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| cornell master of 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 of 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 of 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 of 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 of 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 of 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 of 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 of 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 of 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 of 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 of 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. |
| cornell master of 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 of data science: Black is the Night Maxim Jakubowski, Neil Gaiman, A.K. Benedict, Samantha Lee Howe, Joe R. Lansdale, 2022-10-25 A gritty and thrilling anthology of 30 new short stories in tribute to pulp noir master, Cornell Woolrich, author of 'Rear Window' that inspired Alfred Hitchock's classic film. Featuring Neil Gaiman, Kim Newman, James Sallis, A.K. Benedict, USA Today-bestseller Samantha Lee Howe, Joe R. Lansdale and many more. An anthology of exclusive new short stories in tribute to the master of pulp era crime writing, Cornell Woolrich. Woolrich, also published as William Irish and George Hopley, stands with Raymond Chandler, Erle Stanley Gardner and Dashiell Hammett as a legend in the genre. He is a hugely influential figure for crime writers, and is also remembered through the 50+ films made from his novels and stories, including Alfred Hitchcock’s Rear Window, The Bride Wore Black, I Married a Dead Man, Phantom Lady, Truffaut's La Sirène du Mississippi, and Black Alibi. Collected and edited by one of the most experienced editors in the field, Maxim Jakubowski, features original work from: Neil Gaiman Joel Lane Joe R. Lansdale Vaseem Khan Brandon Barrows Tara Moss Kim Newman Nick Mamatas Mason Cross Martin Edwards Donna Moore James Grady Lavie Tidhar Barry N. Malzberg James Sallis A.K. Benedict Warren Moore Max Décharné Paul Di Filippo M.W. Craven Charles Ardai Susi Holliday Bill Pronzini Kristine Kathryn Rusch Maxim Jakubowski Joseph S. Walker Samantha Lee Howe O'Neil De Noux David Quantick Ana Teresa Pereira William Boyle. |
| cornell master of 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 of data science: Plumb's Veterinary Drug Handbook Donald C. Plumb, 2018-02-21 Plumb’s Veterinary Drug Handbook, Ninth Edition updates the most complete, detailed, and trusted source of drug information relevant to veterinary medicine. Provides a fully updated edition of the classic veterinary drug handbook, with carefully curated dosages per indication for clear guidance on selecting a dose Features 16 new drugs Offers an authoritative, complete reference for detailed information about animal medication Designed to be used every day in the fast-paced veterinary setting Includes dosages for a wide range of species, including dogs, cats, exotic animals, and farm animals |
| cornell master of 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 of 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 of 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 of 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 of 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 of data science: Smartups Rob Ryan, 2002 Ryan focuses on methods he has developed over the years for building a sustainable business that makes money. He shows how to turn an idea into real product. |
| cornell master of 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 of 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 of 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 of data science: Next Generation Sequencing Lee-Jun C. Wong, 2013-05-31 In recent years, owing to the fast development of a variety of sequencing technologies in the post human genome project era, sequencing analysis of a group of target genes, entire protein coding regions of the human genome, and the whole human genome has become a reality. Next Generation Sequencing (NGS) or Massively Parallel Sequencing (MPS) technologies offers a way to screen for mutations in many different genes in a cost and time efficient manner by deep coverage of the target sequences. This novel technology has now been applied to clinical diagnosis of Mendelian disorders of well characterized or undefined diseases, discovery of new disease genes, noninvasive prenatal diagnosis using maternal blood, and population based carrier testing of severe autosomal recessive disorders. This book covers topics of these applications, including potential limitations and expanded application in the future. |
