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| data science degree berkeley: 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. |
| data science degree berkeley: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| data science degree berkeley: Big Data on Campus Karen L. Webber, Henry Y. Zheng, 2020-11-03 Webber, Henry Y. Zheng, Ying Zhou |
| data science degree berkeley: Data Structures And Algorithms Shi-kuo Chang, 2003-09-29 This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at www.cs.pitt.edu/~jung/GrowingBook/, so that both teachers and students can benefit from their expertise. |
| data science degree berkeley: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| data science degree berkeley: The Charisma Machine Morgan G. Ames, 2019-11-19 A fascinating examination of technological utopianism and its complicated consequences. In The Charisma Machine, Morgan Ames chronicles the life and legacy of the One Laptop per Child project and explains why—despite its failures—the same utopian visions that inspired OLPC still motivate other projects trying to use technology to “disrupt” education and development. Announced in 2005 by MIT Media Lab cofounder Nicholas Negroponte, One Laptop per Child promised to transform the lives of children across the Global South with a small, sturdy, and cheap laptop computer, powered by a hand crank. In reality, the project fell short in many ways—starting with the hand crank, which never materialized. Yet the project remained charismatic to many who were captivated by its claims of access to educational opportunities previously out of reach. Behind its promises, OLPC, like many technology projects that make similarly grand claims, had a fundamentally flawed vision of who the computer was made for and what role technology should play in learning. Drawing on fifty years of history and a seven-month study of a model OLPC project in Paraguay, Ames reveals that the laptops were not only frustrating to use, easy to break, and hard to repair, they were designed for “technically precocious boys”—idealized younger versions of the developers themselves—rather than the children who were actually using them. The Charisma Machine offers a cautionary tale about the allure of technology hype and the problems that result when utopian dreams drive technology development. |
| data science degree berkeley: Optimization Models Giuseppe C. Calafiore, Laurent El Ghaoui, 2014-10-31 This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects. |
| data science degree berkeley: Open Sources Chris DiBona, Sam Ockman, 1999-01-03 Freely available source code, with contributions from thousands of programmers around the world: this is the spirit of the software revolution known as Open Source. Open Source has grabbed the computer industry's attention. Netscape has opened the source code to Mozilla; IBM supports Apache; major database vendors haved ported their products to Linux. As enterprises realize the power of the open-source development model, Open Source is becoming a viable mainstream alternative to commercial software.Now in Open Sources, leaders of Open Source come together for the first time to discuss the new vision of the software industry they have created. The essays in this volume offer insight into how the Open Source movement works, why it succeeds, and where it is going.For programmers who have labored on open-source projects, Open Sources is the new gospel: a powerful vision from the movement's spiritual leaders. For businesses integrating open-source software into their enterprise, Open Sources reveals the mysteries of how open development builds better software, and how businesses can leverage freely available software for a competitive business advantage.The contributors here have been the leaders in the open-source arena: Brian Behlendorf (Apache) Kirk McKusick (Berkeley Unix) Tim O'Reilly (Publisher, O'Reilly & Associates) Bruce Perens (Debian Project, Open Source Initiative) Tom Paquin and Jim Hamerly (mozilla.org, Netscape) Eric Raymond (Open Source Initiative) Richard Stallman (GNU, Free Software Foundation, Emacs) Michael Tiemann (Cygnus Solutions) Linus Torvalds (Linux) Paul Vixie (Bind) Larry Wall (Perl) This book explains why the majority of the Internet's servers use open- source technologies for everything from the operating system to Web serving and email. Key technology products developed with open-source software have overtaken and surpassed the commercial efforts of billion dollar companies like Microsoft and IBM to dominate software markets. Learn the inside story of what led Netscape to decide to release its source code using the open-source mode. Learn how Cygnus Solutions builds the world's best compilers by sharing the source code. Learn why venture capitalists are eagerly watching Red Hat Software, a company that gives its key product -- Linux -- away.For the first time in print, this book presents the story of the open- source phenomenon told by the people who created this movement.Open Sources will bring you into the world of free software and show you the revolution. |
| data science degree berkeley: Born to Be Good: The Science of a Meaningful Life Dacher Keltner, 2009-10-05 “A landmark book in the science of emotions and its implications for ethics and human universals.”—Library Journal, starred review In this startling study of human emotion, Dacher Keltner investigates an unanswered question of human evolution: If humans are hardwired to lead lives that are “nasty, brutish, and short,” why have we evolved with positive emotions like gratitude, amusement, awe, and compassion that promote ethical action and cooperative societies? Illustrated with more than fifty photographs of human emotions, Born to Be Good takes us on a journey through scientific discovery, personal narrative, and Eastern philosophy. Positive emotions, Keltner finds, lie at the core of human nature and shape our everyday behavior—and they just may be the key to understanding how we can live our lives better. Some images in this ebook are not displayed owing to permissions issues. |
