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| carnegie mellon masters data science: The Data Science Handbook Field Cady, 2017-02-28 A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon. |
| carnegie mellon masters data science: Business Trends in Practice Bernard Marr, 2021-11-15 WINNER OF THE BUSINESS BOOK OF THE YEAR AWARD 2022! Stay one step ahead of the competition with this expert review of the most impactful and disruptive business trends coming down the pike Far from slowing down, change and transformation in business seems to come only at a more and more furious rate. The last ten years alone have seen the introduction of groundbreaking new trends that pose new opportunities and challenges for leaders in all industries. In Business Trends in Practice: The 25+ Trends That Are Redefining Organizations, best-selling business author and strategist Bernard Marr breaks down the social and technological forces underlying these rapidly advancing changes and the impact of those changes on key industries. Critical consumer trends just emerging today—or poised to emerge tomorrow—are discussed, as are strategies for rethinking your organisation’s product and service delivery. The book also explores: Crucial business operations trends that are changing the way companies conduct themselves in the 21st century The practical insights and takeaways you can glean from technological and social innovation when you cut through the hype Disruptive new technologies, including AI, robotic and business process automation, remote work, as well as social and environmental sustainability trends Business Trends in Practice: The 25+ Trends That Are Redefining Organizations is a must-read resource for executives, business leaders and managers, and business development and innovation leads trying to get – and stay – on top of changes and disruptions that are right around the corner. |
| carnegie mellon masters data science: e-Learning and the Science of Instruction Ruth C. Clark, Richard E. Mayer, 2016-02-19 The essential e-learning design manual, updated with the latest research, design principles, and examples e-Learning and the Science of Instruction is the ultimate handbook for evidence-based e-learning design. Since the first edition of this book, e-learning has grown to account for at least 40% of all training delivery media. However, digital courses often fail to reach their potential for learning effectiveness and efficiency. This guide provides research-based guidelines on how best to present content with text, graphics, and audio as well as the conditions under which those guidelines are most effective. This updated fourth edition describes the guidelines, psychology, and applications for ways to improve learning through personalization techniques, coherence, animations, and a new chapter on evidence-based game design. The chapter on the Cognitive Theory of Multimedia Learning introduces three forms of cognitive load which are revisited throughout each chapter as the psychological basis for chapter principles. A new chapter on engagement in learning lays the groundwork for in-depth reviews of how to leverage worked examples, practice, online collaboration, and learner control to optimize learning. The updated instructor's materials include a syllabus, assignments, storyboard projects, and test items that you can adapt to your own course schedule and students. Co-authored by the most productive instructional research scientist in the world, Dr. Richard E. Mayer, this book distills copious e-learning research into a practical manual for improving learning through optimal design and delivery. Get up to date on the latest e-learning research Adopt best practices for communicating information effectively Use evidence-based techniques to engage your learners Replace popular instructional ideas, such as learning styles with evidence-based guidelines Apply evidence-based design techniques to optimize learning games e-Learning continues to grow as an alternative or adjunct to the classroom, and correspondingly, has become a focus among researchers in learning-related fields. New findings from research laboratories can inform the design and development of e-learning. However, much of this research published in technical journals is inaccessible to those who actually design e-learning material. By collecting the latest evidence into a single volume and translating the theoretical into the practical, e-Learning and the Science of Instruction has become an essential resource for consumers and designers of multimedia learning. |
