Advertisement
| cornell master data science: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| cornell master data science: Big Data Science in Finance Irene Aldridge, Marco Avellaneda, 2021-01-08 Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners. |
| cornell master data science: Molecular Nutrition Janos Zempleni, Hannelore Daniel, 2003 Molecular nutrition (the study of interactions between nutrients and various intracellular and extracellular molecules) is one of the most rapidly developing fields in nutritional science. Ultimately, molecular nutrition research will reveal how nutrients may affect fundamental processes such as DNA repair, cell proliferation, and apoptosis. This book is the only single complete volume available reviewing the field of molecular nutrition. It contains contributions from leading international experts, and reviews the most important and latest research from various areas of molecular nutrition. |
| cornell master data science: Materials Science and Engineering for the 1990s National Research Council, Division on Engineering and Physical Sciences, National Materials Advisory Board, Board on Physics and Astronomy, Commission on Engineering and Technical Systems, Commission on Physical Sciences, Mathematics, and Resources, Solid State Sciences Committee, Committee on Materials Science and Engineering, 1989-02-01 Materials science and engineering (MSE) contributes to our everyday lives by making possible technologies ranging from the automobiles we drive to the lasers our physicians use. Materials Science and Engineering for the 1990s charts the impact of MSE on the private and public sectors and identifies the research that must be conducted to help America remain competitive in the world arena. The authors discuss what current and future resources would be needed to conduct this research, as well as the role that industry, the federal government, and universities should play in this endeavor. |
| cornell master data science: Semiparametric Regression with R Jaroslaw Harezlak, David Ruppert, Matt P. Wand, 2018-12-12 This easy-to-follow applied book on semiparametric regression methods using R is intended to close the gap between the available methodology and its use in practice. Semiparametric regression has a large literature but much of it is geared towards data analysts who have advanced knowledge of statistical methods. While R now has a great deal of semiparametric regression functionality, many of these developments have not trickled down to rank-and-file statistical analysts. The authors assemble a broad range of semiparametric regression R analyses and put them in a form that is useful for applied researchers. There are chapters devoted to penalized spines, generalized additive models, grouped data, bivariate extensions of penalized spines, and spatial semi-parametric regression models. Where feasible, the R code is provided in the text, however the book is also accompanied by an external website complete with datasets and R code. Because of its flexibility, semiparametric regression has proven to be of great value with many applications in fields as diverse as astronomy, biology, medicine, economics, and finance. This book is intended for applied statistical analysts who have some familiarity with R. |
| cornell master data science: Statistical Foundations of Data Science Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou, 2020-09-21 Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning. |
| cornell master data science: Transformation and Weighting in Regression Raymond J. Carroll, David Ruppert, 2017-10-19 This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression. |
| cornell master data science: A History of Cornell Morris Bishop, 2014-10-15 Cornell University is fortunate to have as its historian a man of Morris Bishop's talents and devotion. As an accurate record and a work of art possessing form and personality, his book at once conveys the unique character of the early university—reflected in its vigorous founder, its first scholarly president, a brilliant and eccentric faculty, the hardy student body, and, sometimes unfortunately, its early architecture—and establishes Cornell's wider significance as a case history in the development of higher education. Cornell began in rebellion against the obscurantism of college education a century ago. Its record, claims the author, makes a social and cultural history of modern America. This story will undoubtedly entrance Cornellians; it will also charm a wider public. Dr. Allan Nevins, historian, wrote: I anticipated that this book would meet the sternest tests of scholarship, insight, and literary finish. I find that it not only does this, but that it has other high merits. It shows grasp of ideas and forces. It is graphic in its presentation of character and idiosyncrasy. It lights up its story by a delightful play of humor, felicitously expressed. Its emphasis on fundamentals, without pomposity or platitude, is refreshing. Perhaps