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| data science major ucla: Causal Inference in Statistics Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016-01-25 CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as Does this treatment harm or help patients? But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding. |
| data science major ucla: Stuck in the Shallow End, updated edition Jane Margolis, 2017-03-03 Why so few African American and Latino/a students study computer science: updated edition of a book that reveals the dynamics of inequality in American schools. The number of African Americans and Latino/as receiving undergraduate and advanced degrees in computer science is disproportionately low. And relatively few African American and Latino/a high school students receive the kind of institutional encouragement, educational opportunities, and preparation needed for them to choose computer science as a field of study and profession. In Stuck in the Shallow End, Jane Margolis and coauthors look at the daily experiences of students and teachers in three Los Angeles public high schools: an overcrowded urban high school, a math and science magnet school, and a well-funded school in an affluent neighborhood. They find an insidious “virtual segregation” that maintains inequality. The race gap in computer science, Margolis discovers, is one example of the way students of color are denied a wide range of occupational and educational futures. Stuck in the Shallow End is a story of how inequality is reproduced in America—and how students and teachers, given the necessary tools, can change the system. Since the 2008 publication of Stuck in the Shallow End, the book has found an eager audience among teachers, school administrators, and academics. This updated edition offers a new preface detailing the progress in making computer science accessible to all, a new postscript, and discussion questions (coauthored by Jane Margolis and Joanna Goode). |
| data science major ucla: Hierarchical Modeling and Analysis for Spatial Data Sudipto Banerjee, 2003-12-17 Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, |
| data science major ucla: Unlocking the Clubhouse Jane Margolis, Allan Fisher, 2003-02-28 Understanding and overcoming the gender gap in computer science education. The information technology revolution is transforming almost every aspect of society, but girls and women are largely out of the loop. Although women surf the Web in equal numbers to men and make a majority of online purchases, few are involved in the design and creation of new technology. It is mostly men whose perspectives and priorities inform the development of computing innovations and who reap the lion's share of the financial rewards. As only a small fraction of high school and college computer science students are female, the field is likely to remain a male clubhouse, absent major changes. In Unlocking the Clubhouse, social scientist Jane Margolis and computer scientist and educator Allan Fisher examine the many influences contributing to the gender gap in computing. The book is based on interviews with more than 100 computer science students of both sexes from Carnegie Mellon University, a major center of computer science research, over a period of four years, as well as classroom observations and conversations with hundreds of college and high school faculty. The interviews capture the dynamic details of the female computing experience, from the family computer kept in a brother's bedroom to women's feelings of alienation in college computing classes. The authors investigate the familial, educational, and institutional origins of the computing gender gap. They also describe educational reforms that have made a dramatic difference at Carnegie Mellon—where the percentage of women entering the School of Computer Science rose from 7% in 1995 to 42% in 2000—and at high schools around the country. |
| data science major ucla: Algorithms of Oppression Safiya Umoja Noble, 2018-02-20 Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author |
| data science major ucla: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science major ucla: Power to the Transfer Dimpal Jain, Santiago N. Bernal Melendez, Alfred R. Herrera, 2020-02-01 Currently, U.S. community colleges serve nearly half of all students of color in higher education who, for a multitude of reasons, do not continue their education by transferring to a university. For those students who do transfer, often the responsibility for the application process, retention, graduation, and overall success is placed on them rather than their respective institutions. This book aims to provide direction toward the development and maintenance of a transfer receptive culture, which is defined as an institutional commitment by a university to support transfer students of color. A transfer receptive culture explicitly acknowledges the roles of race and racism in the vertical transfer process from a community college to a university and unapologetically centers transfer as a form of equity in the higher education pipeline. The framework is guided by critical race theory in education, which acknowledges the role of white supremacy and its contemporary and historical role in shaping institutions of higher learning. |
