Data Science Discovery Program Berkeley

  data science discovery program 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 discovery program berkeley: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.
  data science discovery program 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 discovery program berkeley: Transparent and Reproducible Social Science Research Garret Christensen, Jeremy Freese, Edward Miguel, 2019-07-23 Recently, social science has had numerous episodes of influential research that was found invalid when placed under rigorous scrutiny. The growing sense that many published results are potentially erroneous has made those conducting social science research more determined to ensure the underlying research is sound. Transparent and Reproducible Social Science Research is the first book to summarize and synthesize new approaches to combat false positives and non-reproducible findings in social science research, document the underlying problems in research practices, and teach a new generation of students and scholars how to overcome them. Understanding that social science research has real consequences for individuals when used by professionals in public policy, health, law enforcement, and other fields, the book crystallizes new insights, practices, and methods that help ensure greater research transparency, openness, and reproducibility. Readers are guided through well-known problems and are encouraged to work through new solutions and practices to improve the openness of their research. Created with both experienced and novice researchers in mind, Transparent and Reproducible Social Science Research serves as an indispensable resource for the production of high quality social science research.
  data science discovery program 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 discovery program berkeley: The Cambridge Handbook of Undergraduate Research Harald A. Mieg, Elizabeth Ambos, Angela Brew, Dominique Galli, Judith Lehmann, 2022-07-07 Undergraduate Research (UR) can be defined as an investigation into a specific topic within a discipline by an undergraduate student that makes an original contribution to the field. It has become a major consideration among research universities around the world, in order to advance both academic teaching and research productivity. Edited by an international team of world authorities in UR, this Handbook is the first truly comprehensive and systematic account of undergraduate research, which brings together different international approaches, with attention to both theory and practice. It is split into sections covering different countries, disciplines, and methodologies. It also provides an overview of current research and theoretical perspectives on undergraduate research as well as future developmental prospects of UR. Written in an engaging style, yet wide-ranging in its scope, it is essential reading for anyone wishing to broaden their understanding of how undergraduate research is implemented worldwide.
  data science discovery program 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 discovery program 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 discovery program berkeley: Data Science and Predictive Analytics Ivo D. Dinov, 2023-02-16 This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.
  data science discovery program berkeley: Targeted Learning Mark J. van der Laan, Sherri Rose, 2011-06-17 The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
  data science discovery program berkeley: Winner-Take-All Politics Jacob S. Hacker, Paul Pierson, 2010 In this groundbreaking book on one of the world's greatest economic crises, Hacker and Pierson explain why the richest of the rich are getting richer while the rest of the world isn't.
  data science discovery program berkeley: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice.
  data science discovery program 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 discovery program berkeley: The City That Became Safe Franklin E. Zimring, 2013-11 Discusses many of the ways that New York City dropped its crime rate between the years of 1991 and 2000.
  data science discovery program berkeley: The Instant Physicist Richard A. Muller, 2010-11-23 Presents fun cartoons alongside explanations of scientific curiosities such as chocolate having more energy than TNT, and wine being radioactive.
  data science discovery program berkeley: Scientific Discovery in the Social Sciences Mark Addis, Peter C. R. Lane, Peter D. Sozou, Fernand Gobet, 2019-09-12 This volume offers selected papers exploring issues arising from scientific discovery in the social sciences. It features a range of disciplines including behavioural sciences, computer science, finance, and statistics with an emphasis on philosophy. The first of the three parts examines methods of social scientific discovery. Chapters investigate the nature of causal analysis, philosophical issues around scale development in behavioural science research, imagination in social scientific practice, and relationships between paradigms of inquiry and scientific fraud. The next part considers the practice of social science discovery. Chapters discuss the lack of genuine scientific discovery in finance where hypotheses concern the cheapness of securities, the logic of scientific discovery in macroeconomics, and the nature of that what discovery with the Solidarity movement as a case study. The final part covers formalising theories in social science. Chapters analyse the abstract model theory of institutions as a way of representing the structure of scientific theories, the semi-automatic generation of cognitive science theories, and computational process models in the social sciences. The volume offers a unique perspective on scientific discovery in the social sciences. It will engage scholars and students with a multidisciplinary interest in the philosophy of science and social science.
  data science discovery program berkeley: Situating Data Science Michelle Hoda Wilkerson, Joseph L. Polman, 2022-04-14 This book explores how one distinguishing feature of Data Science - its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded - suggests serious implications for learning and education.