most important of all, it achieves one goal that in the history of a living university is both extremely difficult and extremely valuable: it recreates the changing atmosphere of time and place. It is written, very plainly, by a man who has known and loved Cornell and Ithaca for a long time, who has steeped himself in the traditions and spirit of the institution, and who possesses the enthusiasm and skill to convey his understanding of these intangibles to the reader. The distinct personalities of Ezra Cornell and first president Andrew Dickson White dominate the early chapters. For a vignette of the founder, see Bishop's description of his first buildings (Cascadilla, Morrill, McGraw, White, Sibley): At best, he writes, they embody the character of Ezra Cornell, grim, gray, sturdy, and economical. To the English historian, James Anthony Froude, Mr. Cornell was the most surprising and venerable object I have seen in America. The first faculty, chosen by President White, reflected his character: his idealism, his faith in social emancipation by education, his dislike of dogmatism, confinement, and inherited orthodoxy; while the romantic upstate gothic architecture of such buildings as the President's house (now Andrew D. White Center for the Humanities), Sage Chapel, and Franklin Hall may be said to portray the taste and Soul of Andrew Dickson White. Other memorable characters are Louis Fuertes, the beloved naturalist; his student, Hugh Troy, who once borrowed Fuertes' rhinoceros-foot wastebasket for illicit if hilarious purposes; the more noteworthy and the more eccentric among the faculty of succeeding presidential eras; and of course Napoleon, the campus dog, whose talent for hailing streetcars brought him home safely—and alone—from the Penn game. The humor in A History of Cornell is at times kindly, at times caustic, and always illuminating. |
| cornell master data science: Machines as the Measure of Men Michael Adas, 1989 This new edition of what has become a standard account of Western expansion and technological dominance includes a new preface by the author that discusses how subsequent developments in gender and race studies, as well as global technology and politics, enter into conversation with his original arguments. |
| cornell master data science: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| cornell master data science: Grit Angela Duckworth, 2016-05-03 In this instant New York Times bestseller, Angela Duckworth shows anyone striving to succeed that the secret to outstanding achievement is not talent, but a special blend of passion and persistence she calls “grit.” “Inspiration for non-geniuses everywhere” (People). The daughter of a scientist who frequently noted her lack of “genius,” Angela Duckworth is now a celebrated researcher and professor. It was her early eye-opening stints in teaching, business consulting, and neuroscience that led to her hypothesis about what really drives success: not genius, but a unique combination of passion and long-term perseverance. In Grit, she takes us into the field to visit cadets struggling through their first days at West Point, teachers working in some of the toughest schools, and young finalists in the National Spelling Bee. She also mines fascinating insights from history and shows what can be gleaned from modern experiments in peak performance. Finally, she shares what she’s learned from interviewing dozens of high achievers—from JP Morgan CEO Jamie Dimon to New Yorker cartoon editor Bob Mankoff to Seattle Seahawks Coach Pete Carroll. “Duckworth’s ideas about the cultivation of tenacity have clearly changed some lives for the better” (The New York Times Book Review). Among Grit’s most valuable insights: any effort you make ultimately counts twice toward your goal; grit can be learned, regardless of IQ or circumstances; when it comes to child-rearing, neither a warm embrace nor high standards will work by themselves; how to trigger lifelong interest; the magic of the Hard Thing Rule; and so much more. Winningly personal, insightful, and even life-changing, Grit is a book about what goes through your head when you fall down, and how that—not talent or luck—makes all the difference. This is “a fascinating tour of the psychological research on success” (The Wall Street Journal). |