| data science degree berkeley: Engineering Software as a Service Armando Fox, David A. Patterson, 2016 (NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details.(NOTE: this Beta Edition may contain errors. See http://saasbook.info for details.) A one-semester college course in software engineering focusing on cloud computing, software as a service (SaaS), and Agile development using Extreme Programming (XP). This book is neither a step-by-step tutorial nor a reference book. Instead, our goal is to bring a diverse set of software engineering topics together into a single narrative, help readers understand the most important ideas through concrete examples and a learn-by-doing approach, and teach readers enough about each topic to get them started in the field. Courseware for doing the work in the book is available as a virtual machine image that can be downloaded or deployed in the cloud. A free MOOC (massively open online course) at saas-class.org follows the book's content and adds programming assignments and quizzes. See http://saasbook.info for details. |
| data science degree berkeley: The Promise of Access Daniel Greene, 2021 Based on fieldwork at three distinct sites in Washington, DC, this book finds that the persistent problem of poverty is often framed as a problem of technology-- |
| data science degree berkeley: Electing Peace Aila M. Matanock, 2017-07-25 This book examines the causes and consequences of post-conflict elections in securing and stabilizing peace agreements without the need to send troops. It will interest scholars and advanced students of civil war and peacebuilding in comparative politics, political sociology, and peace and conflict studies. |
| data science degree berkeley: Learning Analytics Johann Ari Larusson, Brandon White, 2014-07-04 In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success. |
| data science degree berkeley: How to Be a High School Superstar Cal Newport, 2010-07-27 Do Less, Live More, Get Accepted What if getting into your reach schools didn’t require four years of excessive A.P. classes, overwhelming activity schedules, and constant stress? In How to Be a High School Superstar, Cal Newport explores the world of relaxed superstars—students who scored spots at the nation’s top colleges by leading uncluttered, low stress, and authentic lives. Drawing from extensive interviews and cutting-edge science, Newport explains the surprising truths behind these superstars’ mixture of happiness and admissions success, including: · Why doing less is the foundation for becoming more impressive. · Why demonstrating passion is meaningless, but being interesting is crucial. · Why accomplishments that are hard to explain are better than accomplishments that are hard to do. These insights are accompanied by step-by-step instructions to help any student adopt the relaxed superstar lifestyle—proving that getting into college doesn’t have to be a chore to survive, but instead can be the reward for living a genuinely interesting life. |
| data science degree berkeley: Cognitive Surplus Clay Shirky, 2010-06-10 The author of the breakout hit Here Comes Everybody reveals how new technology is changing us for the better. In his bestselling Here Comes Everybody, Internet guru Clay Shirky provided readers with a much-needed primer for the digital age. Now, with Cognitive Surplus, he reveals how new digital technology is unleashing a torrent of creative production that will transform our world. For the first time, people are embracing new media that allow them to pool their efforts at vanishingly low cost. The results of this aggregated effort range from mind-expanding reference tools like Wikipedia to life-saving Web sites like Ushahidi.com, which allows Kenyans to report acts of violence in real time. Cognitive Surplus explores what's possible when people unite to use their intellect, energy, and time for the greater good. |
| data science degree berkeley: Berkeley DB Sleepycat Software Inc, 2001 Small, special-purpose computing devices and high-end core Internet servers need fast, reliable database management. Berkeley DB is an embedded database that provides high-performance, scalable, transaction-protected and recoverable data management services to applications. Extremely portable, this library runs under almost all UNIX and Windows variants, as well as a number of embedded, real-time operating systems. Berkeley DB is the ultimate resource for the world's most widely deployed embedded database engine. This book will aid software architects and engineers, product managers, and systems and network administrators without the overhead imposed by other database products. Designed by programmers for programmers, this classic library style toolkit provides a broad base of functionality to application writers. This book will help you to make intelligent choices about when and how to use Berkeley DB to meet your needs. You can visit the Sleepycat website to get the latest errata for this book. NOTE: The first printing of this book contained an error in the table of contents that caused the page numbers to be off. This will be corrected in the second printing. If you have an earlier edition, you can download a pdf of the correct table of contents that you can print out and use with your book. If you have any questions, please feel free to contact the editor of this book at stephanie.wall@newriders.com. |