| carnegie mellon masters data science: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| carnegie mellon masters data science: Data Scientist Zacharias Voulgaris, 2014 Learn what a data scientist is and how to become one. As our society transforms into a data-driven one, the role of the Data Scientist is becoming more and more important. If you want to be on the leading edge of what is sure to become a major profession in the not-too-distant future, this book can show you how. Each chapter is filled with practical information that will help you reap the fruits of big data and become a successful Data Scientist: Learn what big data is and how it differs from traditional data through its main characteristics: volume, variety, velocity, and veracity. Explore the different types of Data Scientists and the skillset each one has. Dig into what the role of the Data Scientist requires in terms of the relevant mindset, technical skills, experience, and how the Data Scientist connects with other people. Be a Data Scientist for a day, examining the problems you may encounter and how you tackle them, what programs you use, and how you expand your knowledge and know-how. See how you can become a Data Scientist, based on where you are starting from: a programming, machine learning, or data-related background. Follow step-by-step through the process of landing a Data Scientist job: where you need to look, how you would present yourself to a potential employer, and what it takes to follow a freelancer path. Read the case studies of experienced, senior-level Data Scientists, in an attempt to get a better perspective of what this role is, in practice. At the end of the book, there is a glossary of the most important terms that have been introduced, as well as three appendices - a list of useful sites, some relevant articles on the web, and a list of offline resources for further reading. |
| carnegie mellon masters data science: Foundations of Machine Learning, second edition Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar, 2018-12-25 A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition. |
| carnegie mellon masters data science: How Learning Works Susan A. Ambrose, Michael W. Bridges, Michele DiPietro, Marsha C. Lovett, Marie K. Norman, 2010-04-16 Praise for How Learning Works How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning. —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching. —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues. —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book. —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning |
| carnegie mellon masters data science: The Last Lecture Randy Pausch, Jeffrey Zaslow, 2010 The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family. |
| carnegie mellon masters data science: Analytics and Knowledge Management Suliman Hawamdeh, Hsia-Ching Chang, 2018-08-06 The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms. |
| carnegie mellon masters data science: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| carnegie mellon masters data science: Beginning Data Science with R Manas A. Pathak, 2014-12-08 “We live in the age of data. In the last few years, the methodology of extracting insights from data or data science has emerged as a discipline in its own right. The R programming language has become one-stop solution for all types of data analysis. The growing popularity of R is due its statistical roots and a vast open source package library. The goal of “Beginning Data Science with R” is to introduce the readers to some of the useful data science techniques and their implementation with the R programming language. The book attempts to strike a balance between the how: specific processes and methodologies, and understanding the why: going over the intuition behind how a particular technique works, so that the reader can apply it to the problem at hand. This book will be useful for readers who are not familiar with statistics and the R programming language. |
| carnegie mellon masters data science: Big Data Analytics Kim H. Pries, Robert Dunnigan, 2015-02-05 With this book, managers and decision makers are given the tools to make more informed decisions about big data purchasing initiatives. Big Data Analytics: A Practical Guide for Managers not only supplies descriptions of common tools, but also surveys the various products and vendors that supply the big data market.Comparing and contrasting the dif |
| carnegie mellon masters data science: Capital Ideas Evolving Peter L. Bernstein, 2011-01-31 A lot has happened in the financial markets since 1992, when Peter Bernstein wrote his seminal Capital Ideas. Happily, Peter has taken up his facile pen again to describe these changes, a virtual revolution in the practice of investing that relies heavily on complex mathematics, derivatives, hedging, and hyperactive trading. This fine and eminently readable book is unlikely to be surpassed as the definitive chronicle of a truly historic era. John C. Bogle, founder of The Vanguard Group and author, The Little Book of Common Sense Investing Just as Dante could not have understood or survived the perils of the Inferno without Virgil to guide him, investors today need Peter Bernstein to help find their way across dark and shifting ground. No one alive understands Wall Street's intellectual history better, and that makes Bernstein our best and wisest guide to the future. He is the only person who could have written this book; thank goodness he did. Jason Zweig, Investing Columnist, Money magazine Another must-read from Peter Bernstein! This well-written and thought-provoking book provides valuable insights on how key finance theories have evolved from their ivory tower formulation to profitable application by portfolio managers. This book will certainly be read with keen interest by, and undoubtedly influence, a wide range of participants in international finance. Dr. Mohamed A. El-Erian, President and CEO of Harvard Management Company, Deputy