| cornell master data science: How Much Inequality Is Fair? Venkat Venkatasubramanian, 2017-08-08 Many in the United States feel that the nation’s current level of economic inequality is unfair and that capitalism is not working for 90% of the population. Yet some inequality is inevitable. The question is: What level of inequality is fair? Mainstream economics has offered little guidance on fairness and the ideal distribution of income. Political philosophy, meanwhile, has much to say about fairness yet relies on qualitative theories that cannot be verified by empirical data. To address inequality, we need to know what the goal is—and for this, we need a quantitative, testable theory of fairness for free-market capitalism. How Much Inequality Is Fair? synthesizes concepts from economics, political philosophy, game theory, information theory, statistical mechanics, and systems engineering into a mathematical framework for a fair free-market society. The key to this framework is the insight that maximizing fairness means maximizing entropy, which makes it possible to determine the fairest possible level of pay inequality. The framework therefore provides a moral justification for capitalism in mathematical terms. Venkat Venkatasubramanian also compares his theory’s predictions to actual inequality data from various countries—showing, for instance, that Scandinavia has near-ideal fairness, while the United States is markedly unfair—and discusses the theory’s implications for tax policy, social programs, and executive compensation. |
| cornell master data science: Colleges that Change Lives Loren Pope, 1996 The distinctive group of forty colleges profiled here is a well-kept secret in a status industry. They outdo the Ivies and research universities in producing winners. And they work their magic on the B and C students as well as on the A students. Loren Pope, director of the College Placement Bureau, provides essential information on schools that he has chosen for their proven ability to develop potential, values, initiative, and risk-taking in a wide range of students. Inside you'll find evaluations of each school's program and personality to help you decide if it's a community that's right for you; interviews with students that offer an insider's perspective on each college; professors' and deans' viewpoints on their school, their students, and their mission; and information on what happens to the graduates and what they think of their college experience. Loren Pope encourages you to be a hard-nosed consumer when visiting a college, advises how to evaluate a school in terms of your own needs and strengths, and shows how the college experience can enrich the rest of your life. |
| cornell master 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. |
| cornell master data science: Social Sequence Analysis Benjamin Cornwell, 2015-08-06 Social sequence analysis includes a diverse and rapidly growing body of methods that social scientists have developed to help study complex ordered social processes, including chains of transitions, trajectories and other ordered phenomena. Social sequence analysis is not limited by content or time scale and can be used in many different fields, including sociology, communication, information science and psychology. Social Sequence Analysis aims to bring together both foundational and recent theoretical and methodological work on social sequences from the last thirty years. A unique reference book for a new generation of social scientists, this book will aid demographers who study life-course trajectories and family histories, sociologists who study career paths or work/family schedules, communication scholars and micro-sociologists who study conversation, interaction structures and small-group dynamics, as well as social epidemiologists. |
| cornell master data science: Privacy in Context Helen Nissenbaum, 2009-11-24 Privacy is one of the most urgent issues associated with information technology and digital media. This book claims that what people really care about when they complain and protest that privacy has been violated is not the act of sharing information itself—most people understand that this is crucial to social life —but the inappropriate, improper sharing of information. Arguing that privacy concerns should not be limited solely to concern about control over personal information, Helen Nissenbaum counters that information ought to be distributed and protected according to norms governing distinct social contexts—whether it be workplace, health care, schools, or among family and friends. She warns that basic distinctions between public and private, informing many current privacy policies, in fact obscure more than they clarify. In truth, contemporary information systems should alarm us only when they function without regard for social norms and values, and thereby weaken the fabric of social life. |
| cornell master data science: The Atlas for the Aspiring Network Scientist Michele Coscia, 2021-01-11 Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets of nodes connected by sets of edges and a number of node and edge attributes. This deceptively simple object is the starting point of never-ending complexity, due to its ability to represent almost every facet of reality: chemical interactions, protein pathways inside cells, neural connections inside the brain, scientific collaborations, financial relations, citations in art history, just to name a few examples. If we hope to make sense of complex networks, we need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, and more. This book aims at providing the first access to all these tools. It is intended as an Atlas, because its interest is not in making you a specialist in using any of these techniques. Rather, after reading this book, you will have a general understanding about the existence and the mechanics of all these approaches. You can use such an understanding as the starting point of your own career in the field of network science. This has been, so far, an interdisciplinary endeavor. The founding fathers of this field come from many different backgrounds: mathematics, sociology, computer science, physics, history, digital humanities, and more. This Atlas is charting your path to be something different from all of that: a pure network scientist. |
