business world, along with clear and concise explanations of the theory, intuition, hands-on examples, and the coding required to employ regression modeling. Each chapter includes the mathematical formulation and details of regression analysis and provides in-depth practical analysis using the R programming language. |
| business analytics penn state: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| business analytics penn state: Astrostatistics Gutti Jogesh Babu, E.D. Feigelson, 1996-08-01 Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The marriage of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields. |
| business analytics penn state: Statistical Methods in Customer Relationship Management V. Kumar, J. Andrew Petersen, 2012-07-26 Statistical Methods in Customer Relationship Management focuses on the quantitative and modeling aspects of customer management strategies that lead to future firm profitability, with emphasis on developing an understanding of Customer Relationship Management (CRM) models as the guiding concept for profitable customer management. To understand and explore the functioning of CRM models, this book traces the management strategies throughout a customer’s tenure with a firm. Furthermore, the book explores in detail CRM models for customer acquisition, customer retention, customer acquisition and retention, customer churn, and customer win back. Statistical Methods in Customer Relationship Management: Provides an overview of a CRM system, introducing key concepts and metrics needed to understand and implement these models. Focuses on five CRM models: customer acquisition, customer retention, customer churn, and customer win back with supporting case studies. Explores each model in detail, from investigating the need for CRM models to looking at the future of the models. Presents models and concepts that span across the introductory, advanced, and specialist levels. Academics and practitioners involved in the area of CRM as well as instructors of applied statistics and quantitative marketing courses will benefit from this book. |
| business analytics penn state: The Decline of the Death Penalty and the Discovery of Innocence Frank R. Baumgartner, Suzanna L. De Boef, Amber E. Boydstun, 2008-01-07 Since 1996, death sentences in America have declined by more than 60 percent, reversing a generation-long trend toward greater acceptance of capital punishment. In theory, most Americans continue to support the death penalty. But it is no longer seen as a theoretical matter. Prosecutors, judges, and juries across the country have moved in large numbers to give much greater credence to the possibility of mistakes - mistakes that in this arena are potentially fatal. The discovery of innocence, documented in this book through painstaking analyses of media coverage and with newly developed methods, has led to historic shifts in public opinion and to a sharp decline in use of the death penalty by juries across the country. A social cascade, starting with legal clinics and innocence projects, has snowballed into a national phenomenon that may spell the end of the death penalty in America. |
| business analytics penn state: Exploring Business Karen Collins, 2009 |
| business analytics penn state: Ten Types of Innovation Larry Keeley, Helen Walters, Ryan Pikkel, Brian Quinn, 2013-07-15 Innovation principles to bring about meaningful and sustainable growth in your organization Using a list of more than 2,000 successful innovations, including Cirque du Soleil, early IBM mainframes, the Ford Model-T, and many more, the authors applied a proprietary algorithm and determined ten meaningful groupings—the Ten Types of Innovation—that provided insight into innovation. The Ten Types of Innovation explores these insights to diagnose patterns of innovation within industries, to identify innovation opportunities, and to evaluate how firms are performing against competitors. The framework has proven to be one of the most enduring and useful ways to start thinking about transformation. Details how you can use these innovation principles to bring about meaningful—and sustainable—growth within your organization Author Larry Keeley is a world renowned speaker, innovation consultant, and president and co-founder of Doblin, the innovation practice of Monitor Group; BusinessWeek named Keeley one of seven Innovation Gurus who are changing the field The Ten Types of Innovation concept has influenced thousands of executives and companies around the world since its discovery in 1998. The Ten Types of Innovation is the first book explaining how to implement it. |
| business analytics penn state: Business Statistics Ken Black, Ken (University of Houston Black, Clear Lake TX), 2023-12-25 |
Graduate Program in Data Analytics - Penn State World Campus
In the 9-credit online Graduate Certificate in Business Analytics program you can learn how to leverage data to make strategic business decisions for your organization. As the data from …
THE APPLICATIONS OF USAGE OF BUSINESS ANALYTICS IN …
9 Business analytics is a powerful tool used extensively in supply chain optimization to 10 enhance efficiency, reduce costs, improve customer service, and make informed decisions. 11 Business …
Graduate Certificate in Data Analytics - Penn State Great Valley …
May 30, 2024 · The Data Analytics Graduate Certificate prepares students for working as a Data Analyst, Information Officer, or Quantitative Analyst responsible for presenting data in visual and …
Supply Chain and Information Systems - smeal.psu.edu
explore, analyze, integrate, and report business data; students excel in developing proficiency in business analytics, competency in system analysis and design, and mastering of core business …
Business Analytics (BAN) - Pennsylvania State University
BAN 830 explores the use of descriptive analytics concepts, tools, and techniques throughout a wide range of business scenarios and problems.
