| data mining for business intelligence rutgers reddit: Predictive Data Mining Sholom M. Weiss, Nitin Indurkhya, 1998 This book is the first technical guide to provide a complete, generalized road map for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses. |
| data mining for business intelligence rutgers reddit: Big Data Analytics with R and Hadoop Vignesh Prajapati, 2013 Big Data Analytics with R and Hadoop is a tutorial style book that focuses on all the powerful big data tasks that can be achieved by integrating R and Hadoop.This book is ideal for R developers who are looking for a way to perform big data analytics with Hadoop. This book is also aimed at those who know Hadoop and want to build some intelligent applications over Big data with R packages. It would be helpful if readers have basic knowledge of R. |
| data mining for business intelligence rutgers reddit: Market Intelligence Resources 2008 , |
| data mining for business intelligence rutgers reddit: Introductory Relational Database Design for Business, with Microsoft Access Jonathan Eckstein, Bonnie R. Schultz, 2018-01-16 A hands-on beginner’s guide to designing relational databases and managing data using Microsoft Access Relational databases represent one of the most enduring and pervasive forms of information technology. Yet most texts covering relational database design assume an extensive, sophisticated computer science background. There are texts on relational database software tools like Microsoft Access that assume less background, but they focus primarily on details of the user interface, with inadequate coverage of the underlying design issues of how to structure databases. Growing out of Professor Jonathan Eckstein’s twenty years’ experience teaching courses on management information systems (MIS) at Rutgers Business School, this book fills this gap in the literature by providing a rigorous introduction to relational databases for readers without prior computer science or programming experience. Relational Database Design for Business, with Microsoft Access helps readers to quickly develop a thorough, practical understanding of relational database design. It takes a step-by-step, real-world approach, using application examples from business and finance every step the way. As a result, readers learn to think concretely about database design and how to address issues that commonly arise when developing and manipulating relational databases. By the time they finish the final chapter, students will have the knowledge and skills needed to build relational databases with dozens of tables. They will also be able to build complete Microsoft Access applications around such databases. This text: Takes a hands-on approach using numerous real-world examples drawn from the worlds of business, finance, and more Gets readers up and running, fast, with the skills they need to use and develop relational databases with Microsoft Access Moves swiftly from conceptual fundamentals to advanced design techniques Leads readers step-by-step through data management and design, relational database theory, multiple tables and the possible relationships between them, Microsoft Access features such as forms and navigation, formulating queries in SQL, and normalization Introductory Relational Database Design for Business, with MicrosoftAccess is the definitive guide for undergraduate and graduate students in business, finance, and data analysis without prior experience in database design. While Microsoft Access is its primary “hands-on” learning vehicle, most of the skills in this text are transferrable to other relational database software such as MySQL. |
| data mining for business intelligence rutgers reddit: Handbook Of Financial Econometrics, Mathematics, Statistics, And Machine Learning (In 4 Volumes) Cheng Few Lee, John C Lee, 2020-07-30 This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience. |
| data mining for business intelligence rutgers reddit: Essentials of Business Analytics Bhimasankaram Pochiraju, Sridhar Seshadri, 2019-07-10 This comprehensive edited volume is the first of its kind, designed to serve as a textbook for long-duration business analytics programs. It can also be used as a guide to the field by practitioners. The book has contributions from experts in top universities and industry. The editors have taken extreme care to ensure continuity across the chapters. The material is organized into three parts: A) Tools, B) Models and C) Applications. In Part A, the tools used by business analysts are described in detail. In Part B, these tools are applied to construct models used to solve business problems. Part C contains detailed applications in various functional areas of business and several case studies. Supporting material can be found in the appendices that develop the pre-requisites for the main text. Every chapter has a business orientation. Typically, each chapter begins with the description of business problems that are transformed into data questions; and methodology is developed to solve these questions. Data analysis is conducted using widely used software, the output and results are clearly explained at each stage of development. These are finally transformed into a business solution. The companion website provides examples, data sets and sample code for each chapter. |