| data science major ucla: State and Metropolitan Area Data Book , 1991 Contains data similar to that found in the County and City Databook, but on the state and MSA (Metropolitan Statistical Areas) levels. |
| data science major ucla: New Advances in Statistics and Data Science Ding-Geng Chen, Zhezhen Jin, Gang Li, Yi Li, Aiyi Liu, Yichuan Zhao, 2018-01-17 This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice. |
| data science major ucla: 100 Things UCLA Fans Should Know & Do Before They Die Ben Bolch, Kenny Easley, Gail Goodrich, 2018-11-06 With traditions, records, and Bruins lore, this lively, detailed book explores the personalities, events, and facts every UCLA fan should know--from the hardwood to the hard courts, the gridiron, the diamond, and beyond. It contains crucial information such as important dates, behind-the-scenes tales, memorable moments, and outstanding achievements by legends like Kareem Abdul-Jabbar, Troy Aikman, Jackie Robinson, Bill Walton, Russell Westbrook, and more. Whether you were there for the glory days of John Wooden or are a more recent fan of Josh Rosen, this is the ultimate resource guide for all Bruins faithful. |
| data science major ucla: Eastern Europe, Russia and Central Asia 2003 , 2000 |
| data science major ucla: International Historical Statistics Brian Mitchell, 1998-07-29 International Historical Statistics: Africa, Asia, Oceania is the latest edition of the most authoritative collection of statistics available. Fully updated to 1993, it provides key economic and social indicators for the last 250 years. The volume includes both hard to find historical data, and the latest figures available. |
| data science major ucla: Physics 4al Grossmann, 2001 |
| data science major ucla: Cheating Tim Groseclose, 2014-04 Because of California's Proposition 209, public universities such as UCLA cannot use race as a factor in admissions. However, as this book shows, UCLA gives significant preferences to African Americans, while it discriminates against Asians. The author, a professor of political science and economics at UCLA, documents what he witnessed as a member of UCLA's faculty oversight committee for admissions. He also describes findings from a UCLA internal report as well as statistics from a large data set that he has posted online. All show that UCLA is breaking the law. The discrimination is not simply a byproduct of class-based preferences. For instance, for one aspect of the admissions process, a rich African American's chance of admission is almost double that of a poor Asian, even when the two applicants have identical grades, SAT scores, and other factors. |
| data science major ucla: Visualize This Nathan Yau, 2011-06-13 Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing. |
| data science major ucla: Weaving an Otherwise Amanda Tachine, Z Nicolazzo, 2023-07-03 Who (and what) are you bearing witness to (and for) through your research? When you witness, what claims are you making about who and what matters? What does your research forget, and does it do it on purpose?This book reconceptualizes qualitative research as an in-relations process, one that is centered on, fully concerned with, and lifts up those who have been and continue to be dispossessed, harmed, dehumanized, and erased because of white supremacy, settler colonialism, or other hegemonic world views.It prompts scholars to make connections between themselves as “researchers” and affect, ancestors, community, family and kinship, space and place, and the more than human beings with whom they are always already in community.What are the modes and ways of knowing through which we approach our research? How can the practice of research bring us closer to the peoples, places, more than human beings, histories, presents, and futures in which we are embedded and connected to? If we are the instruments of our research, then how must we be attentive to all of the affects and relations that make us who we are and what will become? These questions animate Weaving an Otherwise, providing a wellspring from which we think about our interconnections to the past, present, and future possibilities of research.After an opening chapter by the editors that explores the consequences and liberating opportunities of rejecting dominant qualitative methodologies that erase the voices of the subordinated and disdained, the contributors of nine chapters explore and enact approaches that uncover hidden connections and reveal unconscious value systems. |