| data science degree berkeley: Cornerstones of Attachment Research Robbie Duschinsky, 2020 This is an open access title available under the terms of a [CC BY-NC-ND 4.0 International] licence. It is free to read at Oxford Clinical Psychology Online and offered as a free PDF download from OUP and selected open access locations. Attachment theory is among the most popular theories of human socioemotional development, with a global research community and widespread interest from clinicians, child welfare professionals, educationalists and parents. It has been considered one of the most generative contemporary ideas about family life in modern society. It is one of the last of the grand theories of human development that still retains an active research tradition. Attachment theory and research speak to fundamental questions about human emotions, relationships and development. They do so in terms that feel experience-near, with a remarkable combination of intuitive ideas and counter-intuitive assessments and conclusions. Over time, attachment theory seems to have become more, rather than less, appealing and popular, in part perhaps due to alignment with current concern with the lifetime implications of early brain development Cornerstones of Attachment Research re-examines the work of key laboratories that have contributed to the study of attachment. In doing so, the book traces the development in a single scientific paradigm through parallel but separate lines of inquiry. Chapters address the work of Bowlby, Ainsworth, Main and Hesse, Sroufe and Egeland, and Shaver and Mikulincer. Cornerstones of Attachment Research utilises attention to these five research groups as a lens on wider themes and challenges faced by attachment research over the decades. The chapters draw on a complete analysis of published scholarly and popular works by each research group, as well as much unpublished material. |
| data science degree berkeley: Beginning Mathematica and Wolfram for Data Science Jalil Villalobos Alva, 2021-03-28 Enhance your data science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. The book introduces you to the Wolfram programming language and its syntax, as well as the structure of Mathematica and its advantages and disadvantages. You’ll see how to use the Wolfram language for data science from a theoretical and practical perspective. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. You’ll cover how to use Mathematica where data management and mathematical computations are needed. Along the way you’ll appreciate how Mathematica provides a complete integrated platform: it has a mixed syntax as a result of its symbolic and numerical calculations allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. What You Will Learn Use Mathematica to explore data and describe the concepts using Wolfram language commands Create datasets, work with data frames, and create tables Import, export, analyze, and visualize data Work with the Wolfram data repository Build reports on the analysis Use Mathematica for machine learning, with different algorithms, including linear, multiple, and logistic regression; decision trees; and data clustering The fundamentals of the Wolfram Neural Network Framework and how to build your neural network for different tasks How to use pre-trained models from the Wolfram Neural Net Repository Who This Book Is For Data scientists new to using Wolfram and Mathematica as a language/tool to program in. Programmers should have some prior programming experience, but can be new to the Wolfram language. |
| data science degree berkeley: Why We Sleep Matthew Walker, 2017-10-03 Sleep is one of the most important but least understood aspects of our life, wellness, and longevity ... An explosion of scientific discoveries in the last twenty years has shed new light on this fundamental aspect of our lives. Now ... neuroscientist and sleep expert Matthew Walker gives us a new understanding of the vital importance of sleep and dreaming--Amazon.com. |
| data science degree berkeley: Innovation Engineering Ikhlaq Sidhu, 2019-09-12 Innovation Engineering is a practical guide to creating anything new - whether in a large firm, research lab, new venture or even in an innovative student project. As an executive, are you happy with the return on investment of your innovative projects? As an innovator, do you feel confident that you can navigate obstacles and achieve success with your innovative project? The reality is that most innovation projects fail. The challenge in developing any new technology, application, or venture is that the innovator must be able to execute while also learning. Innovation Engineering, developed and used at UC Berkeley, provides the tactical process, leadership, and behaviors necessary for successful innovation projects. Our validation tests have shown that teams which properly use Innovation Engineering accomplished their innovative projects approximately 4X faster than and with higher quality results. They also on-board new team members faster, they have much fewer unnecessary meetings, and they even report a more positive outlook on the project itself. Inter-woven between the chapters are real-life case studies with some of the world's most successful innovators to provide context, patterns, and playbooks that you can follow. Highly applied, and very realistic, Innovation Engineering builds on 30 years of technology innovation projects within large firms, advanced development labs, and new ventures at UC Berkeley, in Silicon Valley, and globally. If your goal is to create something new and have it successfully used in real life, this book is for you. |