Treasurer of Harvard University, and member of the faculty of the Harvard Business School Reading Capital Ideas Evolving is an experience not to be missed. Peter Bernstein's knowledge of the principal characters-the giants in the development of investment theory and practice-brings this subject to life. Linda B. Strumpf, Vice President and Chief Investment Officer, The Ford Foundation With great clarity, Peter Bernstein introduces us to the insights of investment giants, and explains how they transformed financial theory into portfolio practice. This is not just a tale of money and models; it is a fascinating and contemporary story about people and the power of their ideas. Elroy Dimson, BGI Professor of Investment Management, London Business School Capital Ideas Evolving provides us with a unique appreciation for the pervasive impact that the theory of modern finance has had on the development of our capital markets. Peter Bernstein once again has produced a masterpiece that is must reading for practitioners, educators and students of finance. Andr F. Perold, Professor of Finance, Harvard Business School |
| carnegie mellon masters data science: Java Illuminated Julie Anderson, Hervé Franceschi, 2012 With a variety of interactive learning features and user-friendly pedagogy, the Third Edition provides a comprehensive introduction to programming using the most current version of Java. Throughout the text the authors incorporate an active learning approach which asks students to take an active role in their understanding of the language through the use of numerous interactive examples, exercises, and projects. Object-oriented programming concepts are developed progressively and reinforced through numerous Programming Activities, allowing students to fully understand and implement both basic and sophisticated techniques. In response to students growing interest in animation and visualization the text includes techniques for producing graphical output and animations beginning in Chapter 4 with applets and continuing throughout the text. You will find Java Illuminated, Third Edition comprehensive and user-friendly. Students will find it exciting to delve into the world of programming with hands-on, real-world applications!New to the Third Edition:-Includes NEW examples and projects throughout-Every NEW copy of the text includes a CD-ROM with the following: *programming activity framework code*full example code from each chapter*browser-based modules with visual step-by-step demonstrations of code execution*links to popular integrated development environments and the Java Standard Edition JDK-Every new copy includes full student access to TuringsCraft Custome CodeLab. Customized to match the organization of this textbook, CodeLab provides over 300 short hands-on programming exercises with immediate feedback.Instructor Resources: Test Bank, PowerPoint Lecture Outlines, Solutions to Programming Activities in text, and Answers to the chapter exercisesAlso available:Java Illuminated: Brief Edition, Third Edition (ISBN-13: 978-1-4496-3202-1). This Brief Edition is suitable for the one-term introductory course. |
| carnegie mellon masters data science: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-21 Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. How to lead in data science shares unique leadership techniques from high-performance data teams. It's filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You'll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you'll build practical skills to grow and improve your team, your company's data culture, and yourself. |
| carnegie mellon masters data science: Machine Learning Jaime Guillermo Carbonell, 1989 |
| carnegie mellon masters data science: Bayesian Data Analysis, Third Edition Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 2013-11-01 Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page. |
| carnegie mellon masters data science: Oh You Robot Saints! Rebecca Morgan Frank, 2021-02-27 Part bestiary, part litany, part elegy, Rebecca Morgan Frank's Oh You Robot Saints! is populated by a strange menagerie of early automata and robots, including octobots and an eighteenth-century digesting duck, set alongside medieval mechanical virgins and robot priests. From a riveting robobee sonnet sequence that links weapons of war and industrial fixes for infertility to a microdrama sketching out a missing Sophocles play on the mythical bronze man, Talos, these muscular poems blur and sing the lines between machines and the divine. This lyrical exploration of the ongoing human desire to create life navigates wonder and grief, joining the uncanny investigation of what it is to be, to make, and to be made. |
| carnegie mellon masters data science: M-Commerce Norman Sadeh, 2003-01-03 The first complete introduction to the technology and business issues surrounding m-commerce With the number of mobile phone users fast approaching the one billion mark, it is clear that mobile e-commerce (a.k.a. m-commerce) is the next business frontier. Authored by a recognized international authority in the field, this book describes the brave new world of m-commerce for technical and business managers alike. Readers learn about the driving forces behind m-commerce, the impact of WAP, 3G, mobile payment, and emerging location-sensitive and context-aware technologies. A comprehensive look at emerging m-commerce services and business models, as well as the changing role of mobile network operators, content providers, and other key players. The author concludes with informed predictions about the future of m-commerce. |