| cornell master data science: Foundations of Probabilistic Programming Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva, 2020-12-03 This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core. |
| cornell master data science: From Solidarity to Geopolitics Tsveta Petrova, 2014-09-22 This book theorizes a mechanism underlying regime-change waves, the deliberate efforts of diffusion entrepreneurs to spread a particular regime and regime-change model across state borders. Why do only certain states and nonstate actors emerge as such entrepreneurs? Why, how, and how effectively do they support regime change abroad? To answer these questions, the book studies the entrepreneurs behind the third wave of democratization, with a focus on the new eastern European democracies - members of the European Union. The study finds that it is not the strongest democracies nor the democracies trying to ensure their survival in a neighborhood of nondemocracies that become the most active diffusion entrepreneurs. It is, instead, the countries where the organizers of the domestic democratic transitions build strong solidarity movements supporting the spread of democracy abroad that do. The book also draws parallels between their activism abroad and their experiences with democratization and democracy assistance at home. |
| cornell master data science: Joseph Cornell and Astronomy Kirsten A. Hoving, 2009 Joseph Cornell and Astronomy provides an in-depth look at one artist's intense fascination with the science of astronomy. Joseph Cornell (1903-72) has often been viewed as a recluse, isolated in his home on Utopia Parkway, lost in the fairy tales and charming objects of his collages and assemblage boxes. Less commonly known has been Cornell's vested and serious interest in the history of astronomy and the cutting-edge discoveries made during his own lifetime. An avid reader, he amassed a library of books and articles about science and astronomy, and his reflections about these subjects had a direct impact on his art. This book explores why astronomy captivated Cornell, and considers hundreds of his works--found-footage films, three-dimensional space-object boxes, enigmatic collages, and cosmic ephemera--that contain references to astronomical phenomena. Kirsten Hoving considers Cornell's enormous collection of astronomy materials, ranging from eighteenth-century books to recent works; newspaper and magazine articles that Cornell clipped and sorted; and diary entries of his observations while stargazing in his backyard. She examines how Cornell explored many dimensions of astronomy through his identities as a Christian Scientist and surrealist artist. Unfolding Cornell's work with depth and breadth, Joseph Cornell and Astronomy offers a convincing and original appreciation of this intriguing American artist. |
| cornell master data science: Healthcare Data Analytics Chandan K. Reddy, Charu C. Aggarwal, 2015-06-23 At the intersection of computer science and healthcare, data analytics has emerged as a promising tool for solving problems across many healthcare-related disciplines. Supplying a comprehensive overview of recent healthcare analytics research, Healthcare Data Analytics provides a clear understanding of the analytical techniques currently available |
| cornell master data science: Authentic Happiness Martin Seligman, 2011-01-11 In this important, entertaining book, one of the world's most celebrated psychologists, Martin Seligman, asserts that happiness can be learned and cultivated, and that everyone has the power to inject real joy into their lives. In Authentic Happiness, he describes the 24 strengths and virtues unique to the human psyche. Each of us, it seems, has at least five of these attributes, and can build on them to identify and develop to our maximum potential. By incorporating these strengths - which include kindness, originality, humour, optimism, curiosity, enthusiasm and generosity -- into our everyday lives, he tells us, we can reach new levels of optimism, happiness and productivity. Authentic Happiness provides a variety of tests and unique assessment tools to enable readers to discover and deploy those strengths at work, in love and in raising children. By accessing the very best in ourselves, we can improve the world around us and achieve new and lasting levels of authentic contentment and joy. |