| data mining for business intelligence rutgers reddit: Graduate Admissions Essays, Fifth Edition Donald Asher, 2024-07-16 The fully updated fifth edition of the go-to guide for crafting winning essays for any type of graduate program or scholarship, including PhD, master's, MD, JD, Rhodes, and postdocs, with brand-new essays and the latest hot tips and secret techniques. Based on thousands of interviews with successful grad students and admissions officers, Graduate Admissions Essays deconstructs and demystifies the ever-challenging application process for getting into graduate and scholarship programs. The book presents: Sample essays in a comprehensive range of subjects, including some available from no other source: medical residencies, postdocs, elite fellowships, academic autobiographies, and more! The latest on AI, the GRE, and diversity and adversity essays. Detailed strategies that have proven successful for some of the most competitive graduate programs in the country (learn how to beat 1% admissions rates!). How to get strong letters of recommendation, how to get funding when they say they have no funding, and how to appeal for more financial aid. Brand-new sample supplemental application letters, letters to faculty mentors, and letters of continuing interest. Full of Dr. Donald Asher's expert advice, this is the perfect graduate application resource whether you're fresh out of college and eager to get directly into graduate school or decades into your career and looking for a change. |
| data mining for business intelligence rutgers reddit: Organizational Change Laurie Lewis, 2011-03-21 Organizational Change integrates major empirical, theoretical and conceptual approaches to implementing communication in organizational settings. Laurie Lewis ties together the disparate literatures in management, education, organizational sociology, and communication to explore how the practices and processes of communication work in real-world cases of change implementation. Gives a bold and comprehensive overview of communication research and ideas on change and those who bring it about Fills in an important piece of the applied communication puzzle as it relates to organizations Illustrated with student friendly, real life case studies from organizations, including organizational mergers, governmental or nonprofit policy or procedural implementation, or technological innovation Winner of the 2011 Organizational Communication NCA Division Book of the Year |
| data mining for business intelligence rutgers reddit: Mathematics and Statistics for Financial Risk Management Michael B. Miller, 2013-12-31 Mathematics and Statistics for Financial Risk Management is a practical guide to modern financial risk management for both practitioners and academics. Now in its second edition with more topics, more sample problems and more real world examples, this popular guide to financial risk management introduces readers to practical quantitative techniques for analyzing and managing financial risk. In a concise and easy-to-read style, each chapter introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion Web site includes interactive Excel spreadsheet examples and templates. Mathematics and Statistics for Financial Risk Management is an indispensable reference for today’s financial risk professional. |
| data mining for business intelligence rutgers reddit: Data Visualization Made Simple Kristen Sosulski, 2018-09-27 Data Visualization Made Simple is a practical guide to the fundamentals, strategies, and real-world cases for data visualization, an essential skill required in today’s information-rich world. With foundations rooted in statistics, psychology, and computer science, data visualization offers practitioners in almost every field a coherent way to share findings from original research, big data, learning analytics, and more. In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and looks at options for design visualization; and includes cases and interviews to provide a contemporary view of how data graphics are used by professionals across industries Both novices and seasoned designers in education, business, and other areas can use this book’s effective, linear process to develop data visualization literacy and promote exploratory, inquiry-based approaches to visualization problems. |