| carnegie mellon masters data science: What It Feels Like Stephanie R. Larson, 2021-07-15 Winner of the 2022 Association for the Rhetoric of Science, Technology, and Medicine (ARSTM) Book Award Winner of the 2022 Winifred Bryan Horner Outstanding Book Award from the Coalition of Feminist Scholars in the History of Rhetoric and Composition What It Feels Like interrogates an underexamined reason for our failure to abolish rape in the United States: the way we communicate about it. Using affective and feminist materialist approaches to rhetorical criticism, Stephanie Larson examines how discourses about rape and sexual assault rely on strategies of containment, denying the felt experiences of victims and ultimately stalling broader claims for justice. Investigating anti-pornography debates from the 1980s, Violence Against Women Act advocacy materials, sexual assault forensic kits, public performances, and the #MeToo movement, Larson reveals how our language privileges male perspectives and, more deeply, how it is shaped by systems of power—patriarchy, white supremacy, ableism, and heteronormativity. Interrogating how these systems work to propagate masculine commitments to “science” and “hard evidence,” Larson finds that US culture holds a general mistrust of testimony by women, stereotyping it as “emotional.” But she also gives us hope for change, arguing that testimonies grounded in the bodily, material expression of violation are necessary for giving voice to victims of sexual violence and presenting, accurately, the scale of these crimes. Larson makes a case for visceral rhetorics, theorizing them as powerful forms of communication and persuasion. Demonstrating the communicative power of bodily feeling, Larson challenges the long-held commitment to detached, distant, rationalized discourses of sexual harassment and rape. Timely and poignant, the book offers a much-needed corrective to our legal and political discourses. |
| carnegie mellon masters data science: Big Data Is Not a Monolith Cassidy R. Sugimoto, Hamid R. Ekbia, Michael Mattioli, 2016-10-21 Perspectives on the varied challenges posed by big data for health, science, law, commerce, and politics. Big data is ubiquitous but heterogeneous. Big data can be used to tally clicks and traffic on web pages, find patterns in stock trades, track consumer preferences, identify linguistic correlations in large corpuses of texts. This book examines big data not as an undifferentiated whole but contextually, investigating the varied challenges posed by big data for health, science, law, commerce, and politics. Taken together, the chapters reveal a complex set of problems, practices, and policies. The advent of big data methodologies has challenged the theory-driven approach to scientific knowledge in favor of a data-driven one. Social media platforms and self-tracking tools change the way we see ourselves and others. The collection of data by corporations and government threatens privacy while promoting transparency. Meanwhile, politicians, policy makers, and ethicists are ill-prepared to deal with big data's ramifications. The contributors look at big data's effect on individuals as it exerts social control through monitoring, mining, and manipulation; big data and society, examining both its empowering and its constraining effects; big data and science, considering issues of data governance, provenance, reuse, and trust; and big data and organizations, discussing data responsibility, “data harm,” and decision making. Contributors Ryan Abbott, Cristina Alaimo, Kent R. Anderson, Mark Andrejevic, Diane E. Bailey, Mike Bailey, Mark Burdon, Fred H. Cate, Jorge L. Contreras, Simon DeDeo, Hamid R. Ekbia, Allison Goodwell, Jannis Kallinikos, Inna Kouper, M. Lynne Markus, Michael Mattioli, Paul Ohm, Scott Peppet, Beth Plale, Jason Portenoy, Julie Rennecker, Katie Shilton, Dan Sholler, Cassidy R. Sugimoto, Isuru Suriarachchi, Jevin D. West |
| carnegie mellon masters data science: The Complete Guide to Capital Markets for Quantitative Professionals Alex Kuznetsov, 2006-11-22 The Complete Guide to Capital Markets for Quantitative Professionals is a comprehensive resource for readers with a background in science and technology who want to transfer their skills to the financial industry. It is written in a clear, conversational style and requires no prior knowledge of either finance or financial analytics. The book begins by discussing the operation of the financial industry and the business models of different types of Wall Street firms, as well as the job roles those with technical backgrounds can fill in those firms. Then it describes the mechanics of how these firms make money trading the main financial markets (focusing on fixed income, but also covering equity, options and derivatives markets), and highlights the ways in which quantitative professionals can participate in this money-making process. The second half focuses on the main areas of Wall Street technology and explains how financial models and systems are created, implemented, and used in real life. This is one of the few books that offers a review of relevant literature and Internet resources. |