| data science major ucla: Mismatch Richard Sander, Stuart Taylor Jr, 2012-10-09 The debate over affirmative action has raged for over four decades, with little give on either side. Most agree that it began as noble effort to jump-start racial integration; many believe it devolved into a patently unfair system of quotas and concealment. Now, with the Supreme Court set to rule on a case that could sharply curtail the use of racial preferences in American universities, law professor Richard Sander and legal journalist Stuart Taylor offer a definitive account of what affirmative action has become, showing that while the objective is laudable, the effects have been anything but. Sander and Taylor have long admired affirmative action's original goals, but after many years of studying racial preferences, they have reached a controversial but undeniable conclusion: that preferences hurt underrepresented minorities far more than they help them. At the heart of affirmative action's failure is a simple phenomenon called mismatch. Using dramatic new data and numerous interviews with affected former students and university officials of color, the authors show how racial preferences often put students in competition with far better-prepared classmates, dooming many to fall so far behind that they can never catch up. Mismatch largely explains why, even though black applicants are more likely to enter college than whites with similar backgrounds, they are far less likely to finish; why there are so few black and Hispanic professionals with science and engineering degrees and doctorates; why black law graduates fail bar exams at four times the rate of whites; and why universities accept relatively affluent minorities over working class and poor people of all races. Sander and Taylor believe it is possible to achieve the goal of racial equality in higher education, but they argue that alternative policies -- such as full public disclosure of all preferential admission policies, a focused commitment to improving socioeconomic diversity on campuses, outreach to minority communities, and a renewed focus on K-12 schooling -- will go farther in achieving that goal than preferences, while also allowing applicants to make informed decisions. Bold, controversial, and deeply researched, Mismatch calls for a renewed examination of this most divisive of social programs -- and for reforms that will help realize the ultimate goal of racial equality. |
| data science major ucla: Mathematics for Economists Carl P. Simon, Lawrence Blume, 1994 Mathematics for Economists, a new text for advanced undergraduate and beginning graduate students in economics, is a thoroughly modern treatment of the mathematics that underlies economic theory. An abundance of applications to current economic analysis, illustrative diagrams, thought-provoking exercises, careful proofs, and a flexible organisation-these are the advantages that Mathematics for Economists brings to today's classroom. |
| data science major ucla: The Book of Why Judea Pearl, Dana Mackenzie, 2018-05-15 A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence Correlation is not causation. This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why. |
| data science major ucla: Learning to Solve Problems by Searching for Macro-operators Richard E. Korf, 1985 This monograph explores the idea of learning efficient strategies for solving problems by searching for macro-operators. |
| data science major ucla: How Information Systems Came to Rule the World Burt Swanson, 2021-12-24 This book offers a fresh perspective on information systems, a field of study and practice currently undergoing substantial upheaval, even as it expands rapidly and widely with new technologies and applications. Mapping the field as it has developed, the author firmly establishes the under-recognized importance of the field, and grounds it firmly in the subject’s history. He argues against the view of enthusiasts who believe that the field has somehow moved beyond information systems to something more exotic and offers a short and compelling manifesto on behalf of the field and its future. Offering a comprehensive insight into the significance of the information systems field, this book will appeal primarily to scholars and practitioners working in information systems, management, communication studies, technology studies, and related areas. |
| data science major ucla: The Gender Gap in College: Maximizing the Developmental Potential of Women and Men Linda J. Sax, Alexander W. Astin, Helen S. Astin, 2008-09-02 Praise for The Gender Gap in College Linda Sax has produced an encyclopedic volume comparing women's and men's development during the undergraduate years. We believe it is destined to become a classic in the higher education literature. —From the Foreword by Alexander W. Astin and Helen S. Astin Using findings from an important national data set, Linda Sax has skillfully crafted a definitive work about the gender gap in college. It is a major scholarly achievement that will be influential for many years to come. —Ernest Pascarella, Petersen Professor of Higher Education, University of Iowa Linda Sax has produced a meticulously researched, carefully documented analysis that identifies many ways that college impacts men and women differently. This book will be an invaluable resource to researchers and practitioners seeking to better understand and serve traditional-age students at four-year colleges and universities. —Jacqueline E. King, assistant vice president, Center for Policy Analysis, American Council on Education |