One-Year Master of Accounting Program - Penn State Smeal …
Learn from world-class professors who will prepare you with the quantitative and analytical skills required for the modern accounting professional. Gain practical experience during a semester …
Graduate Certificate in Business Analytics
Learn about effective integration and implementation of prescriptive analytics in supply-side decision-making processes, such as risk mitigation, supply chain management, service …
BUSINESS ANALYTICS IN THE CASE OF INVENTORY …
Business Analytics has emerged as a crucial tool for optimizing inventory control and supply chain management in this context. This integration of digital technologies, the Internet of Things,...
MPS in Data Analytics Core, Required and Elective Courses
The MPS in Data Analytics will be awarded upon successful completion of the 30-credit curriculum below, and successful completion of the SARI Requirement. The courses are not listed in …
Data Analytics - Pennsylvania State University
Penn State Great Valley offers two graduate programs that cultivate the skills to collect, classify, analyze, and model data: the Master of Data Analytics (MDAAN) and the Master of Science in …
Online Graduate Programs in Data Analytics - Penn State World …
Learn to apply big data analytics, data mining techniques, and predictive analytics to meet your organization’s business objectives and improve your organization’s competitive standing in the …
Business, B.S. (Abington) - Pennsylvania State University
The Business Analytics option prepares students to pursue careers in applying business analytics techniques to implement appropriate decision-making outcomes using data for companies.
Penn State Online MBA - Penn State World Campus
Business Analytics covers how to use optimization and forecasting techniques across various areas of business. Business Architecture explores the concepts of enterprise modeling approaches …
DATA ANALYTICS - Pennsylvania State University
Students select to follow either the base program, which prepares them to design and deploy predictive analytics systems, or specialized options in Business Analytics, Marketing Analytics, or …
Graduate Programs in Marketing Analytics - Penn State World …
Penn State’s graduate programs in marketing analytics, offered online through Penn State World Campus in partnership with the internationally ranked Smeal College of Business, can help you …
DATA AN ALY TICS - Pennsylvania State University
Admission to the Master of Data Analytics (MDAAN) program will be based on baccalaureate academic records, applicable work experience, and two letters of recommendation from previous …
Integrated Master of Accounting Program - Penn State Smeal ...
To be licensed as a certified public accountant (CPA) in nearly every state, including Pennsylvania, individuals must complete 150 credit-hours of education. To fulfill this educational requirement, …
MANAGEMENT INFORMATION SYSTEMS, B.S. (BUSINESS)
Graduates develop cross-functional literacy in how techniques and technologies help achieve business objectives, along with competency in applying business analytics methods on behalf of …
Penn State Smeal College of Business Marketing
MKTG 474 (3): Marketing Analytics Introduction to a variety of analytical techniques used for data-driven marketing decision making. MKTG 497 (3): Penn State Prime Practicum Penn State Prime …
Graduate Program in Data Analytics - Penn State World Campus
More than just predictive analytics, the business analytics curriculum explores and analyzes large sets of data to support data-driven business decisions, utilizing the complete spectrum of …
Graduate Certificate in Business Analytics
The Graduate Certificate in Business Analytics consists of three courses from the Master of Professional Studies in Data Analytics—Business Analytics option.
Online Graduate Programs in Data Analytics - Penn State World …
graduate data analytics program at Penn State, you can learn how to leverage data to make strategic business decisions that benefit your organization. Penn State’s online programs allow …
Penn State Online MBA - Penn State World Campus
“The Penn State Online MBA provides a highly engaging and integrative learning environment for professionals who want to advance or change their career paths. The program can be …
Graduate Programs in Marketing Analytics - Penn State World …
Penn State’s graduate programs in marketing analytics, offered online through Penn State World Campus in partnership with the internationally ranked Smeal College of Business, can help you …
Graduate Programs in Supply Chain Management - Penn State …
fields of study, including business analytics, corporate innovation, strategic . leadership, business sustainability strategy, negotiation, and more. worldcampu. s.psu.edu/scm. 4 “The Penn State …
Graduate Certificate in Business-to-Business Marketing
provide you with a unique understanding of business markets and teach the fundamental concepts, theories, and tools specific to B2B marketing strategy, innovation, and analytics. Required …
Bachelor of Science in Functional Data Analytics - Penn State …
Data Analytics The Bachelor of Science in Functional Data Analytics, offered 100% online through Penn State World Campus, is tailored to prepare you for a successful career as a data analyst, …
Graduate Programs in Corporate Innovation and Entrepreneurship
ranked Penn State Smeal College of Business and delivered online through Penn State World Campus, these interdisciplinary programs help you gain expertise in the strategies for creating …
Welcome [www.worldcampus.psu.edu]
critical thinking to solve real-world business problems Build an in-depth understanding of how successful businesses operate Gain marketable skills in areas such as business analytics, …