| carnegie mellon masters data science: Proceedings of the international conference on Machine Learning John Anderson, E. R. Bareiss, Ryszard Stanisław Michalski, 19?? |
| carnegie mellon masters data science: She Plays to Win Prabhleen Kaur Lamba, 2021-11-30 |
| carnegie mellon masters data science: Web and Network Data Science Thomas W. Miller, 2015 Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University's prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems. |
| carnegie mellon masters data science: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| carnegie mellon masters data science: Building Machine Learning Pipelines Hannes Hapke, Catherine Nelson, 2020-07-13 Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques |
| carnegie mellon masters data science: Projective Ecologies Chris Reed, Nina-Marie Lister, 2020-04-30 The past two decades have witnessed a resurgence of ecological ideas and ecological thinking in discussions of urbanism, society, culture, and design. The field of ecology has moved from classical determinism and a reductionist Newtonian concern with stability, certainty, and order in favor of more contemporary understandings of dynamic systemic change and the related phenomena of adaptability, resilience, and flexibility. But ecology is not simply a project of the natural sciences. Researchers, theorists, social commentators, and designers have all used ecology as a broader idea or metaphor for a set of conditions and relationships with political, economic, and social implications. Projective Ecologies takes stock of the diversity of contemporary ecological research and theory--embracing Felix Guattari's broader definition of ecology as at once environmental, social, and existential--and speculates on potential paths forward for design practices. Where are ecological thinking and theory now? What do current trajectories of research suggest for future practice? How can advances in ecological research and modeling, in social theory, and in digital visualization inform, with greater rigor, more robust design thinking and practice? How does all of this point to potential paths forward in an age of climate change and the need for adaptation and mitigation? With Contributions of: Jesse M. Keenan, foreword to the second edition Charles Waldheim, foreword to the first edition James Corner Christopher Hight C.S. Holling and M.A. Goldberg Wenche E. Dramstad, James D. Olson, and Richard T.T. Forman Daniel Botkin Erle C. Ellis Jane Wolff Robert E. Cook Peter Del Tredici David Fletcher Frances Westley and Katharine McGowan Sean Lally Sanford Kwinter |
| carnegie mellon masters data science: Learning Machine Translation Cyril Goutte, Nicola Cancedda, Marc Dymetman, George Foster, 2009 How Machine Learning can improve machine translation: enabling technologies and new statistical techniques. |
| carnegie mellon masters data science: Envisioning the Data Science Discipline National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-03-05 The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education. |
| carnegie mellon masters data science: Data Science: From Research to Application Mahdi Bohlouli, Bahram Sadeghi Bigham, Zahra Narimani, Mahdi Vasighi, Ebrahim Ansari, 2020-01-28 This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well as Internet of Things in the context of Industry 4.0. Data science is a rapidly growing field and, as a profession, incorporates a wide variety of areas, from statistics, mathematics and machine learning, to applied big data analytics. According to Forbes magazine, “Data Science” was listed as LinkedIn’s fastest-growing job in 2017. This book presents selected papers from the International Conference on Contemporary Issues in Data Science (CiDaS 2019), a professional data science event that provided a real workshop (not “listen-shop”) where scientists and scholars had the chance to share ideas, form new collaborations, and brainstorm on major challenges; and where industry experts could catch up on emerging solutions to help solve their concrete data science problems. Given its scope, the book will benefit not only data scientists and scientists from other domains, but also industry experts, policymakers and politicians. |
| carnegie mellon masters data science: Computer Systems Randal E.. Bryant, David Richard O'Hallaron, 2013-07-23 For Computer Systems, Computer Organization and Architecture courses in CS, EE, and ECE departments. Few students studying computer science or computer engineering will ever have the opportunity to build a computer system. On the other hand, most students will be required to use and program computers on a near daily basis. Computer Systems: A Programmer's Perspective introduces the important and enduring concepts that underlie computer systems by showing how these ideas affect the correctness, performance, and utility of application programs. The text's hands-on approach (including a comprehensive set of labs) helps students understand the under-the-hood operation of a modern computer system and prepares them for future courses in systems topics such as compilers, computer architecture, operating systems, and networking. |