| cornell master of data science: Culture and Commerce Mukti Khaire, 2017-06-20 Art and business are often described as worlds apart, even diametric opposites. And yet, these realms are close cousins in creative industries where firms bring cultural goods to market, attaching price tags to music, paintings, theater, literature, film, and fashion. Building on theories of value construction and cultural production, Culture and Commerce details the processes by which artistic worth is decoded, translated, and converted to economic value. Mukti Khaire introduces readers to three industry players: creators, producers (who bring to market and distribute cultural goods), and intermediaries (who critique and rave about them). Case studies of firms from Chanel and Penguin to tastemakers like the Pritzker Prize and The Sundance Institute illuminate how these professionals construct a vital value chain. Highlighting the role of pioneer entrepreneurs—who carve out space for radical, new product categories—Khaire illustrates how creative professionals influence our sense of value, shifting consumer behavior and our culture in deep, surprising ways. |
| cornell master of data science: Evolutionary Patterns and Processes D. R. Lees, Dianne Edwards, 1993 Evolution is the central theme of all biology. Researcarcch in the many branches of evolutionary study continues to flourish. This book, based on a symposium of the Linnean Society, discusses the diversity in currentevolutionary research. It approaches the subject ambitiously and from several angles, bringing ttogether eminent authors from a variety of disciplines paleontologists traditionally with a macroevolutionary bias, neontologists concentrating on microevolutionary processes, and those studying the very essence ofsses and those studying the very essence of evolution the process of speciation in living organisms. Evolutionary Patterns and Processes will appeal to a broad spectrum of professional biologistsworking in such fields as paleontology, population biology, and evolutionary genetics. Biologists will enjoy chapters by Stephen J. Gould, discovering in the much earlier work of Hugo de Vries parallels with his ideas on punctuational evolution; Guy Bush,considering why there are so many small animals; Peter Sheldon, examining detailed fossil trilobite sequences for evidence of microevolutionary processes and considering models of speciation; as well as others dealing with cytological, ecological, and behavioral processes leading to the evolution of new species. None |
| cornell master of 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 of 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 of 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 of data science: Introduction to Biostatistics Ronald N. Forthofer, Eun Sul Lee, 2014-05-19 The Biostatistics course is often found in the schools of public Health, medical schools, and, occasionally, in statistics and biology departments. The population of students in these courses is a diverse one, with varying preparedness. Introduction to Biostatistics assumes the reader has at least two years of high school algebra, but no previous exposure to statistics is required. Written for individuals who might be fearful of mathematics, this book minimizes the technical difficulties and emphasizes the importance of statistics in scientific investigation. An understanding of underlying design and analysis is stressed. The limitations of the research, design and analytical techniques are discussed, allowing the reader to accurately interpret results. Real data, both processed and raw, are used extensively in examples and exercises. Statistical computing packages - MINITAB, SAS and Stata - are integrated. The use of the computer and software allows a sharper focus on the concepts, letting the computer do the necessary number-crunching. - Emphasizes underlying statistical concepts more than competing texts - Focuses on experimental design and analysis, at an elementary level - Includes an introduction to linear correlation and regression - Statistics are central: probability is downplayed - Presents life tables and survival analysis - Appendix with solutions to many exercises - Special instructor's manual with solution to all exercises |
| cornell master of data science: Introduction To Algorithms Thomas H Cormen, Charles E Leiserson, Ronald L Rivest, Clifford Stein, 2001 An extensively revised edition of a mathematically rigorous yet accessible introduction to algorithms. |
| cornell master of 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 of data science: American Higher Education in Crisis? Goldie Blumenstyk, 2015 Disinvestment by states has driven up tuition prices, and student debt has reached an all-time high. Americans are questioning the worth of a college education, even as studies show how important it is to economic and social mobility |
| cornell master of 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 of data science: Sequences and Power Series , |