| data science degree berkeley: Corporate Diplomacy Witold J. Henisz, 2017-09-08 Managers of multinational organizations are struggling to win the strategic competition for the hearts and minds of external stakeholders. These stakeholders differ fundamentally in their worldview, their understanding of the market economy and their aspirations and fears for the future. Their collective opinions of managers and corporations will shape the competitive landscape of the global economy and have serious consequences for businesses that fail to meet their expectations. This important new book argues that the strategic management of relationships with external stakeholders – what the author calls Corporate Diplomacy – is not just canny PR, but creates real and lasting business value.Using a mix of colourful examples, practically relevant tools and considered perspectives, the book hones in on a fundamental challenge that managers of multinational corporations face as they strive to compete in the 21st century. As falling communication costs shrink, the distance between external stakeholders and shareholder value is increasingly created and protected through a strategic integration of the external stakeholder facing functions. These include government affairs, stakeholder relations, sustainability, enterprise risk management, community relations and corporate communications. Through such integration, the place where business, politics and society intersect need not be a source of nasty surprises or unexpected expenses. Most of the firms profiled in the book are now at the frontier of corporate diplomacy. But they didn’t start there. Many of them were motivated by past failings. They fell into conflicts with critical stakeholders – politicians, communities, NGO staffers, or activists – and they suffered. They experienced delays or disruptions to their operations, higher costs, angry customers, or thwarted attempts at expansion. Eventually, the managers of these companies developed smarter strategies for stakeholder engagement. They became corporate diplomats. The book draws on their experiences to take the reader to the forefront of stakeholder engagement and to highlight the six elements of corprate diplomacy. |
| data science degree berkeley: Learning How to Learn Barbara Oakley, PhD, Terrence Sejnowski, PhD, Alistair McConville, 2018-08-07 A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course Learning How to Learn have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid rut think in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun. |
| data science degree berkeley: Making Parents Charis Thompson, 2005 Reproductive technologies, says Thompson, are part of the increasing tendency to turn social problems into biomedical questions and can be used as a lens to see the resulting changes in the relations between science and society.--BOOK JACKET. |
| data science degree berkeley: From Berkeley to Berlin Tom Francis Ramos, 2022-02-15 In November 1960, bolstered by anti-Communist ideologies, John F. Kennedy was elected president of the United States. Soviet Premier Nikita Khrushchev brandished nuclear diplomacy in an attempt to force the United States to abandon Berlin, setting the stage for a major nuclear confrontation over the fate of West Berlin. From Berkeley to Berlin explores how the United States had the wherewithal to stand up to Khrushchev's attempts to expand Soviet influence around the globe. The story begins when a South Dakotan, Ernest Lawrence, the grandson of Norwegian immigrants, created a laboratory on the Berkeley campus of the University of California. The Rad Lab attracted some of the finest talent in America to pursue careers in nuclear physics. When it was discovered that Nazi Germany had the means to build an atomic bomb, Lawrence threw all his energy into waking up the American government to act. Ten years later, when Joseph Stalin's Soviet Union became a nuclear power, Lawrence drove his students to take on the challenge to deter a Communist despot's military ambitions. Their journey was not easy: they had to overcome ridicule over three successive failures, which led to calls to see them, and their laboratory, shut down. At the Nobska Conference in 1956, the Rad Lab physicists took up the daunting challenge to provide the Navy with a warhead for Polaris. The success of the Polaris missile, which could be carried by submarines, was a critical step in establishing nuclear deterrent capability and helped Kennedy stare down Khrushchev during the Berlin Crisis of 1961. Six months after the height of the Berlin Crisis, Kennedy thought about how close the country had come to destruction, and he flew out to Berkeley to meet and thank a small group of Rad Lab physicists for helping the country avert a nuclear war. |
| data science degree berkeley: Neural Network Learning Martin Anthony, Peter L. Bartlett, 1999-11-04 This work explores probabilistic models of supervised learning problems and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, the authors develop a model of classification by real-output networks, and demonstrate the usefulness of classification... |
| data science degree berkeley: Search User Interfaces Marti A. Hearst, 2009-09-21 The truly world-wide reach of the Web has brought with it a new realisation of the enormous importance of usability and user interface design. In the last ten years, much has become understood about what works in search interfaces from a usability perspective, and what does not. Researchers and practitioners have developed a wide range of innovative interface ideas, but only the most broadly acceptable make their way into major web search engines. This book summarizes these developments, presenting the state of the art of search interface design, both in academic research and in deployment in commercial systems. Many books describe the algorithms behind search engines and information retrieval systems, but the unique focus of this book is specifically on the user interface. It will be welcomed by industry professionals who design systems that use search interfaces as well as graduate students and academic researchers who investigate information systems. |