| data mining for business intelligence rutgers reddit: Operational Risk Management Philippa X. Girling, 2013-10-14 A best practices guide to all of the elements of an effective operational risk framework While many organizations know how important operational risks are, they still continue to struggle with the best ways to identify and manage them. Organizations of all sizes and in all industries need best practices for identifying and managing key operational risks, if they intend on exceling in today's dynamic environment. Operational Risk Management fills this need by providing both the new and experienced operational risk professional with all of the tools and best practices needed to implement a successful operational risk framework. It also provides real-life examples of successful methods and tools you can use while facing the cultural challenges that are prevalent in this field. Contains informative post-mortems on some of the most notorious operational risk events of our time Explores the future of operational risk in the current regulatory environment Written by a recognized global expert on operational risk An effective operational risk framework is essential for today's organizations. This book will put you in a better position to develop one and use it to identify, assess, control, and mitigate any potential risks of this nature. |
| data mining for business intelligence rutgers reddit: Business Statistics David F. Groebner, 2005 This comprehensive text presents descriptive and inferential statistics with an assortment of business examples and real data, and an emphasis on decision-making. The accompanying CD-ROM presents Excel and Minitab tutorials as well as data files for all the exercises and exmaples presented. |
| data mining for business intelligence rutgers reddit: Blown to Bits Harold Abelson, Ken Ledeen, Harry R. Lewis, 2008 'Blown to Bits' is about how the digital explosion is changing everything. The text explains the technology, why it creates so many surprises and why things often don't work the way we expect them to. It is also about things the information explosion is destroying: old assumptions about who is really in control of our lives. |
| data mining for business intelligence rutgers reddit: Security Issues and Privacy Concerns in Industry 4.0 Applications Shibin David, R. S. Anand, V. Jeyakrishnan, M. Niranjanamurthy, 2021-08-24 SECURITY ISSUES AND PRIVACY CONCERNS IN INDUSTRY 4.0 APPLICATIONS Written and edited by a team of international experts, this is the most comprehensive and up-to-date coverage of the security and privacy issues surrounding Industry 4.0 applications, a must-have for any library. The scope of Security Issues and Privacy Concerns in Industry 4.0 Applications is to envision the need for security in Industry 4.0 applications and the research opportunities for the future. This book discusses the security issues in Industry 4.0 applications for research development. It will also enable the reader to develop solutions for the security threats and attacks that prevail in the industry. The chapters will be framed on par with advancements in the industry in the area of Industry 4.0 with its applications in additive manufacturing, cloud computing, IoT (Internet of Things), and many others. This book helps a researcher and an industrial specialist to reflect on the latest trends and the need for technological change in Industry 4.0. Smart water management using IoT, cloud security issues with network forensics, regional language recognition for industry 4.0, IoT-based health care management systems, artificial intelligence for fake profile detection, and packet drop detection in agriculture-based IoT are covered in this outstanding new volume. Leading innovations such as smart drone for railway track cleaning, everyday life-supporting blockchain and big data, effective prediction using machine learning, classification of dog breed based on CNN, load balancing using the SPE approach and cyber culture impact on media consumers are also addressed. Whether a reference for the veteran engineer or an introduction to the technologies covered in the book for the student, this is a must-have for any library. |
| data mining for business intelligence rutgers reddit: Mathematical Statistics with Applications in R Kandethody M. Ramachandran, Chris P. Tsokos, 2014-09-14 Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. - Step-by-step procedure to solve real problems, making the topic more accessible - Exercises blend theory and modern applications - Practical, real-world chapter projects - Provides an optional section in each chapter on using Minitab, SPSS and SAS commands - Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods |
| data mining for business intelligence rutgers reddit: Bayesian Inference in Statistical Analysis George E. P. Box, George C. Tiao, 2011-01-25 Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach. |