  data science discovery program berkeley: Text Analysis with R Matthew L. Jockers, Rosamond Thalken, 2020-03-30 Now in its second edition, Text Analysis with R provides a practical introduction to computational text analysis using the open source programming language R. R is an extremely popular programming language, used throughout the sciences; due to its accessibility, R is now used increasingly in other research areas. In this volume, readers immediately begin working with text, and each chapter examines a new technique or process, allowing readers to obtain a broad exposure to core R procedures and a fundamental understanding of the possibilities of computational text analysis at both the micro and the macro scale. Each chapter builds on its predecessor as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying. Text Analysis with R is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological toolkit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that readers simply cannot gather using traditional qualitative methods of close reading and human synthesis. This new edition features two new chapters: one that introduces dplyr and tidyr in the context of parsing and analyzing dramatic texts to extract speaker and receiver data, and one on sentiment analysis using the syuzhet package. It is also filled with updated material in every chapter to integrate new developments in the field, current practices in R style, and the use of more efficient algorithms.
  data science discovery program berkeley: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
  data science discovery program berkeley: California Master Gardener Handbook, 2nd Edition Dennis Pittenger, 2014-12-15 Since it was first published in 2002, the California Master Gardener Handbook has been the definitive guide to best practices and advice for gardeners throughout the West. Now the much-anticipated 2nd Edition to the Handbook is here—completely redesigned, with updated tables, graphics, and color photos throughout. Whether you're a beginner double digging your first bed or a University of California Master Gardener, this handbook will be your go-to source for the practical, science-based information you need to sustainably maintain your landscape and garden and become an effective problem solver. Chapters cover soil, fertilizer, and water management, plant propagation, plant physiology; weeds and pests; home vegetable gardening; specific garden crops including grapes, berries temperate fruits and nuts, citrus, and avocados. Also included is information on lawns, woody landscape plants, and landscape design. New to the 2nd Edition is information on invasive plants and principles of designing and maintaining landscapes for fire protection. Inside are updates to the technical information found in each chapter, reorganization of information for better ease of use, and new content on important emerging topics. Useful conversions for many units of measure found in the Handbook or needed in caring for gardens and landscapes are located in Appendix A. A glossary of important technical terms used and an extensive index round out the book.
  data science discovery program berkeley: The Discipline of Organizing: Professional Edition Robert J. Glushko, 2014-08-25 Note about this ebook: This ebook exploits many advanced capabilities with images, hypertext, and interactivity and is optimized for EPUB3-compliant book readers, especially Apple's iBooks and browser plugins. These features may not work on all ebook readers. We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren’t possible before. The Professional Edition includes new and revised content about the active resources of the Internet of Things, and how the field of Information Architecture can be viewed as a subset of the discipline of organizing. You’ll find: 600 tagged endnotes that connect to one or more of the contributing disciplines Nearly 60 new pictures and illustrations Links to cross-references and external citations Interactive study guides to test on key points The Professional Edition is ideal for practitioners and as a primary or supplemental text for graduate courses on information organization, content and knowledge management, and digital collections. FOR INSTRUCTORS: Supplemental materials (lecture notes, assignments, exams, etc.) are available at http://disciplineoforganizing.org. FOR STUDENTS: Make sure this is the edition you want to buy. There's a newer one and maybe your instructor has adopted that one instead.
  data science discovery program berkeley: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
  data science discovery program berkeley: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build.
  data science discovery program berkeley: The Urban Climatic Map Edward Ng, Chao Ren, 2015-09-07 Rapid urbanization, higher density and more compact cities have brought about a new science of urban climatology. An understanding of the mapping of this phenomenon is crucial for urban planners. The book brings together experts in the field of Urban Climatic Mapping to provide the state of the art understanding on how urban climatic knowledge can be made available and utilized by urban planners. The book contains the technology, methodology, and various focuses and approaches of urban climatic map making. It illustrates this understanding with examples and case studies from around the world, and it explains how urban climatic information can be analysed, interpreted and applied in urban planning. The book attempts to bridge the gap between the science of urban climatology and the practice of urban planning. It provides a useful one-stop reference for postgraduates, academics and urban climatologists wishing to better understand the needs for urban climatic knowledge in city planning; and urban planners and policy makers interested in applying the knowledge to design future sustainable cities and quality urban spaces.
  data science discovery program berkeley: Targeted Learning in Data Science Mark J. van der Laan, Sherri Rose, 2018-03-28 This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
  data science discovery program berkeley: The Egypt Game Zilpha Keatley Snyder, 2012-10-23 The first time Melanie Ross meets April Hall, she’s not sure they have anything in common. But she soon discovers that they both love anything to do with ancient Egypt. When they stumble upon a deserted storage yard, Melanie and April decide it’s the perfect spot for the Egypt Game. Before long there are six Egyptians, and they all meet to wear costumes, hold ceremonies, and work on their secret code. Everyone thinks it’s just a game until strange things start happening. Has the Egypt Game gone too far?