| data science degree berkeley: Biodemography James R. Carey, Deborah A. Roach, 2020-01-07 An authoritative overview of the concepts and applications of biological demography This book provides a comprehensive introduction to biodemography, an exciting interdisciplinary field that unites the natural science of biology with the social science of human demography. Biodemography is an essential resource for demographers, epidemiologists, gerontologists, and health professionals as well as ecologists, population biologists, entomologists, and conservation biologists. This accessible and innovative book is also ideal for the classroom. James Carey and Deborah Roach cover everything from baseline demographic concepts to biodemographic applications, and present models and equations in discrete rather than continuous form to enhance mathematical accessibility. They use a wealth of real-world examples that draw from data sets on both human and nonhuman species and offer an interdisciplinary approach to demography like no other, with topics ranging from kinship theory and family demography to reliability engineering, tort law, and demographic disasters such as the Titanic and the destruction of Napoleon's Grande Armée. Provides the first synthesis of demography and biology Covers baseline demographic models and concepts such as Lexis diagrams, mortality, fecundity, and population theory Features in-depth discussions of biodemographic applications like harvesting theory and mark-recapture Draws from data sets on species ranging from fruit flies and plants to elephants and humans Uses a uniquely interdisciplinary approach to demography, bringing together a diverse range of concepts, models, and applications Includes informative biodemographic shorts, appendixes on data visualization and management, and more than 150 illustrations of models and equations |
| data science degree berkeley: Getting Mentored in Graduate School W. Brad Johnson, Jennifer M. Huwe, 2003 Getting Mentored in Graduate School is the first guide to mentoring relationships written exclusively for graduate students. Research has shown that students who are mentored enjoy many benefits, including better training, greater career success, and a stronger professional identity. Authors Johnson and Huwe draw directly from their own experiences as mentor and protege to advise students on finding a mentor and maintaining the mentor relationship throughout graduate school. Conversational, accessible, and informative, this book offers practical strategies that can be employed not only by students pursuing mentorships but also by professors seeking to improve their mentoring skills. Johnson and Huwe arm readers with the tools they need to anticipate and prevent common pitfalls and to resolve problems that may arise in mentoring relationships. This book is essential reading for students who want to learn and master the unwritten rules that lead to finding a mentor and getting more from graduate school and your career. |
| data science degree berkeley: Gutsy Girls Of Science Ilina Singh, 2022-02-28 Eleven gutsy women who loved science enough to fight for their place in the sun... This book explores the contribution of these remarkable Indian women -- from cytogeneticist Archana Sharma and botanist Janaki Ammal to mathematician Raman Parimala, physicist Bibha Chowdhuri, chemist Asima Chatterjee and several others. This book is a celebration of their lives and the wonderful world of science. With intelligence and innate artistic talent, young Ilina Singh presents through this book 11 trailblazing Indian women who overcame all odds to achieve success in STEM. -- Eric Falt, Director and UNESCO Representative to Bhutan, India, Maldives and Sri Lanka The book includes a foreword by Eric Falt from UNESCO's Delhi office. |
| data science degree berkeley: Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II) Anna Bargagliotti, Christine Franklin, Pip Arnold, Rob Gould, 2020 This document lays out a curriculum framework for pre-K-12 educational programs that is designed to help students achieve data literacy and become statistically literate. The framework and subsequent sections in this book recommend curriculum and implementation strategies covering pre-K-12 statistics education-- |
| data science degree berkeley: The Belmont Report United States. National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1978 |
| data science degree berkeley: The Alignment Problem: Machine Learning and Human Values Brian Christian, 2020-10-06 A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful. |
| data science degree berkeley: Senegal Abroad Maya Angela Smith, 2019-03-05 Senegal Abroad explores the fascinating role of language in national, transnational, postcolonial, racial, and migrant identities. Capturing the experiences of Senegalese in Paris, Rome, and New York, it depicts how they make sense of who they are—and how they fit into their communities, countries, and the larger global Senegalese diaspora. Drawing on extensive interviews with a wide range of emigrants as well as people of Senegalese heritage, Maya Angela Smith contends that they shape their identity as they purposefully switch between languages and structure their discourse. The Senegalese are notable, Smith suggests, both in their capacity for movement and in their multifaceted approach to language. She finds that, although the emigrants she interviews express complicated relationships to the multiple languages they speak and the places they inhabit, they also convey pleasure in both travel and language. Offering a mix of poignant, funny, reflexive, introspective, and witty stories, they blur the lines between the utility and pleasure of language, allowing a more nuanced understanding of why and how Senegalese move. |