| data science major ucla: Computational Statistics Geof H. Givens, Jennifer A. Hoeting, 2012-10-09 This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice. |
| data science major ucla: Cardiovascular Biomechanics Peter R. Hoskins, Patricia V. Lawford, Barry J. Doyle, 2017-02-16 This book provides a balanced presentation of the fundamental principles of cardiovascular biomechanics research, as well as its valuable clinical applications. Pursuing an integrated approach at the interface of the life sciences, physics and engineering, it also includes extensive images to explain the concepts discussed. With a focus on explaining the underlying principles, this book examines the physiology and mechanics of circulation, mechanobiology and the biomechanics of different components of the cardiovascular system, in-vivo techniques, in-vitro techniques, and the medical applications of this research. Written for undergraduate and postgraduate students and including sample problems at the end of each chapter, this interdisciplinary text provides an essential introduction to the topic. It is also an ideal reference text for researchers and clinical practitioners, and will benefit a wide range of students and researchers including engineers, physicists, biologists and clinicians who are interested in the area of cardiovascular biomechanics. |
| data science major ucla: Right College, Right Price Frank Palmasani, 2013 Describes how the Financial Fit program can help families determine how much college will really cost beyond the sticker price and factor cost into the college search, and explains how to maximize financial aid benefits. |
| data science major ucla: Brain Informatics and Health Yike Guo, Karl Friston, Faisal Aldo, Sean Hill, Hanchuan Peng, 2015-08-20 This book constitutes the proceedings of the International Conference on Brain Informatics and Health, BIH 2015, held in London, UK, in August/September 2015. The 42 full papers presented were carefully reviewed and selected from 82 submissions. Following the success of past conferences in this series, BIH 2015 has a strong emphasis on emerging trends of big data analysis and management technology for brain research, behavior learning, and real-world applications of brain science in human health and wellbeing. |
| data science major ucla: Essentials of Geographic Information Systems Michael Edward Shin, Jonathan Campbell, Sierra Burkhart, 2022 |
| data science major ucla: Kaplan LSAT 2002-2003 Kaplan, 2002-07 You will score higher. We guarantee it. Kaplan's LSAT 2003 comes complete with a comprehensive review of all the material on the exam, plus Kaplan's test-taking strategies to maximize your score. This powerful combination is a highly effective way for you to score higher on the LSAT and make you and your application competitive for law school admissions. Succeed on the Writing Sample with Kaplan's expert strategies for constructing clear, concise, and high-scoring essays. Prepare with hundreds of practice questions for Logic Games, Logical Reasoning, and Reading Comprehension. Practice with 3 full-length LSATs, complete with explanations for every answer and detailed score analysis. Score Higher with effective strategies and advice from Kaplan's top instructors. Sign up for the Law School Edge. Tap into Kaplan's expertise with the Law School Edge, our free email newsletter. Filled with admissions tips, the latest test and career news, important deadline reminders, study aids, and more, the Law School Edge is an excellent resource for critical business school admissions information. Sign up today at kaptest.com Test Prep, Admissions and Guidance. For life. Kaplan has helped more than 3 million students achieve their educational and career goals. With 185 centers and more than 1,200 classroom locations throughout the U.S. and abroad, Kaplan provides a full range of services, including test prep courses, admissions consulting, programs for international students, professional licensing preparation, and more. For more information, contact us at 1-800-KAP-TEST or visit kaptest.com (AOL Keyword: kaplan). |
| data science major ucla: Breaking Numbers Into Parts Dr Oleg Gleizer, Dr Olga Radko, 2015-12-09 This book teaches 5 and 6-year-old children to break numbers into parts in all the possible ways. It also explains why a+b always equals b+a and takes a look at elementary arithmetic from a novel angle. The book's authors work for UCLA Department of Mathematics. The book was tried and tested at LAMC, Los Angeles Math Circle, a free Sunday school for mathematically inclined children run by the Department. |