| data mining for business intelligence rutgers reddit: Biomass to Biofuels Alain A. Vertes, Nasib Qureshi, Hideaki Yukawa, Hans P. Blaschek, 2011-08-24 Focusing on the key challenges that still impede the realization of the billion-ton renewable fuels vision, this book integrates technological development and business development rationales to highlight the key technological.developments that are necessary to industrialize biofuels on a global scale. Technological issues addressed in this work include fermentation and downstream processing technologies, as compared to current industrial practice and process economics. Business issues that provide the lens through which the technological review is performed span the entire biofuel value chain, from financial mechanisms to fund biotechnology start-ups in the biofuel arena up to large green field manufacturing projects, to raw material farming, collection and transport to the bioconversion plant, manufacturing, product recovery, storage, and transport to the point of sale. Emphasis has been placed throughout the book on providing a global view that takes into account the intrinsic characteristics of various biofuels markets from Brazil, the EU, the US, or Japan, to emerging economies as agricultural development and biofuel development appear undissociably linked. |
| data mining for business intelligence rutgers reddit: Talking to Strangers Malcolm Gladwell, 2019-09-10 Malcolm Gladwell, host of the podcast Revisionist History and author of the #1 New York Times bestseller Outliers, offers a powerful examination of our interactions with strangers and why they often go wrong—now with a new afterword by the author. A Best Book of the Year: The Financial Times, Bloomberg, Chicago Tribune, and Detroit Free Press How did Fidel Castro fool the CIA for a generation? Why did Neville Chamberlain think he could trust Adolf Hitler? Why are campus sexual assaults on the rise? Do television sitcoms teach us something about the way we relate to one another that isn’t true? Talking to Strangers is a classically Gladwellian intellectual adventure, a challenging and controversial excursion through history, psychology, and scandals taken straight from the news. He revisits the deceptions of Bernie Madoff, the trial of Amanda Knox, the suicide of Sylvia Plath, the Jerry Sandusky pedophilia scandal at Penn State University, and the death of Sandra Bland—throwing our understanding of these and other stories into doubt. Something is very wrong, Gladwell argues, with the tools and strategies we use to make sense of people we don’t know. And because we don’t know how to talk to strangers, we are inviting conflict and misunderstanding in ways that have a profound effect on our lives and our world. In his first book since his #1 bestseller David and Goliath, Malcolm Gladwell has written a gripping guidebook for troubled times. |
| data mining for business intelligence rutgers reddit: The Grammar of Graphics Leland Wilkinson, 2013-03-09 Written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. It was designed for a distributed computing environment, with special attention given to conserving computer code and system resources. While the tangible result of this work is a Java production graphics library, the text focuses on the deep structures involved in producing quantitative graphics from data. It investigates the rules that underlie pie charts, bar charts, scatterplots, function plots, maps, mosaics, and radar charts. These rules are abstracted from the work of Bertin, Cleveland, Kosslyn, MacEachren, Pinker, Tufte, Tukey, Tobler, and other theorists of quantitative graphics. |
| data mining for business intelligence rutgers reddit: A Companion to the Gilded Age and Progressive Era Christopher McKnight Nichols, Nancy C. Unger, 2022-06-15 A Companion to the Gilded Age and Progressive Era presents a collection of new historiographic essays covering the years between 1877 and 1920, a period which saw the U.S. emerge from the ashes of Reconstruction to become a world power. The single, definitive resource for the latest state of knowledge relating to the history and historiography of the Gilded Age and Progressive Era Features contributions by leading scholars in a wide range of relevant specialties Coverage of the period includes geographic, social, cultural, economic, political, diplomatic, ethnic, racial, gendered, religious, global, and ecological themes and approaches In today’s era, often referred to as a “second Gilded Age,” this book offers relevant historical analysis of the factors that helped create contemporary society Fills an important chronological gap in period-based American history collections |
| data mining for business intelligence rutgers reddit: Financial Statement Analysis Martin S. Fridson, Fernando Alvarez, 2002-10-01 Praise for Financial Statement Analysis A Practitioner's Guide Third Edition This is an illuminating and insightful tour of financial statements, how they can be used to inform, how they can be used to mislead, and how they can be used to analyze the financial health of a company. -Professor Jay O. Light Harvard Business School Financial Statement Analysis should be required reading for anyone who puts a dime to work in the securities markets or recommends that others do the same. -Jack L. Rivkin Executive Vice President (retired) Citigroup Investments Fridson and Alvarez provide a valuable practical guide for understanding, interpreting, and critically assessing financial reports put out by firms. Their