| carnegie mellon masters data science: Cracking the Data Science Interview Leondra R. Gonzalez, Aaren Stubberfield, 2024-02-29 Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much more Key Features Acquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learning Gain the confidence to explain complex statistical, machine learning, and deep learning theory Extend your expertise beyond model development with version control, shell scripting, and model deployment fundamentals Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learn Explore data science trends, job demands, and potential career paths Secure interviews with industry-standard resume and portfolio tips Practice data manipulation with Python and SQL Learn about supervised and unsupervised machine learning models Master deep learning components such as backpropagation and activation functions Enhance your productivity by implementing code versioning through Git Streamline workflows using shell scripting for increased efficiency Who this book is for Whether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews. |
| carnegie mellon masters data science: Streaming, Sharing, Stealing Michael D. Smith, Rahul Telang, 2017-08-25 How big data is transforming the creative industries, and how those industries can use lessons from Netflix, Amazon, and Apple to fight back. “[The authors explain] gently yet firmly exactly how the internet threatens established ways and what can and cannot be done about it. Their book should be required for anyone who wishes to believe that nothing much has changed.” —The Wall Street Journal “Packed with examples, from the nimble-footed who reacted quickly to adapt their businesses, to laggards who lost empires.” —Financial Times Traditional network television programming has always followed the same script: executives approve a pilot, order a trial number of episodes, and broadcast them, expecting viewers to watch a given show on their television sets at the same time every week. But then came Netflix's House of Cards. Netflix gauged the show's potential from data it had gathered about subscribers' preferences, ordered two seasons without seeing a pilot, and uploaded the first thirteen episodes all at once for viewers to watch whenever they wanted on the devices of their choice. In this book, Michael Smith and Rahul Telang, experts on entertainment analytics, show how the success of House of Cards upended the film and TV industries—and how companies like Amazon and Apple are changing the rules in other entertainment industries, notably publishing and music. We're living through a period of unprecedented technological disruption in the entertainment industries. Just about everything is affected: pricing, production, distribution, piracy. Smith and Telang discuss niche products and the long tail, product differentiation, price discrimination, and incentives for users not to steal content. To survive and succeed, businesses have to adapt rapidly and creatively. Smith and Telang explain how. How can companies discover who their customers are, what they want, and how much they are willing to pay for it? Data. The entertainment industries, must learn to play a little “moneyball.” The bottom line: follow the data. |
| carnegie mellon masters data science: Computational Statistics in Data Science Richard A. Levine, Walter W. Piegorsch, Hao Helen Zhang, Thomas C. M. Lee, 2022-03-23 Ein unverzichtbarer Leitfaden bei der Anwendung computergestützter Statistik in der modernen Datenwissenschaft In Computational Statistics in Data Science präsentiert ein Team aus bekannten Mathematikern und Statistikern eine fundierte Zusammenstellung von Konzepten, Theorien, Techniken und Praktiken der computergestützten Statistik für ein Publikum, das auf der Suche nach einem einzigen, umfassenden Referenzwerk für Statistik in der modernen Datenwissenschaft ist. Das Buch enthält etliche Kapitel zu den wesentlichen konkreten Bereichen der computergestützten Statistik, in denen modernste Techniken zeitgemäß und verständlich dargestellt werden. Darüber hinaus bietet Computational Statistics in Data Science einen kostenlosen Zugang zu den fertigen Einträgen im Online-Nachschlagewerk Wiley StatsRef: Statistics Reference Online. Außerdem erhalten die Leserinnen und Leser: * Eine gründliche Einführung in die computergestützte Statistik mit relevanten und verständlichen Informationen für Anwender und Forscher in verschiedenen datenintensiven Bereichen * Umfassende Erläuterungen zu aktuellen Themen in der Statistik, darunter Big Data, Datenstromverarbeitung, quantitative Visualisierung und Deep Learning Das Werk eignet sich perfekt für Forscher und Wissenschaftler sämtlicher Fachbereiche, die Techniken der computergestützten Statistik auf einem gehobenen oder fortgeschrittenen Niveau anwenden müssen. Zudem gehört Computational Statistics in Data Science in das Bücherregal von Wissenschaftlern, die sich mit der Erforschung und Entwicklung von Techniken der computergestützten Statistik und statistischen Grafiken beschäftigen. |
