Advertisement
| cornell data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: Nature Rx Donald A. Rakow, Gregory T. Eells, 2019-05-15 The Nature Rx movement is changing campus life. Offering alternative ways to deal with the stress that students are under, these programs are redefining how to provide students with the best possible environment in which to be healthy, productive members of the academic community. In Nature Rx, Donald A. Rakow and Gregory T. Eells summarize the value of nature prescription programs designed to encourage college students to spend time in nature and to develop a greater appreciation for the natural world. Because these programs are relatively new, there are many lessons for practitioners to learn; but clinical studies demonstrate that students who regularly spend time in nature have reduced stress and anxiety levels and improved mood and outlook. In addition to the latest research, the authors present a step-by-step formula for constructing, sustaining, and evaluating Nature Rx programs, and they profile four such programs at American colleges. The practical guidance in Nature Rx alongside the authors' vigorous argument for the benefits of these programs for both students and institutions places Rakow and Eells at the forefront of this burgeoning movement. |
| cornell data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: The Professor Is In Karen Kelsky, 2015-08-04 The definitive career guide for grad students, adjuncts, post-docs and anyone else eager to get tenure or turn their Ph.D. into their ideal job Each year tens of thousands of students will, after years of hard work and enormous amounts of money, earn their Ph.D. And each year only a small percentage of them will land a job that justifies and rewards their investment. For every comfortably tenured professor or well-paid former academic, there are countless underpaid and overworked adjuncts, and many more who simply give up in frustration. Those who do make it share an important asset that separates them from the pack: they have a plan. They understand exactly what they need to do to set themselves up for success. They know what really moves the needle in academic job searches, how to avoid the all-too-common mistakes that sink so many of their peers, and how to decide when to point their Ph.D. toward other, non-academic options. Karen Kelsky has made it her mission to help readers join the select few who get the most out of their Ph.D. As a former tenured professor and department head who oversaw numerous academic job searches, she knows from experience exactly what gets an academic applicant a job. And as the creator of the popular and widely respected advice site The Professor is In, she has helped countless Ph.D.’s turn themselves into stronger applicants and land their dream careers. Now, for the first time ever, Karen has poured all her best advice into a single handy guide that addresses the most important issues facing any Ph.D., including: -When, where, and what to publish -Writing a foolproof grant application -Cultivating references and crafting the perfect CV -Acing the job talk and campus interview -Avoiding the adjunct trap -Making the leap to nonacademic work, when the time is right The Professor Is In addresses all of these issues, and many more. |
| cornell data science master: The Last Lecture Randy Pausch, Jeffrey Zaslow, 2010 The author, a computer science professor diagnosed with terminal cancer, explores his life, the lessons that he has learned, how he has worked to achieve his childhood dreams, and the effect of his diagnosis on him and his family. |
| cornell data science master: 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 data science master: 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 data science master: Doctoral Dissertations in Musicology American Musicological Society, International Musicological Society, 1984 |
| cornell data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| cornell data science master: 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 data science master: The Design of Approximation Algorithms David P. Williamson, David B. Shmoys, 2011-04-26 Discrete optimization problems are everywhere, from traditional operations research planning problems, such as scheduling, facility location, and network design; to computer science problems in databases; to advertising issues in viral marketing. Yet most such problems are NP-hard. Thus unless P = NP, there are no efficient algorithms to find optimal solutions to such problems. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first part of the book is devoted to a single algorithmic technique, which is then applied to several different problems. The second part revisits the techniques but offers more sophisticated treatments of them. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithms courses, the book will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems. |
| cornell data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: Sugar Maple Research , 1982 |
| cornell data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: 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 data science master: Why Does College Cost So Much? Robert B. Archibald, David Henry Feldman, 2011 College tuition has risen more rapidly than the overall inflation rate for much of the past century. To explain rising college cost, the authors place the higher education industry firmly within the larger economic history of the United States. |
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 …
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 …
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 …
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 …