discussion of profits-'quality of earnings'-is particularly insightful given the recent spate of reporting problems encountered by firms. I highly recommend their book to anyone interested in getting behind the numbers as a means of predicting future profits and stock prices. -Paul Brown Chair-Department of Accounting Leonard N. Stern School of Business, NYU Let this book assist in financial awareness and transparency and higher standards of reporting, and accountability to all stakeholders. -Patricia A. Small Treasurer Emeritus, University of California Partner, KCM Investment Advisors This book is a polished gem covering the analysis of financial statements. It is thorough, skeptical and extremely practical in its review. -Daniel J. Fuss Vice Chairman Loomis, Sayles & Company, LP |
| data mining for business intelligence rutgers reddit: The Fearless Organization Amy C. Edmondson, 2018-11-14 Conquer the most essential adaptation to the knowledge economy The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth offers practical guidance for teams and organizations who are serious about success in the modern economy. With so much riding on innovation, creativity, and spark, it is essential to attract and retain quality talent—but what good does this talent do if no one is able to speak their mind? The traditional culture of fitting in and going along spells doom in the knowledge economy. Success requires a continuous influx of new ideas, new challenges, and critical thought, and the interpersonal climate must not suppress, silence, ridicule or intimidate. Not every idea is good, and yes there are stupid questions, and yes dissent can slow things down, but talking through these things is an essential part of the creative process. People must be allowed to voice half-finished thoughts, ask questions from left field, and brainstorm out loud; it creates a culture in which a minor flub or momentary lapse is no big deal, and where actual mistakes are owned and corrected, and where the next left-field idea could be the next big thing. This book explores this culture of psychological safety, and provides a blueprint for bringing it to life. The road is sometimes bumpy, but succinct and informative scenario-based explanations provide a clear path forward to constant learning and healthy innovation. Explore the link between psychological safety and high performance Create a culture where it’s “safe” to express ideas, ask questions, and admit mistakes Nurture the level of engagement and candor required in today’s knowledge economy Follow a step-by-step framework for establishing psychological safety in your team or organization Shed the yes-men approach and step into real performance. Fertilize creativity, clarify goals, achieve accountability, redefine leadership, and much more. The Fearless Organization helps you bring about this most critical transformation. |
| data mining for business intelligence rutgers reddit: Business Statistics for Contemporary Decision Making Ignacio Castillo, Ken Black, Tiffany Bayley, 2023-05-08 Show students why business statistics is an increasingly important business skill through a student-friendly pedagogy. In this fourth Canadian edition of Business Statistics For Contemporary Decision Making authors Ken Black, Tiffany Bayley, and Ignacio Castillo uses current real-world data to equip students with the business analytics techniques and quantitative decision-making skills required to make smart decisions in today's workplace. |
| data mining for business intelligence rutgers reddit: Continuum Scale Simulation of Engineering Materials Dierk Raabe, Franz Roters, Frédéric Barlat, Long-Qing Chen, 2006-03-06 This book fills a gap by presenting our current knowledge and understanding of continuum-based concepts behind computational methods used for microstructure and process simulation of engineering materials above the atomic scale. The volume provides an excellent overview on the different methods, comparing the different methods in terms of their respective particular weaknesses and advantages. This trains readers to identify appropriate approaches to the new challenges that emerge every day in this exciting domain. Divided into three main parts, the first is a basic overview covering fundamental key methods in the field of continuum scale materials simulation. The second one then goes on to look at applications of these methods to the prediction of microstructures, dealing with explicit simulation examples, while the third part discusses example applications in the field of process simulation. By presenting a spectrum of different computational approaches to materials, the book aims to initiate the development of corresponding virtual laboratories in the industry in which these methods are exploited. As such, it addresses graduates and undergraduates, lecturers, materials scientists and engineers, physicists, biologists, chemists, mathematicians, and mechanical engineers. |