| cornell master of data science: Computing the Mind Shimon Edelman, 2008-09-08 In a culmination of humanity's millennia-long quest for self knowledge, the sciences of the mind are now in a position to offer concrete, empirically validated answers to the most fundamental questions about human nature. What does it mean to be a mind? How is the mind related to the brain? How are minds shaped by their embodiment and environment? What are the principles behind cognitive functions such as perception, memory, language, thought, and consciousness? By analyzing the tasks facing any sentient being that is subject to stimulation and a pressure to act, Shimon Edelman identifies computation as the common denominator in the emerging answers to all these questions. Any system composed of elements that exchange signals with each other and occasionally with the rest of the world can be said to be engaged in computation. A brain composed of neurons is one example of a system that computes, and the computations that the neurons collectively carry out constitute the brain's mind. Edelman presents a computational account of the entire spectrum of cognitive phenomena that constitutes the mind. He begins with sentience, and uses examples from visual perception to demonstrate that it must, at its very core, be a type of computation. Throughout his account, Edelman acknowledges the human mind's biological origins. Along the way, he also demystifies traits such as creativity, language, and individual and collective consciousness, and hints at how naturally evolved minds can transcend some of their limitations by moving to computational substrates other than brains. The account that Edelman gives in this book is accessible, yet unified and rigorous, and the big picture he presents is supported by evidence ranging from neurobiology to computer science. The book should be read by anyone seeking a comprehensive and current introduction to cognitive psychology. |
| cornell master of data science: Free Speech on Campus Erwin Chemerinsky, Howard Gillman, 2017-09-12 Can free speech coexist with an inclusive campus environment? Hardly a week goes by without another controversy over free speech on college campuses. On one side, there are increased demands to censor hateful, disrespectful, and bullying expression and to ensure an inclusive and nondiscriminatory learning environment. On the other side are traditional free speech advocates who charge that recent demands for censorship coddle students and threaten free inquiry. In this clear and carefully reasoned book, a university chancellor and a law school dean—both constitutional scholars who teach a course in free speech to undergraduates—argue that campuses must provide supportive learning environments for an increasingly diverse student body but can never restrict the expression of ideas. This book provides the background necessary to understanding the importance of free speech on campus and offers clear prescriptions for what colleges can and can’t do when dealing with free speech controversies. |
| cornell master of data science: Doctoral Dissertations in Musicology American Musicological Society, International Musicological Society, 1984 |
| cornell master of data science: Sustainable Energy Jefferson W. Tester, 2005 Evaluates trade-offs and uncertainties inherent in achieving sustainable energy, analyzes the major energy technologies, and provides a framework for assessing policy options. |
在康奈尔大学 (Cornell University) 就读是种怎样的体验？ - 知乎
但这里就分享一个好玩的经历吧，这件事我觉得真心是Cornell这样的名校才能给我的，而且是我看完《阿拉伯的劳伦斯》后一直神往的地方，那就是我在读书期间获得了沙特阿拉伯政府全额奖 …
大家怎么看位于纽约市的 Cornell Tech（康奈尔科技校区）项目？ …
因为我在Cornell本部也读过，应该比较有发言权，我就来解释下这个事。Cornell一直因为它较偏僻的地理位置被诟病，所以Cornell长期以来都有在纽约的分校，而且分校和本部之间联系紧密。 …
硕士毕业论文是深度学习相关，需要自己做数据集，但我做出来的 …
盲审的话有两个点可以毙掉你的论文： （1）自己做的数据集。一般算法创新需要在公开数据集上测试效果，如果需要特殊数据集，应该先在公开数据集上证明自己方法的有效性，然后再在自 …
常春藤、25所新常春藤、公立常春藤都是哪些学校？ - 知乎
康奈尔大学（Cornell University）#18; 新常春藤（25所） 范德堡大学（Vanderbilt University）#14; 圣路易斯华盛顿大学（Washington University in St. Louis）#16; 莱斯大学（Rice …
如何评价英伟达发布的 Tesla V100 计算卡？ - 知乎
原文：Cornell University -> Cornell Virtual Workshop -> Understanding GPU Architecture -> GPU Example: Tesla V100. It's fine to have a general understanding of what graphics processing …
致久坐腰疼的年轻人——七年总结的办公久坐护腰指南
Oct 24, 2023 · 根据2:1的规律，每天仍有至少有6小时以上的坐姿时间，更何况996的老哥门，每天至少有8小时需要坐在椅子上。
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学金参加了沙特的一个中东高管 …
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。除了Cornell Tech以外，纽约还 …
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 …
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，然后再在自己数据集上展示效果。
常春藤、25所新常春藤、公立常春藤都是哪些学校？ - 知乎
康奈尔大学（Cornell University）#18; 新常春藤（25所） 范德堡大学（Vanderbilt University）#14; 圣路易斯华盛顿大学（Washington University in St. Louis）#16; 莱斯大学（Rice …
如何评价英伟达发布的 Tesla V100 计算卡？ - 知乎
原文：Cornell University -> Cornell Virtual Workshop -> Understanding GPU Architecture -> GPU Example: Tesla V100. It's fine to have a general understanding of what graphics …