| cornell master data science: Why Things Break Mark Eberhart, 2007-12-18 Did you know— • It took more than an iceberg to sink the Titanic. • The Challenger disaster was predicted. • Unbreakable glass dinnerware had its origin in railroad lanterns. • A football team cannot lose momentum. • Mercury thermometers are prohibited on airplanes for a crucial reason. • Kryptonite bicycle locks are easily broken. “Things fall apart” is more than a poetic insight—it is a fundamental property of the physical world. Why Things Break explores the fascinating question of what holds things together (for a while), what breaks them apart, and why the answers have a direct bearing on our everyday lives. When Mark Eberhart was growing up in the 1960s, he learned that splitting an atom leads to a terrible explosion—which prompted him to worry that when he cut into a stick of butter, he would inadvertently unleash a nuclear cataclysm. Years later, as a chemistry professor, he remembered this childhood fear when he began to ponder the fact that we know more about how to split an atom than we do about how a pane of glass breaks. In Why Things Break, Eberhart leads us on a remarkable and entertaining exploration of all the cracks, clefts, fissures, and faults examined in the field of materials science and the many astonishing discoveries that have been made about everything from the explosion of the space shuttle Challenger to the crashing of your hard drive. Understanding why things break is crucial to modern life on every level, from personal safety to macroeconomics, but as Eberhart reveals here, it is also an area of cutting-edge science that is as provocative as it is illuminating. |
| cornell master data science: Handbook of Big Data Analytics Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, 2018-07-20 Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science. |
| cornell master data science: Statistics and Data Analysis for Financial Engineering David Ruppert, David S. Matteson, 2015-04-21 The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest. |
| cornell master 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. |
| cornell master data science: Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs Hugh G. Gauch (Jr.), 1992-11-06 Basic statistical concepts. AMMI and related models. Estimation. Selection. Modeling. Efficient experiments. |
| cornell master data science: An Introduction to Data Science Jeffrey S. Saltz, Jeffrey M. Stanton, 2017-08-25 An Introduction to Data Science is an easy-to-read data science textbook for those with no prior coding knowledge. It features exercises at the end of each chapter, author-generated tables and visualizations, and R code examples throughout. |
| cornell master data science: The Substance of Civilization Stephen L. Sass, 2011-08 Demonstrates the way in which the discovery, application, and adaptation of materials has shaped the course of human history and the routines of our daily existence. |
| cornell master data science: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| cornell master data science: The New Science of Strong Materials J. E. Gordon, 2006-02-19 This new edition of the book on the properties of materials used in engineering answers some fundamental questions about how the material world around us functions. In particular: the author focuses on so-called strong materials, such as metals, wood, ceramics, glass, and bone. For each material in question, the author explains the unique physical and chemical basis for its inherent structural qualities. He also shows how an in-depth understanding of these materials' intrinsic strengths (and weaknesses) guides our engineering choices, allowing us to build the structures that support our modern society. |
| cornell master data science: Life and Death in Captivity Geoffrey P. R. Wallace, 2015-04-30 In Life and Death in Captivity, Geoffrey P. R. Wallace explores the profound differences in the ways captives are treated during armed conflict. Wallace focuses on the dual role played by regime type and the nature of the conflict in determining whether captor states opt for brutality or mercy. |
| cornell master data science: Data Science for Business With R Jeffrey S. Saltz, Jeffrey M. Stanton, 2021-02-03 Data Science for Business with R, written by Jeffrey S. Saltz and Jeffrey M. Stanton, focuses on the concepts foundational for students starting a business analytics or data science degree program. To keep the book practical and applied, the authors feature a running case using a global airline business’s customer survey dataset to illustrate how to turn data in business decisions, in addition to numerous examples throughout. To aid in usability beyond the classroom, the text features full integration of freely-available R and RStudio software, one of the most popular data science tools available. Designed for students with little to no experience in related areas like computer science, the book chapters follow a logical order from introduction and installation of R and RStudio, working with data architecture, undertaking data collection, performing data analysis, and transitioning to data archiving and presentation. Each chapter follows a familiar structure, starting with learning objectives and background, following the basic steps of functions alongside simple examples, applying these functions to the case study, and ending with chapter challenge questions, sources, and a list of R functions so students know what to expect in each step of their data science course. Data Science for Business with R provides readers with a straightforward and applied guide to this new and evolving field. |