| carnegie mellon masters data science: Big Data-Enabled Nursing Connie W. Delaney, Charlotte A. Weaver, Judith J. Warren, Thomas R. Clancy, Roy L. Simpson, 2017-11-02 Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. This text reflects how the learning health system infrastructure is maturing, and being advanced by health information exchanges (HIEs) with multiple organizations blending their data, or enabling distributed computing. It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors. |
| carnegie mellon masters data science: Leadership in Statistics and Data Science Amanda L. Golbeck, 2021-03-22 This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture. |
| carnegie mellon masters data science: Computational Discrete Mathematics Helmut Alt, 2003-06-30 This book is based on a graduate education program on computational discrete mathematics run for several years in Berlin, Germany, as a joint effort of theoretical computer scientists and mathematicians in order to support doctoral students and advanced ongoing education in the field of discrete mathematics and algorithmics. The 12 selected lectures by leading researchers presented in this book provide recent research results and advanced topics in a coherent and consolidated way. Among the areas covered are combinatorics, graph theory, coding theory, discrete and computational geometry, optimization, and algorithmic aspects of algebra. |
| carnegie mellon masters data science: Learning Analytics in Education David Niemi, Roy D. Pea, Bror Saxberg, Richard E. Clark, 2018-08-01 This book provides a comprehensive introduction by an extraordinary range of experts to the recent and rapidly developing field of learning analytics. Some of the finest current thinkers about ways to interpret and benefit from the increasing amount of evidence from learners’ experiences have taken time to explain their methods, describe examples, and point out new underpinnings for the field. Together, they show how this new field has the potential to dramatically increase learner success through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. Learning analytics is much more than “analyzing learning data”—it is about deeply understanding what learning activities work well, for whom, and when. Learning Analytics in Education provides an essential framework, as well as guidance and examples, for a wide range of professionals interested in the future of learning. If you are already involved in learning analytics, or otherwise trying to use an increasing density of evidence to understand learners’ progress, these leading thinkers in the field may give you new insights. If you are engaged in teaching at any level, or training future teachers/faculty for this new, increasingly technology-enhanced learning world, and want some sense of the potential opportunities (and pitfalls) of what technology can bring to your teaching and students, these forward-thinking leaders can spark your imagination. If you are involved in research around uses of technology, improving learning measurements, better ways to use evidence to improve learning, or in more deeply understanding human learning itself, you will find additional ideas and insights from some of the best thinkers in the field here. If you are involved in making administrative or policy decisions about learning, you will find new ideas (and dilemmas) coming your way from inevitable changes in how we design and deliver instruction, how we measure the outcomes, and how we provide feedback to students, teachers, developers, administrators, and policy-makers. For all these players, the trick will be to get the most out of all the new developments to efficiently and effectively improve learning performance, without getting distracted by “shiny” technologies that are disconnected from how human learning and development actually work. |
| carnegie mellon masters data science: Adaptive Educational Technologies for Literacy Instruction Scott A. Crossley, Danielle S. McNamara, 2016-06-17 While current educational technologies have the potential to fundamentally enhance literacy education, many of these tools remain unknown to or unused by today’s practitioners due to a lack of access and support. Adaptive Educational Technologies for Literacy Instruction presents actionable information to educators, administrators, and researchers about available educational technologies that provide adaptive, personalized literacy instruction to students of all ages. These accessible, comprehensive chapters, written by leading researchers who have developed systems and strategies for classrooms, introduce effective technologies for reading comprehension and writing skills. |
Andrew Carnegie - Wikipedia
Andrew Carnegie (English: / kɑːrˈnɛɡi / kar-NEG-ee, Scots: [kɑrˈnɛːɡi]; [2][3][note 1] November 25, 1835 – August 11, 1919) was a Scottish-American industrialist and philanthropist. Carnegie …
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Carnegie Fabrics | Sustainable & High Performance Textiles
Carnegie designs and manufactures a suite of fully-customizable, remarkably effective, and radically sustainable acoustic solutions that will help keep the noise down and style factor up …
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Carnegie Learning is an innovative education technology and curriculum solutions provider for K-12 math, literacy & ELA, world languages, and more.