| data science major ucla: Runaway Genres Yogita Goyal, 2019-10-29 Winner, 2021 René Wellek Prize, given by the American Comparative Literature Association Winner, 2021 Barbara Perkins and George Perkins Award, given by the International Society for the Study of Narrative Honorable Mention, 2020 James Russell Lowell Prize, given by the Modern Language Association Argues that the slave narrative is a new world literary genre In Runaway Genres, Yogita Goyal tracks the emergence of slavery as the defining template through which current forms of human rights abuses are understood. The post-black satire of Paul Beatty and Mat Johnson, modern slave narratives from Sudan to Sierra Leone, and the new Afropolitan diaspora of writers like Teju Cole and Chimamanda Ngozi Adichie all are woven into Goyal’s argument for the slave narrative as a new world literary genre, exploring the full complexity of this new ethical globalism. From the humanitarian spectacles of Kony 2012 and #BringBackOurGirls through gothic literature, Runaway Genres unravels, for instance, how and why the African child soldier has now appeared as the afterlife of the Atlantic slave. Goyal argues that in order to fathom forms of freedom and bondage today—from unlawful detention to sex trafficking to the refugee crisis to genocide—we must turn to contemporary literature, which reveals how the literary forms used to tell these stories derive from the antebellum genre of the slave narrative. Exploring the ethics and aesthetics of globalism, the book presents alternative conceptions of human rights, showing that the revival and proliferation of slave narratives offers not just an occasion to revisit the Atlantic past, but also for re-narrating the global present. In reassessing these legacies and their ongoing relation to race and the human, Runaway Genres creates a new map with which to navigate contemporary black diaspora literature. |
| data science major ucla: Global Marketing Management Robert Dow Buzzell, John A. Quelch, Christopher A. Bartlett, 1992 Designing strategies for global competition; Global marketing programs; Organizing and controlling global marketing operations; Special issues in global marketing. |
| data science major ucla: Programming Machine Ethics Luís Moniz Pereira, Ari Saptawijaya, 2016-02-10 This book addresses the fundamentals of machine ethics. It discusses abilities required for ethical machine reasoning and the programming features that enable them. It connects ethics, psychological ethical processes, and machine implemented procedures. From a technical point of view, the book uses logic programming and evolutionary game theory to model and link the individual and collective moral realms. It also reports on the results of experiments performed using several model implementations. Opening specific and promising inroads into the terra incognita of machine ethics, the authors define here new tools and describe a variety of program-tested moral applications and implemented systems. In addition, they provide alternative readings paths, allowing readers to best focus on their specific interests and to explore the concepts at different levels of detail. Mainly written for researchers in cognitive science, artificial intelligence, robotics, philosophy of technology and engineering of ethics, the book will also be of general interest to other academics, undergraduates in search of research topics, science journalists as well as science and society forums, legislators and military organizations concerned with machine ethics. |
| data science major ucla: Building Problem Solvers Kenneth D. Forbus, Johan De Kleer, 1993 After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems. |
| data science major ucla: Computational Imaging Ayush Bhandari, Achuta Kadambi, Ramesh Raskar, 2022-10-25 A comprehensive and up-to-date textbook and reference for computational imaging, which combines vision, graphics, signal processing, and optics. Computational imaging involves the joint design of imaging hardware and computer algorithms to create novel imaging systems with unprecedented capabilities. In recent years such capabilities include cameras that operate at a trillion frames per second, microscopes that can see small viruses long thought to be optically irresolvable, and telescopes that capture images of black holes. This text offers a comprehensive and up-to-date introduction to this rapidly growing field, a convergence of vision, graphics, signal processing, and optics. It can be used as an instructional resource for computer imaging courses and as a reference for professionals. It covers the fundamentals of the field, current research and applications, and light transport techniques. The text first presents an imaging toolkit, including optics, image sensors, and illumination, and a computational toolkit, introducing modeling, mathematical tools, model-based inversion, data-driven inversion techniques, and hybrid inversion techniques. It then examines different modalities of light, focusing on the plenoptic function, which describes degrees of freedom of a light ray. Finally, the text outlines light transport techniques, describing imaging systems that obtain micron-scale 3D shape or optimize for noise-free imaging, optical computing, and non-line-of-sight imaging. Throughout, it discusses the use of computational imaging methods in a range of application areas, including smart phone photography, autonomous driving, and medical imaging. End-of-chapter exercises help put the material in context. |