| cornell master data science: Talking to the Girls Edvige Giunta, Mary Anne Trasciatti, 2022-03-22 Candid and intimate accounts of the factory-worker tragedy that shaped American labor rights On March 25, 1911, a fire broke out on the eighth floor of the Asch Building in Greenwich Village, New York. The top three floors housed the Triangle Waist Company, a factory where approximately 500 workers, mostly young immigrant women and girls, labored to produce fashionable cotton blouses, known as “waists.” The fire killed 146 workers in a mere 15 minutes but pierced the perpetual conscience of citizens everywhere. The Asch Building had been considered a modern fireproof structure, but inadequate fire safety regulations left the workers inside unprotected. The tragedy of the fire, and the resulting movements for change, were pivotal in shaping workers' rights and unions. A powerful collection of diverse voices, Talking to the Girls: Intimate and Political Essays on the Triangle Fire brings together stories from writers, artists, activists, scholars, and family members of the Triangle workers. Nineteen contributors from across the globe speak of a singular event with remarkable impact. One hundred and eleven years after the tragic incident, Talking to the Girls articulates a story of contemporary global relevance and stands as an act of collective testimony: a written memorial to the Triangle victims. |
| cornell master data science: Civic Agriculture Thomas A. Lyson, 2012-05-22 A engaging analysis of food production in the United States emphasizing that sustainable agricultural development is important to community health. |
| cornell master data science: Applied Magnetism R. Gerber, C.D. Wright, G. Asti, 2013-03-09 This book is based on the contributions to a course, entitled Applied Magnetism, which was the 25th Course of the International School of Materials Science and Technology. The Course was held as a NATO Advanced Study Institute at the Ettore Majorana Centre in Erice, Sicily, Italy between the 1st and 12th July 1992, and attracted almost 70 participants from 15 different countries. The book deals with the theory, experiments and applications of the main topical areas of applied magnetism. These selected areas include the physics of magnetic recording, magnetic and magneto-optic recording devices, systems and media, magnetic fine particles, magnetic separation, domains and domain walls in soft magnetic materials, permanent magnets, magnetoresistance, thin film magneto-optics, and finally, microwave, optical and computational magnetics. The material is organised into I 0 self-contained chapters which together provide a comprehensive coverage of the subject of applied magnetism. The aim is to emphasise the connection between the fundamental theoretical concepts, key experiments and the important technological developments which have been achieved in this field up to the present time. Moreover, when and where possible, pointers to future trends are indicated which hopefully, together with the background material, will promote further advancement of research. The organizing committee would like to acknowledge the sponsorship of the NATO Scientific Affairs Division, the National Science Foundation of the USA, the Science and Engineering Research Council of the UK, the Italian Ministry of Education, the Italian Ministry of University and Scientific Research and the Sicilian Regional Government. |
| cornell master data science: Doctoral Dissertations in Musicology American Musicological Society, International Musicological Society, 1984 |
| cornell master data science: Sustainable Diets Barbara Burlingame, Sandro Dernini, 2018-12-10 This book takes a transdisciplinary approach and considers multisectoral actions, integrating health, agriculture, environment, economy, and socio-cultural issues, to comprehensively explore the topic of sustainable diets. Consideration is given to the multi-dimensional nature of diets and food systems, and the book explores the challenging issues connecting food security and nutrition to sustainability, culture, tradition, and a broader range of scientific topics. The first section, 'Grand Challenges' (chapters 1-9), positions sustainable diets in the multi-perspective context of food systems. Within the current international debate, it introduces some