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The Carnegie Endowment for International Peace generates strategic ideas and independent analysis, supports diplomacy, and trains the next generation of scholar-practitioners to help …
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Brief descriptions of each board-approved grant are provided below. The latest edition of Carnegie’s flagship magazine examines what is driving division in our society and how …
Andrew Carnegie | Biography, Company, Steel, Philanthropy, …
May 23, 2025 · Andrew Carnegie (born November 25, 1835, Dunfermline, Fife, Scotland—died August 11, 1919, Lenox, Massachusetts, U.S.) was a Scottish-born American industrialist who …
Andrew Carnegie's Story
Andrew Carnegie (1835–1919) was among the most famous and wealthy industrialists of his day. Through the Carnegie Corporation of New York, the innovative philanthropic foundation he …
Carnegie China | Carnegie Endowment for International Peace
Carnegie China is an East Asia-based research center focused on China’s regional and global role. Our scholars conduct research and analysis, and convene an array of activities with and …
Our History - Carnegie Corporation of New York
Carnegie Corporation of New York, which Andrew Carnegie (1835–1919) established in 1911 “to promote the advancement and diffusion of knowledge and understanding,” is one of the oldest …
Andrew Carnegie - Wikipedia
Andrew Carnegie (English: / kɑːrˈnɛɡi / kar-NEG-ee, Scots: [kɑrˈnɛːɡi]; [2][3][note 1] November 25, 1835 – August 11, 1919) was a Scottish-American industrialist and philanthropist. Carnegie …
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Apr 1, 2019 · Sign in to My CL to access Carnegie Learning's MATHia Software, Teacher's Toolkit or Educator, Parent, or Student Resource Center using this login page.
Carnegie Fabrics | Sustainable & High Performance Textiles
Carnegie designs and manufactures a suite of fully-customizable, remarkably effective, and radically sustainable acoustic solutions that will help keep the noise down and style factor up …
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Carnegie Learning is an innovative education technology and curriculum solutions provider for K-12 math, literacy & ELA, world languages, and more.
Carnegie Endowment for International Peace
The Carnegie Endowment for International Peace generates strategic ideas and independent analysis, supports diplomacy, and trains the next generation of scholar-practitioners to help …
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Brief descriptions of each board-approved grant are provided below. The latest edition of Carnegie’s flagship magazine examines what is driving division in our society and how …
Andrew Carnegie | Biography, Company, Steel, Philanthropy, …
May 23, 2025 · Andrew Carnegie (born November 25, 1835, Dunfermline, Fife, Scotland—died August 11, 1919, Lenox, Massachusetts, U.S.) was a Scottish-born American industrialist who …
Andrew Carnegie's Story
Andrew Carnegie (1835–1919) was among the most famous and wealthy industrialists of his day. Through the Carnegie Corporation of New York, the innovative philanthropic foundation he …
Carnegie China | Carnegie Endowment for International Peace
Carnegie China is an East Asia-based research center focused on China’s regional and global role. Our scholars conduct research and analysis, and convene an array of activities with and …
Our History - Carnegie Corporation of New York
Carnegie Corporation of New York, which Andrew Carnegie (1835–1919) established in 1911 “to promote the advancement and diffusion of knowledge and understanding,” is one of the oldest …