| data science major ucla: Fueling Innovation and Discovery National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on the Mathematical Sciences in 2025, 2012-08-02 The mathematical sciences are part of everyday life. Modern communication, transportation, science, engineering, technology, medicine, manufacturing, security, and finance all depend on the mathematical sciences. Fueling Innovation and Discovery describes recent advances in the mathematical sciences and advances enabled by mathematical sciences research. It is geared toward general readers who would like to know more about ongoing advances in the mathematical sciences and how these advances are changing our understanding of the world, creating new technologies, and transforming industries. Although the mathematical sciences are pervasive, they are often invoked without an explicit awareness of their presence. Prepared as part of the study on the Mathematical Sciences in 2025, a broad assessment of the current state of the mathematical sciences in the United States, Fueling Innovation and Discovery presents mathematical sciences advances in an engaging way. The report describes the contributions that mathematical sciences research has made to advance our understanding of the universe and the human genome. It also explores how the mathematical sciences are contributing to healthcare and national security, and the importance of mathematical knowledge and training to a range of industries, such as information technology and entertainment. Fueling Innovation and Discovery will be of use to policy makers, researchers, business leaders, students, and others interested in learning more about the deep connections between the mathematical sciences and every other aspect of the modern world. To function well in a technologically advanced society, every educated person should be familiar with multiple aspects of the mathematical sciences. |
| data science major ucla: Newman and Carranza's Clinical Periodontology and Implantology E-Book Michael G. Newman, Perry R. Klokkevold, Satheesh Elangovan, Yvonne Kapila, 2023-04-28 **Selected for Doody's Core Titles® 2024 in Dentistry** Learn and master a range of clinical techniques and achieve therapeutic goals with Newman and Carranza's Clinical Periodontology and Implantology, 14th Edition! Unmatched for its comprehensive approach, this resource provides detailed, up-to-date information on the etiology and pathogenesis of periodontal disease. Basic and advanced evidence-based information on the various treatment modalities employed in periodontics and implantology is presented in an easy-to-read format, with callout boxes throughout the text highlighting the clinical relevance of foundational basic science information. Full-color photos and radiographic images depict periodontal conditions and procedures, and the Atlas of Periodontal Pathology is one of the most comprehensive ever compiled in a periodontal textbook. Written by a team of leading experts led by Michael G. Newman, this text not only demonstrates how to perform periodontal procedures but explains the evidence supporting each treatment and provides knowledge on how to achieve the best possible outcomes of periodontal therapy and implant treatment. An eBook version is included with print purchase, providing access to all the text, figures, and references, plus the ability to search, customize content, make notes and highlights, and have content read aloud. The eBook version included with print purchase also includes Periopixel 3D color illustrations, a periodontal classification calculator and interactive learning tool, review questions, case studies, videos, 3D animations, and more! This edition features new chapters on Precision Medicine, Pocket Reduction Therapy, Periodontal Referral, and Digital Implant Workflows, as well as an updated glossary of terms linked to the eBook. It also features first-of-its-kind content on the effects of COVID-19 on treatment from key opinion leaders in this area. Case studies reflect the new format of the Integrated National Board Dental Exam (INBDE). - Full-color photos, illustrations, radiographs, animations, simulations, and videos demonstrate how to perform periodontal and implant procedures. - Current information on clinical techniques in periodontology and the latest advances in basic science. - Evidence-based treatment planning provides knowledge on how to achieve the best possible outcomes of periodontal therapy and implant treatment. - Extensive color atlas of periodontal pathology - Internationally known experts contribute chapters on their areas of specialty. - An eBook version is included with print purchase, providing access to all the text, figures, and references, plus the ability to search, customize content, make notes and highlights, and have content read aloud. |
| data science major ucla: Management 405 , 2010 |
| data science major ucla: Probability and Stochastic Processes Leo Breiman, 1986 |