overarching wicked problems, resistant to resolution in spite of the dire consequences of inaction. The chapters cover multi-sectoral policy, public health, sustainable food systems, climate change, biodiversity loss, agro-ecology, indigenous peoples, the role of cities, and food and waste. The contributions in the second section, 'Qualitative and Quantitative Approaches' (chapters 10-17) discuss the topic of sustainable diets from different cultural, sectoral and disciplinary angles. The issues are analysed with data and methods derived from social sciences, clinical sciences and experimental sciences. Perspectives and solutions, with evidence, are presented to underpin policies and interventions. The last section, 'Moving Forward' (chapters 18-29) deals with selected innovations, initiatives, projects, case studies and programmes enhancing sustainable diets by linking nutrition to food systems. |
| cornell master data science: Hospitality Branding Chekitan S. Dev, 2012-11-01 In recent years the brand has moved squarely into the spotlight as the key to success in the hospitality industry. Business strategy once began with marketing and incorporated branding as one of its elements; today the brand drives marketing within the larger hospitality enterprise. Not only has it become the chief means of attracting customers, it has, more broadly, become the chief organizing principle for most hospitality organizations. The never-ending quest for market share follows trend after trend, from offering ever more elaborate and sophisticated amenities to the use of social media as a marketing tool—all driven by the preeminence of the brand. Chekitan S. Dev’s award-winning research has appeared in leading journals including Cornell Hospitality Quarterly, Journal of Marketing, and Harvard Business Review. He is the recipient of several major hospitality research and teaching awards. A former corporate executive with Oberoi Hotels & Resorts, he has served corporate, government, education, advisory, and private equity clients in more than forty countries as consultant, seminar leader, keynote speaker and expert witness. Hospitality Branding brings together the most important insights from the author’s many years of research and experience, all in a single, affordably priced volume (available in both print and eBook formats). Skillfully blending the knowledge of recent history, the wisdom of cutting-edge research, and promise of future trends, this book offers hospitality organizations the advice they need to survive and thrive in today’s competitive global business environment. |
| cornell master data science: The Lives of Bees Thomas D. Seeley, 2019-05-28 Seeley, a world authority on honey bees, sheds light on why wild honey bees are still thriving while those living in managed colonies are in crisis. Drawing on the latest science as well as insights from his own pioneering fieldwork, he describes in extraordinary detail how honey bees live in nature and shows how this differs significantly from their lives under the management of beekeepers. Seeley presents an entirely new approach to beekeeping--Darwinian Beekeeping--which enables honey bees to use the toolkit of survival skills their species has acquired over the past thirty million years, and to evolve solutions to the new challenges they face today. He shows beekeepers how to use the principles of natural selection to guide their practices, and he offers a new vision of how beekeeping can better align with the natural habits of honey bees. |
在康奈尔大学 (Cornell University) 就读是种怎样的体验？ - 知乎
但这里就分享一个好玩的经历吧，这件事我觉得真心是Cornell这样的名校才能给我的，而且是我看完《阿拉伯的劳伦斯》后一直神往的地方，那就是我在读书期间获得了沙特阿拉伯政府全额奖 …
大家怎么看位于纽约市的 Cornell Tech（康奈尔科技校区）项目？
因为我在Cornell本部也读过，应该比较有发言权，我就来解释下这个事。Cornell一直因为它较偏僻的地理位置被诟病，所以Cornell长期以来都有在纽约的分校，而且分校和本部之间联系紧密。 …
硕士毕业论文是深度学习相关，需要自己做数据集，但我做出来的 …
盲审的话有两个点可以毙掉你的论文： （1）自己做的数据集。一般算法创新需要在公开数据集上测试效果，如果需要特殊数据集，应该先在公开数据集上证明自己方法的有效性，然后再在自 …
常春藤、25所新常春藤、公立常春藤都是哪些学校？ - 知乎
康奈尔大学（Cornell University）#18; 新常春藤（25所） 范德堡大学（Vanderbilt University）#14; 圣路易斯华盛顿大学（Washington University in St. Louis）#16; 莱斯大 …
如何评价英伟达发布的 Tesla V100 计算卡？ - 知乎
原文：Cornell University -> Cornell Virtual Workshop -> Understanding GPU Architecture -> GPU Example: Tesla V100. It's fine to have a general understanding of what graphics processing …
致久坐腰疼的年轻人——七年总结的办公久坐护腰指南
Oct 24, 2023 · 根据2:1的规律，每天仍有至少有6小时以上的坐姿时间，更何况996的老哥门，每天至少有8小时需要坐在椅子上。
在康奈尔大学 (Cornell University) 就读是种怎样的体验？ - 知乎
但这里就分享一个好玩的经历吧，这件事我觉得真心是Cornell这样的名校才能给我的，而且是我看完《阿拉伯的劳伦斯》后一直神往的地方，那就是我在 …
大家怎么看位于纽约市的 Cornell Tech（康奈尔科技校区）项目？ - 知乎
因为我在Cornell本部也读过，应该比较有发言权，我就来解释下这个事。Cornell一直因为它较偏僻的地理位置被诟病，所以Cornell长期以来都有在纽约的 …
硕士毕业论文是深度学习相关，需要自己做数据集，但我做出来的数据集有点小，且模 …
盲审的话有两个点可以毙掉你的论文： （1）自己做的数据集。一般算法创新需要在公开数据集上测试效果，如果需要特殊数据集，应该先在公开数据集 …
常春藤、25所新常春藤、公立常春藤都是哪些学校？ - 知乎
康奈尔大学（Cornell University）#18; 新常春藤（25所） 范德堡大学（Vanderbilt University）#14; 圣路易斯华盛顿大 …
如何评价英伟达发布的 Tesla V100 计算卡？ - 知乎
原文：Cornell University -> Cornell Virtual Workshop -> Understanding GPU Architecture -> GPU Example: Tesla V100. It's fine to …