| data mining for business intelligence rutgers reddit: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data mining for business intelligence rutgers reddit: A Companion to U.S. Foreign Relations Christopher R. W. Dietrich, 2020-03-04 Covers the entire range of the history of U.S. foreign relations from the colonial period to the beginning of the 21st century. A Companion to U.S. Foreign Relations is an authoritative guide to past and present scholarship on the history of American diplomacy and foreign relations from its seventeenth century origins to the modern day. This two-volume reference work presents a collection of historiographical essays by prominent scholars. The essays explore three centuries of America’s global interactions and the ways U.S. foreign policies have been analyzed and interpreted over time. Scholars offer fresh perspectives on the history of U.S. foreign relations; analyze the causes, influences, and consequences of major foreign policy decisions; and address contemporary debates surrounding the practice of American power. The Companion covers a wide variety of methodologies, integrating political, military, economic, social and cultural history to explore the ideas and events that shaped U.S. diplomacy and foreign relations and continue to influence national identity. The essays discuss topics such as the links between U.S. foreign relations and the study of ideology, race, gender, and religion; Native American history, expansion, and imperialism; industrialization and modernization; domestic and international politics; and the United States’ role in decolonization, globalization, and the Cold War. A comprehensive approach to understanding the history, influences, and drivers of U.S. foreign relation, this indispensable resource: Examines significant foreign policy events and their subsequent interpretations Places key figures and policies in their historical, national, and international contexts Provides background on recent and current debates in U.S. foreign policy Explores the historiography and primary sources for each topic Covers the development of diverse themes and methodologies in histories of U.S. foreign policy Offering scholars, teachers, and students unmatched chronological breadth and analytical depth, A Companion to U.S. Foreign Relations: Colonial Era to the Present is an important contribution to scholarship on the history of America’s interactions with the world. |
| data mining for business intelligence rutgers reddit: A Companion to the History of Science Bernard Lightman, 2019-11-11 The Wiley Blackwell Companion to the History of Science is a single volume companion that discusses the history of science as it is done today, providing a survey of the debates and issues that dominate current scholarly discussion, with contributions from leading international scholars. Provides a single-volume overview of current scholarship in the history of science edited by one of the leading figures in the field Features forty essays by leading international scholars providing an overview of the key debates and developments in the history of science Reflects the shift towards deeper historical contextualization within the field Helps communicate and integrate perspectives from the history of science with other areas of historical inquiry Includes discussion of non-Western themes which are integrated throughout the chapters Divided into four sections based on key analytic categories that reflect new approaches in the field |
| data mining for business intelligence rutgers reddit: Golden Gulag Ruth Wilson Gilmore, 2007-01-08 Since 1980, the number of people in U.S. prisons has increased more than 450%. Despite a crime rate that has been falling steadily for decades, California has led the way in this explosion, with what a state analyst called the biggest prison building project in the history of the world. Golden Gulag provides the first detailed explanation for that buildup by looking at how political and economic forces, ranging from global to local, conjoined to produce the prison boom. In an informed and impassioned account, Ruth Wilson Gilmore examines this issue through statewide, rural, and urban perspectives to explain how the expansion developed from surpluses of finance capital, labor, land, and state capacity. Detailing crises that hit California’s economy with particular ferocity, she argues that defeats of radical struggles, weakening of labor, and shifting patterns of capital investment have been key conditions for prison growth. The results—a vast and expensive prison system, a huge number of incarcerated young people of color, and the increase in punitive justice such as the three strikes law—pose profound and troubling questions for the future of California, the United States, and the world. Golden Gulag provides a rich context for this complex dilemma, and at the same time challenges many cherished assumptions about who benefits and who suffers from the state’s commitment to prison expansion. |