| data science major ucla: The Minding Organization Moshe F. Rubinstein, Iris R. Firstenberg, 1999-08-30 A few years ago, Cementos Mexicanos (Cemex), the world's third-largest cement company, was struggling. More than two-thirds of their deliveries were late, customer complaints were numerous, and new orders were dwindling sharply. Then Cemex executives realized they needed to get a glimpse of the future. They saw themselves responding to customer needs as each need emerged. They visualized successful deliveries with orders placed only an hour in advance. Their entire organization became involved in the process of adapting to unplanned occurrences. By embracing the uncertainty and chaos of their business and a company-wide commitment to excellence, Cemex was completely transformed in a matter of months. This is a remarkable example of minding: identifying a purpose, developing a team, and acting to accomplish that purpose. Achieving this kind of high-level connection is what The Minding Organization is all about. This book will show you how to transform your organization into one that behaves like a living organism-alive with ideas and instantly able to adapt for survival in an increasingly complex, unpredictable global business world. A minding organization coordinates its efforts as a single being; the right hand literally knows what the left hand is doing. The minding process will help you save precious work time, avoid costly mistakes, build incentives for speed, and find creative solutions when unpredictable problems arise. Creating a minding organization will teach you how to: * Operate on the edge of chaos, embracing uncertainty as a strategy * Bring insights up front that would normally be learned much later * Distribute decision-making in such a way that everyone has the responsibility to be right and the authority to be wrong * Create an environment in which the human spirit can soar The Minding Organization will show you how less planning and more adapting makes for a competitive advantage, as you learn to cope with new, ever-changing conditions and innovate faster than your competitors. Praise for The Minding Organization Professor Rubinstein is one of the foremost experts on creativity within organizations. The Minding Organization is a well-written guide . . . [that] is must reading for anyone responsible for minding the organization.-Norman R. Augustine, Chairman of the Executive Committee, Lockheed Martin The authors make intuitive good sense and give strategic thinkers the tools they need to turn perceived liabilities-chaos, disorder, unpredictable change-into assets. I've made it must reading for everyone in my organization. The only people I haven't recommended it to are my competitors.-Timothy W. Hannemann, Executive Vice President and General Manager, TRW This book gives an accessible view of the organization as a living, connected organism. Drs. Rubinstein and Firstenberg have shared an insightful and elegant concept of what successful twenty-first-century organizations MUST be like if they want to survive and grow. We are putting the ideas in this book to use now!-Michael E. Allgeier, Division Vice President, Sensors and Electronics Segment, Raytheon Rubinstein is as ebullient in print as he is in person. The Minding Organization transcends time and theory, enabling the practice of innovation as an everyday occurrence.-Stephan Argent, Creative Director, iCandy Inc. In this book you'll find out how to open up a world of opportunity by 'bringing the future to the present'-visualizing the ideal end state and working backwards. You'll see how many obstacles can be eliminated, making the unachievable achievable.-Tom Williams, Vice President, Long Range Strike Business Area, Northrop Grumman Corporation |
| data science major ucla: Data Science in Engineering Vol. 10 Thomas Matarazzo, |
STATISTICS & DATA SCIENCE | 3-YEAR PLAN (as of Fall 2024)
declare the major by Spring-Year 2 for priority enrollment in Fall-Year 3. Major-specific questions? Connect with a Statistics & Data Science department advisor. For all other requirements, …
STATISTICS & DATA SCIENCE MAJOR
To declare a Major in Statistics & Data Science, a student must have completed the 7 courses below with a minimum grade of C or better in each and a 2.5 GPA or above in all prep …
Course Roadmap - University of California, Los Angeles
This roadmap is the expected 2‐year path for students who plan to finish the Statistics & Data Science major by the end of Spring Quarter of their fourth year. Courses may be taken earlier …
STATISTICS - 4 YEAR PLAN YEAR 1 FALL WINTER SPRING …
Prep for Major Requirements • Math íA, í, îA, î, ïA • î UD electives from Stats ï ì, ñ í- õ, Math ï íA, ï í, ñ íA, ñ í, ó ì, ó í, 172B, 175. • Students must complete all prep courses with a or better grade, …
Double Majors Degree Progress Guide for - UCLA Summer …
Satisfies a lower division course requirement for the Statistics and Data Science major. *MATH 31A and 31B overlap in the Economics and Statistics and Data Science majors. CATEGORY …
DATA THEORY B.S. - UCLA Mathematics
All required courses for the major must be at least 4.0 units and taken for a letter grade, unless the class is a supplement to a larger course (e.g. a required lab).