  data science discovery program berkeley: Scientific Data Management Arie Shoshani, Doron Rotem, 2019-08-30 Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time. Scientific Data Management: Challenges, Technology, and Deployment describes cutting-edge technologies and solutions for managing and analyzing vast amounts of data, helping scientists focus on their scientific goals. The book begins with coverage of efficient storage systems, discussing how to write and read large volumes of data without slowing the simulation, analysis, or visualization processes. It then focuses on the efficient data movement and management of storage spaces and explores emerging database systems for scientific data. The book also addresses how to best organize data for analysis purposes, how to effectively conduct searches over large datasets, how to successfully automate multistep scientific process workflows, and how to automatically collect metadata and lineage information. This book provides a comprehensive understanding of the latest techniques for managing data during scientific exploration processes, from data generation to data analysis. Enhanced by numerous detailed color images, it includes real-world examples of applications drawn from biology, ecology, geology, climatology, and more. Check out Dr. Shoshani discuss the book during an interview with International Science Grid This Week (iSGTW): http: //www.isgtw.org/?pid=1002259
  data science discovery program berkeley: Principal-Investigator-Led Missions in the Space Sciences National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Committee on Principal-Investigator-Led Missions in the Space Sciences, 2006-04-22 Principal Investigator-Led (PI-led) missions are an important element of NASA's space science enterprise. While several NRC studies have considered aspects of PI-led missions in the course of other studies for NASA, issues facing the PI-led missions in general have not been subject to much analysis in those studies. Nevertheless, these issues are raising increasingly important questions for NASA, and it requested the NRC to explore them as they currently affect PI-led missions. Among the issues NASA asked to have examined were those concerning cost and scheduling, the selection process, relationships among PI-led team members, and opportunities for knowledge transfer to new PIs. This report provides a discussion of the evolution and current status of the PIled mission concept, the ways in which certain practices have affected its performance, and the steps that can carry it successfully into the future. The study was done in collaboration with the National Academy of Public Administration.
  data science discovery program berkeley: Why Walls Won't Work Michael Dear, 2013-01-16 Why Walls Won't Work is a sweeping account of life along the United States-Mexico border zone, tracing the border's history of cultural interaction since the earliest Mesoamerican times to the present day. As soon as Mexicans, American settlers, and indigenous peoples came into contact along the Rio Grande in the mid-nineteenth century, new forms of interaction and affiliation evolved. By the late-twentieth century, the border states were among the fastest-growing regions in both countries. But as Michael Dear warns, this vibrant zone of economic, cultural and social connectivity is today threatened by highly restrictive American immigration and security policies as well as violence along the border. The U.S. border-industrial complex and the emerging Mexican narco-state are undermining the very existence of the third nation occupying the space between Mexico and the U.S. Through a series of evocative portraits of contemporary border communities, Dear reveals how the promise and potential of this in-between nation still endures and is worth protecting. Now with a new chapter updating this story and suggesting what should be done about the challenges confronting the cross-border zone, Why Walls Won't Work represents a major intellectual intervention into one of the most hotly-contested political issues of our era.
  data science discovery program berkeley: User-Defined Tensor Data Analysis Bin Dong, Kesheng Wu, Suren Byna, 2021-09-29 The SpringerBrief introduces FasTensor, a powerful parallel data programming model developed for big data applications. This book also provides a user's guide for installing and using FasTensor. FasTensor enables users to easily express many data analysis operations, which may come from neural networks, scientific computing, or queries from traditional database management systems (DBMS). FasTensor frees users from all underlying and tedious data management tasks, such as data partitioning, communication, and parallel execution. This SpringerBrief gives a high-level overview of the state-of-the-art in parallel data programming model and a motivation for the design of FasTensor. It illustrates the FasTensor application programming interface (API) with an abundance of examples and two real use cases from cutting edge scientific applications. FasTensor can achieve multiple orders of magnitude speedup over Spark and other peer systems in executing big data analysis operations. FasTensor makes programming for data analysis operations at large scale on supercomputers as productively and efficiently as possible. A complete reference of FasTensor includes its theoretical foundations, C++ implementation, and usage in applications. Scientists in domains such as physical and geosciences, who analyze large amounts of data will want to purchase this SpringerBrief. Data engineers who design and develop data analysis software and data scientists, and who use Spark or TensorFlow to perform data analyses, such as training a deep neural network will also find this SpringerBrief useful as a reference tool.