| data mining for business intelligence rutgers reddit: The Scientific Revolution Steven Shapin, 2018-11-05 This scholarly and accessible study presents “a provocative new reading” of the late sixteenth- and seventeenth-century advances in scientific inquiry (Kirkus Reviews). In The Scientific Revolution, historian Steven Shapin challenges the very idea that any such a “revolution” ever took place. Rejecting the narrative that a new and unifying paradigm suddenly took hold, he demonstrates how the conduct of science emerged from a wide array of early modern philosophical agendas, political commitments, and religious beliefs. In this analysis, early modern science is shown not as a set of disembodied ideas, but as historically situated ways of knowing and doing. Shapin shows that every principle identified as the modernizing essence of science—whether it’s experimentalism, mathematical methodology, or a mechanical conception of nature—was in fact contested by sixteenth- and seventeenth-century practitioners with equal claims to modernity. Shapin argues that this contested legacy is nevertheless rightly understood as the origin of modern science, its problems as well as its acknowledged achievements. This updated edition includes a new bibliographic essay featuring the latest scholarship. “An excellent book.” —Anthony Gottlieb, New York Times Book Review |
| data mining for business intelligence rutgers reddit: Ethics for the Information Age Michael Jay Quinn, 2006 Widely praised for its balanced treatment of computer ethics, Ethics for the Information Age offers a modern presentation of the moral controversies surrounding information technology. Topics such as privacy and intellectual property are explored through multiple ethical theories, encouraging readers to think critically about these issues and to make their own ethical decisions. |
| data mining for business intelligence rutgers reddit: Crimes Committed by Terrorist Groups Mark S. Hamm, 2011 This is a print on demand edition of a hard to find publication. Examines terrorists¿ involvement in a variety of crimes ranging from motor vehicle violations, immigration fraud, and mfg. illegal firearms to counterfeiting, armed bank robbery, and smuggling weapons of mass destruction. There are 3 parts: (1) Compares the criminality of internat. jihad groups with domestic right-wing groups. (2) Six case studies of crimes includes trial transcripts, official reports, previous scholarship, and interviews with law enforce. officials and former terrorists are used to explore skills that made crimes possible; or events and lack of skill that the prevented crimes. Includes brief bio. of the terrorists along with descriptions of their org., strategies, and plots. (3) Analysis of the themes in closing arguments of the transcripts in Part 2. Illus. |
| data mining for business intelligence rutgers reddit: Machine Learning for Hackers Drew Conway, John Myles White, 2012-02-13 If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data |
| data mining for business intelligence rutgers reddit: You Are Not So Smart David McRaney, 2012-11-06 Explains how self-delusion is part of a person's psychological defense system, identifying common misconceptions people have on topics such as caffeine withdrawal, hindsight, and brand loyalty. |
| data mining for business intelligence rutgers reddit: Site Fights Daniel P. Aldrich, 2010-03-18 One of the most vexing problems for governments is building controversial facilities that serve the needs of all citizens but have adverse consequences for host communities. Policymakers must decide not only where to locate often unwanted projects but also what methods to use when interacting with opposition groups. In Site Fights, Daniel P. Aldrich gathers quantitative evidence from close to five hundred municipalities across Japan to show that planners deliberately seek out acquiescent and unorganized communities for such facilities in order to minimize conflict. When protests arise over nuclear power plants, dams, and airports, agencies regularly rely on the coercive powers of the modern state, such as land expropriation and police repression. Only under pressure from civil society do policymakers move toward financial incentives and public relations campaigns. Through fieldwork and interviews with bureaucrats and activists, Aldrich illustrates these dynamics with case studies from Japan, France, and the United States. The incidents highlighted in Site Fights stress the importance of developing engaged civil society even in the absence of crisis, thereby making communities both less attractive to planners of controversial projects and more effective at resisting future threats. |
| data mining for business intelligence rutgers reddit: Linear Models with R Julian J. Faraway, 2016-04-19 A Hands-On Way to Learning Data AnalysisPart of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Second Edition explains how to use linear models |