| data science degree berkeley: Data Science for Social Good Massimo Lapucci, Ciro Cattuto, 2021-10-13 This book is a collection of reflections by thought leaders at first-mover organizations in the exploding field of Data Science for Social Good, meant as the application of knowledge from computer science, complex systems and computational social science to challenges such as humanitarian response, public health, sustainable development. The book provides both an overview of scientific approaches to social impact – identifying a social need, targeting an intervention, measuring impact – and the complementary perspective of funders and philanthropies that are pushing forward this new sector. This book will appeal to students and researchers in the rapidly growing field of data science for social impact, to data scientists at companies whose data could be used to generate more public value, and to decision makers at nonprofits, foundations, and agencies that are designing their own agenda around data. |
| data science degree berkeley: The Loss of Hindustan Manan Ahmed Asif, 2020-11-24 Shortlisted for the Cundill History Prize “Remarkable and pathbreaking...A radical rethink of colonial historiography and a compelling argument for the reassessment of the historical traditions of Hindustan.” —Mahmood Mamdani “The brilliance of Asif’s book rests in the way he makes readers think about the name ‘Hindustan’...Asif’s focus is Indian history but it is, at the same time, a lens to look at questions far bigger.” —Soni Wadhwa, Asian Review of Books “Remarkable...Asif’s analysis and conclusions are powerful and poignant.” —Rudrangshu Mukherjee, The Wire “A tremendous contribution...This is not only a book that you must read, but also one that you must chew over and debate.” —Audrey Truschke, Current History Did India, Pakistan, and Bangladesh have a shared regional identity prior to the arrival of Europeans in the late fifteenth century? Manan Ahmed Asif tackles this contentious question by inviting us to reconsider the work and legacy of the influential historian Muhammad Qasim Firishta, a contemporary of the Mughal emperors Akbar and Jahangir. Inspired by his reading of Firishta and other historians, Asif seeks to rescue our understanding of the region from colonial narratives that emphasize difference and division. Asif argues that a European understanding of India as Hindu has replaced an earlier, native understanding of India as Hindustan, a home for all faiths. Turning to the subcontinent’s medieval past, he uncovers a rich network of historians of Hindustan who imagined, studied, and shaped their kings, cities, and societies. The Loss of Hindustan reveals how multicultural Hindustan was deliberately eclipsed in favor of the religiously partitioned world of today. A magisterial work with far reaching implications, it offers a radical reinterpretation of how India came to its contemporary political identity. |
| data science degree berkeley: Learning Data Science Sam Lau, Joseph Gonzalez, Deborah Nolan, 2023-09-15 As an aspiring data scientist, you appreciate why organizations rely on data for important decisions--whether it's for companies designing websites, cities deciding how to improve services, or scientists discovering how to stop the spread of disease. And you want the skills required to distill a messy pile of data into actionable insights. We call this the data science lifecycle: the process of collecting, wrangling, analyzing, and drawing conclusions from data. Learning Data Science is the first book to cover foundational skills in both programming and statistics that encompass this entire lifecycle. It's aimed at those who wish to become data scientists or who already work with data scientists, and at data analysts who wish to cross the technical/nontechnical divide. If you have a basic knowledge of Python programming, you'll learn how to work with data using industry-standard tools like pandas. Refine a question of interest to one that can be studied with data Pursue data collection that may involve text processing, web scraping, etc. Glean valuable insights about data through data cleaning, exploration, and visualization Learn how to use modeling to describe the data Generalize findings beyond the data |
| data science degree berkeley: World Inequality Report 2022 Lucas Chancel, Thomas Piketty, Emmanuel Saez, Gabriel Zucman, 2022-11 World Inequality Report 2022 is the most authoritative and comprehensive account of global trends in inequality, providing cutting-edge information about income and wealth inequality and also pioneering data about the history of inequality, gender inequality, environmental inequalities, and trends in international tax reform and redistribution. |
| data science degree berkeley: Multimedia Computing Gerald Friedland, Ramesh Jain, 2014-07-28 This innovative textbook presents an experiential, holistic approach to multimedia computing along with practical algorithms. |
| data science degree berkeley: 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 degree berkeley: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