  data science discovery program berkeley: Providing the Tools for Scientific Discovery and Basic Energy Research United States. Congress. House. Committee on Science, Space, and Technology (2011). Subcommittee on Energy, 2013
  data science discovery program berkeley: Data Science and Visual Computing Rae Earnshaw, John Dill, David Kasik, 2019-08-30 Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed.
  data science discovery program berkeley: The Fourth Paradigm Anthony J. G. Hey, 2009 Foreword. A transformed scientific method. Earth and environment. Health and wellbeing. Scientific infrastructure. Scholarly communication.
  data science discovery program berkeley: The Mathematics of Data Michael W. Mahoney, John C. Duchi, Anna C. Gilbert, 2018-11-15 Nothing provided
  data science discovery program berkeley: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.
  data science discovery program berkeley: HBR Guide to Getting the Mentoring You Need Harvard Business Review, 2014-01-14 Find the right person to help supercharge your career. Whether you’re eyeing a specific leadership role, hoping to advance your skills, or simply looking to broaden your professional network, you need to find someone who can help. Wait for a senior manager to come looking for you—and you’ll probably be waiting forever. Instead, you need to find the mentoring that will help you achieve your goals. Managed correctly, mentoring is a powerful and efficient tool for moving up. The HBR Guide to Getting the Mentoring You Need will help you get it right. You’ll learn how to: • Find new ways to stand out in your organization • Set clear and realistic development goals • Identify and build relationships with influential sponsors • Give back and bring value to mentors and senior advisers • Evaluate your progress in reaching your professional goals
  data science discovery program berkeley: Analytics, Data Science, and Artificial Intelligence Ramesh Sharda, Dursun Delen, Efraim Turban, 2020-03-06 For courses in decision support systems, computerized decision-making tools, and management support systems. Market-leading guide to modern analytics, for better business decisionsAnalytics, Data Science, & Artificial Intelligence: Systems for Decision Support is the most comprehensive introduction to technologies collectively called analytics (or business analytics) and the fundamental methods, techniques, and software used to design and develop these systems. Students gain inspiration from examples of organisations that have employed analytics to make decisions, while leveraging the resources of a companion website. With six new chapters, the 11th edition marks a major reorganisation reflecting a new focus -- analytics and its enabling technologies, including AI, machine-learning, robotics, chatbots, and IoT.
  data science discovery program berkeley: The Drunken Monkey Robert Dudley, 2014-05-01 Alcoholism, as opposed to the safe consumption of alcohol, remains a major public health issue. In this accessible book, Robert Dudley presents an intriguing evolutionary interpretation to explain the persistence of alcohol-related problems. Providing a deep-time, interdisciplinary perspective on today’s patterns of alcohol consumption and abuse, Dudley traces the link between the fruit-eating behavior of arboreal primates and the evolution of the sensory skills required to identify ripe and fermented fruits that contain sugar and low levels of alcohol. In addition to introducing this new theory of the relationship of humans to alcohol, the book discusses the supporting research, implications of the hypothesis, and the medical and social impacts of alcoholism. The Drunken Monkey is designed for interested readers, scholars, and students in comparative and evolutionary biology, biological anthropology, medicine, and public health.
  data science discovery program berkeley: Diversifying the STEM Fields: From Individual to Structural Approaches Rodolfo Mendoza-Denton, Colette Patt, Adrienne R. Carter-Sowell, 2023-02-14
  data science discovery program berkeley: Sweet Science Amanda Jo Goldstein, 2017-07-10 Today we do not expect poems to carry scientifically valid information. But it was not always so. In Sweet Science, Amanda Jo Goldstein returns to the beginnings of the division of labor between literature and science to recover a tradition of Romantic life writing for which poetry was a privileged technique of empirical inquiry. Goldstein puts apparently literary projects, such as William Blake’s poetry of embryogenesis, Goethe’s journals On Morphology, and Percy Shelley’s “poetry of life,” back into conversation with the openly poetic life sciences of Erasmus Darwin, J. G. Herder, Jean-Baptiste Lamarck, and Étienne Geoffroy Saint-Hilaire. Such poetic sciences, Goldstein argues, share in reviving Lucretius’s De rerum natura to advance a view of biological life as neither self-organized nor autonomous, but rather dependent on the collaborative and symbolic processes that give it viable and recognizable form. They summon De rerum natura for a logic of life resistant to the vitalist stress on self-authorizing power and to make a monumental case for poetry’s role in the perception and communication of empirical realities. The first dedicated study of this mortal and materialist dimension of Romantic biopoetics, Sweet Science opens a through-line between Enlightenment materialisms of nature and Marx’s coming historical materialism.
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)

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Information Science: PhD 1 Information Science: PhD The Doctoral Program The doctoral program in Information Science is a research-oriented program in which the student chooses …

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