| data mining for business intelligence rutgers reddit: A Companion to the History of American Broadcasting Aniko Bodroghkozy, 2018-10-02 Presented in a single volume, this engaging review reflects on the scholarship and the historical development of American broadcasting A Companion to the History of American Broadcasting comprehensively evaluates the vibrant history of American radio and television and reveals broadcasting’s influence on American history in the twentieth and twenty-first centuries. With contributions from leading scholars on the topic, this wide-ranging anthology explores the impact of broadcasting on American culture, politics, and society from an historical perspective as well as the effect on our economic and social structures. The text’s original and accessibly-written essays offer explorations on a wealth of topics including the production of broadcast media, the evolution of various television and radio genres, the development of the broadcast ratings system, the rise of Spanish language broadcasting in the United States, broadcast activism, African Americans and broadcasting, 1950’s television, and much more. This essential resource: Presents a scholarly overview of the history of radio and television broadcasting and its influence on contemporary American history Contains original essays from leading academics in the field Examines the role of radio in the television era Discusses the evolution of regulations in radio and television Offers insight into the cultural influence of radio and television Analyzes canonical texts that helped shape the field Written for students and scholars of media studies and twentieth-century history, A Companion to the History of American Broadcasting is an essential and field-defining guide to the history and historiography of American broadcasting and its many cultural, societal, and political impacts. |
| data mining for business intelligence rutgers reddit: Future Shock Alvin Toffler, 2022-01-11 NEW YORK TIMES BESTSELLER • The classic work that predicted the anxieties of a world upended by rapidly emerging technologies—and now provides a road map to solving many of our most pressing crises. “Explosive . . . brilliantly formulated.” —The Wall Street Journal Future Shock is the classic that changed our view of tomorrow. Its startling insights into accelerating change led a president to ask his advisers for a special report, inspired composers to write symphonies and rock music, gave a powerful new concept to social science, and added a phrase to our language. Published in over fifty countries, Future Shock is the most important study of change and adaptation in our time. In many ways, Future Shock is about the present. It is about what is happening today to people and groups who are overwhelmed by change. Change affects our products, communities, organizations—even our patterns of friendship and love. But Future Shock also illuminates the world of tomorrow by exploding countless clichés about today. It vividly describes the emerging global civilization: the rise of new businesses, subcultures, lifestyles, and human relationships—all of them temporary. Future Shock will intrigue, provoke, frighten, encourage, and, above all, change everyone who reads it. |
| data mining for business intelligence rutgers reddit: The Polygraph and Lie Detection National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee to Review the Scientific Evidence on the Polygraph, 2003-01-22 The polygraph, often portrayed as a magic mind-reading machine, is still controversial among experts, who continue heated debates about its validity as a lie-detecting device. As the nation takes a fresh look at ways to enhance its security, can the polygraph be considered a useful tool? The Polygraph and Lie Detection puts the polygraph itself to the test, reviewing and analyzing data about its use in criminal investigation, employment screening, and counter-intelligence. The book looks at: The theory of how the polygraph works and evidence about how deceptivenessâ€and other psychological conditionsâ€affect the physiological responses that the polygraph measures. Empirical evidence on the performance of the polygraph and the success of subjects' countermeasures. The actual use of the polygraph in the arena of national security, including its role in deterring threats to security. The book addresses the difficulties of measuring polygraph accuracy, the usefulness of the technique for aiding interrogation and for deterrence, and includes potential alternativesâ€such as voice-stress analysis and brain measurement techniques. |
| data mining for business intelligence rutgers reddit: Machine Learning in Action Peter Harrington, 2012-04-03 Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce |
| data mining for business intelligence rutgers reddit: Machine Learning in Python Michael Bowles, 2015-04-27 Learn a simpler and more effective way to analyze data and predict outcomes with Python Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language. Predict outcomes using linear and ensemble algorithm families Build predictive models that solve a range of simple and complex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