67$7,67,&6 & DATA SCIENCE 0,125 - University of California, …
To declare a Minor in Statistics & Data Science, student must have completed the 3 courses below with a minimum grade of C or better in each and a 2.5 GPA or above in all prep …
ARTICULATION GUIDE 2019-2020 - Palomar College
UCLA’s new Data Theory major will be the first in the world, both in name and content. The goal of the major is to develop knowledge and skills in the mathematical and statistical foundations of …
UCLA D STATISTICS - UCLA College of Letters and Science
designed to support the new Data Theory major, a joint major between Statistics and Mathematics. The Data Theory major was launched two years ago, and its first cohort will …
Course Roadmap - University of California, Los Angeles
This roadmap is the expected 2‐year path for students who plan to finish the Statistics major by the end of Spring Quarter of their fourth year. Courses may be taken earlier than listed. …
2024-25 UNDERGRADUATE PROFILE NEW FRESHMEN AND …
Engaging in research at UCLA is more than an academic exercise; it’s a transformative experience that prepares students to lead in their chosen fields and make meaningful …
SFP Applied Math + Statistics and Data Science
Satisfies a core upper division course requirement in Mathematics for the Statistics and Data Science major. STATS 101A : Introduction to Data Analysis and Regression
UCLA Academic Majors - Undergraduate Admission
Students learn to handle big data as easily as lab equipment. Engineering is even more hands on, with an emphasis on practical problem-solving. SHAPE YOUR FUTURE . Physical sciences …
STATISTICS MAJOR - University of California, Los Angeles
To declare a Major in Statistics, a student must complete the courses below. Due to high enrollment demand and enrollment restrictions, declaring the Major will likely be needed prior …
Academic Majors - Undergraduate Admission
Students learn to handle big data as easily as lab equipment. Engineering is even more hands on, with an emphasis on practical problem-solving. SHAPE YOUR FUTURE Physical sciences …
SPRING QUARTER 2025 - statistics.ucla.edu
Jan 25, 2025 · Social Data Science Minors Open to: All students . Stats C116 Stats 167 . Open to: Senior Stats & DS Majors . Senior Data Theory Majors ; Open to: Stats & DS Majors . Data …
UCLA ANDERSON SCHOOL OF MANAGEMENT EASTON …
This course is designed to provide students (from both technical and non-technical backgrounds) with a practical foundation in data science to manage, analyze and synthesize data to derive …
Updated 7/12/24 - UCLA Mathematics
All students wishing to enroll in MATH 1, 3A, 31AL or 31A are required to take the math diagnostic test. The UCLA Mathematics Department requires students to use ALEKS PPL, a web-based …
UCLA Academic Majors - Undergraduate Admission
Students learn to handle big data as easily as lab equipment. Engineering is even more hands on, with an emphasis on practical problem-solving. SHAPE YOUR FUTURE . Physical sciences …
Updated 10/21/24 See latest handbook version
ii 1 table of contents departmental honors & scholar programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
STATISTICS & DATA SCIENCE | 3-YEAR PLAN (as of Fall 20…
declare the major by Spring-Year 2 for priority enrollment in Fall-Year 3. Major-specific questions? Connect with a Statistics & Data Science …
STATISTICS & DATA SCIENCE MAJOR
To declare a Major in Statistics & Data Science, a student must have completed the 7 courses below with a minimum grade of C or better in …
Course Roadmap - University of California, Los Angeles
This roadmap is the expected 2‐year path for students who plan to finish the Statistics & Data Science major by the end of Spring Quarter of their fourth …
STATISTICS - 4 YEAR PLAN YEAR 1 FALL WINTER SPRIN…
Prep for Major Requirements • Math íA, í, îA, î, ïA • î UD electives from Stats ï ì, ñ í- õ, Math ï íA, ï í, ñ íA, ñ í, ó ì, ó í, 172B, 175. • Students must complete all …
Double Majors Degree Progress Guide for - UCLA …
Satisfies a lower division course requirement for the Statistics and Data Science major. *MATH 31A and 31B overlap in the Economics and …
