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| berkeley master of information and data science: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| berkeley master of information and data science: Quantitative Trading Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po-Shing Wong, 2017-01-06 The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: 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-- |
| berkeley master of information and data science: Sculpting the Self Muhammad Umar Faruque, 2021-08-17 Sculpting the Self addresses “what it means to be human” in a secular, post-Enlightenment world by exploring notions of self and subjectivity in Islamic and non-Islamic philosophical and mystical thought. Alongside detailed analyses of three major Islamic thinkers (Mullā Ṣadrā, Shāh Walī Allāh, and Muhammad Iqbal), this study also situates their writings on selfhood within the wider constellation of related discussions in late modern and contemporary thought, engaging the seminal theoretical insights on the self by William James, Jean-Paul Sartre, and Michel Foucault. This allows the book to develop its inquiry within a spectrum theory of selfhood, incorporating bio-physiological, socio-cultural, and ethico-spiritual modes of discourse and meaning-construction. Weaving together insights from several disciplines such as religious studies, philosophy, anthropology, critical theory, and neuroscience, and arguing against views that narrowly restrict the self to a set of cognitive functions and abilities, this study proposes a multidimensional account of the self that offers new options for addressing central issues in the contemporary world, including spirituality, human flourishing, and meaning in life. This is the first book-length treatment of selfhood in Islamic thought that draws on a wealth of primary source texts in Arabic, Persian, Urdu, Greek, and other languages. Muhammad U. Faruque’s interdisciplinary approach makes a significant contribution to the growing field of cross-cultural dialogue, as it opens up the way for engaging premodern and modern Islamic sources from a contemporary perspective by going beyond the exegesis of historical materials. He initiates a critical conversation between new insights into human nature as developed in neuroscience and modern philosophical literature and millennia-old Islamic perspectives on the self, consciousness, and human flourishing as developed in Islamic philosophical, mystical, and literary traditions. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: Data Science in R Deborah Nolan, Duncan Temple Lang, 2015-04-21 Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: Big Data on Campus Karen L. Webber, Henry Y. Zheng, 2020-11-03 Webber, Henry Y. Zheng, Ying Zhou |
| berkeley master of information and data science: Artificial Intelligence Stuart Russell, Peter Norvig, 2016-09-10 Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: High-Dimensional Data Analysis with Low-Dimensional Models John Wright, Yi Ma, 2022-01-13 Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candès. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: Central Park Love Song Stephen Wolf, 2018-02 Through an imaginative blend of personal memoir and meticulous research, Central Park Love Song tells the remarkable story of America's first great public park and the city that needed and created it. |
| berkeley master of information and data science: How I Became a Quant Richard R. Lindsey, Barry Schachter, 2011-01-11 Praise for How I Became a Quant Led by two top-notch quants, Richard R. Lindsey and Barry Schachter, How I Became a Quant details the quirky world of quantitative analysis through stories told by some of today's most successful quants. For anyone who might have thought otherwise, there are engaging personalities behind all that number crunching! --Ira Kawaller, Kawaller & Co. and the Kawaller Fund A fun and fascinating read. This book tells the story of how academics, physicists, mathematicians, and other scientists became professional investors managing billions. --David A. Krell, President and CEO, International Securities Exchange How I Became a Quant should be must reading for all students with a quantitative aptitude. It provides fascinating examples of the dynamic career opportunities potentially open to anyone with the skills and passion for quantitative analysis. --Roy D. Henriksson, Chief Investment Officer, Advanced Portfolio Management Quants--those who design and implement mathematical models for the pricing of derivatives, assessment of risk, or prediction of market movements--are the backbone of today's investment industry. As the greater volatility of current financial markets has driven investors to seek shelter from increasing uncertainty, the quant revolution has given people the opportunity to avoid unwanted financial risk by literally trading it away, or more specifically, paying someone else to take on the unwanted risk. How I Became a Quant reveals the faces behind the quant revolution, offering you?the?chance to learn firsthand what it's like to be a?quant today. In this fascinating collection of Wall Street war stories, more than two dozen quants detail their roots, roles, and contributions, explaining what they do and how they do it, as well as outlining the sometimes unexpected paths they have followed from the halls of academia to the front lines of an investment revolution. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: Invisible Users Jenna Burrell, 2012-05-04 An account of how young people in Ghana's capital city adopt and adapt digital technology in the margins of the global economy. The urban youth frequenting the Internet cafés of Accra, Ghana, who are decidedly not members of their country's elite, use the Internet largely as a way to orchestrate encounters across distance and amass foreign ties—activities once limited to the wealthy, university-educated classes. The Internet, accessed on second-hand computers (castoffs from the United States and Europe), has become for these youths a means of enacting a more cosmopolitan self. In Invisible Users, Jenna Burrell offers a richly observed account of how these Internet enthusiasts have adopted, and adapted to their own priorities, a technological system that was not designed with them in mind. Burrell describes the material space of the urban Internet café and the virtual space of push and pull between young Ghanaians and the foreigners they encounter online; the region's famous 419 scam strategies and the rumors of “big gains” that fuel them; the influential role of churches and theories about how the supernatural operates through the network; and development rhetoric about digital technologies and the future viability of African Internet cafés in the region. Burrell, integrating concepts from science and technology studies and African studies with empirical findings from her own field work in Ghana, captures the interpretive flexibility of technology by users in the margins but also highlights how their invisibility puts limits on their full inclusion into a global network society. |
| berkeley master of information and data science: The 9 Pitfalls of Data Science Gary Smith, Jay Cordes, 2019 The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. |
| berkeley master of information and data science: The Code Breaker Walter Isaacson, 2021-03-09 A Best Book of 2021 by Bloomberg BusinessWeek, Time, and The Washington Post The bestselling author of Leonardo da Vinci and Steve Jobs returns with a “compelling” (The Washington Post) account of how Nobel Prize winner Jennifer Doudna and her colleagues launched a revolution that will allow us to cure diseases, fend off viruses, and have healthier babies. When Jennifer Doudna was in sixth grade, she came home one day to find that her dad had left a paperback titled The Double Helix on her bed. She put it aside, thinking it was one of those detective tales she loved. When she read it on a rainy Saturday, she discovered she was right, in a way. As she sped through the pages, she became enthralled by the intense drama behind the competition to discover the code of life. Even though her high school counselor told her girls didn’t become scientists, she decided she would. Driven by a passion to understand how nature works and to turn discoveries into inventions, she would help to make what the book’s author, James Watson, told her was the most important biological advance since his codiscovery of the structure of DNA. She and her collaborators turned a curiosity of nature into an invention that will transform the human race: an easy-to-use tool that can edit DNA. Known as CRISPR, it opened a brave new world of medical miracles and moral questions. The development of CRISPR and the race to create vaccines for coronavirus will hasten our transition to the next great innovation revolution. The past half-century has been a digital age, based on the microchip, computer, and internet. Now we are entering a life-science revolution. Children who study digital coding will be joined by those who study genetic code. Should we use our new evolution-hacking powers to make us less susceptible to viruses? What a wonderful boon that would be! And what about preventing depression? Hmmm…Should we allow parents, if they can afford it, to enhance the height or muscles or IQ of their kids? After helping to discover CRISPR, Doudna became a leader in wrestling with these moral issues and, with her collaborator Emmanuelle Charpentier, won the Nobel Prize in 2020. Her story is an “enthralling detective story” (Oprah Daily) that involves the most profound wonders of nature, from the origins of life to the future of our species. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: Algorithms Sanjoy Dasgupta, Christos H. Papadimitriou, Umesh Virkumar Vazirani, 2006 This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal. Features include:The use of boxes to strengthen the narrative: pieces that provide historical context, descriptions of how the algorithms are used in practice, and excursions for the mathematically sophisticated. Carefully chosen advanced topics that can be skipped in a standard one-semester course but can be covered in an advanced algorithms course or in a more leisurely two-semester sequence.An accessible treatment of linear programming introduces students to one of the greatest achievements in algorithms. An optional chapter on the quantum algorithm for factoring provides a unique peephole into this exciting topic. In addition to the text DasGupta also offers a Solutions Manual which is available on the Online Learning Center.Algorithms is an outstanding undergraduate text equally informed by the historical roots and contemporary applications of its subject. Like a captivating novel it is a joy to read. Tim Roughgarden Stanford University |
| berkeley master of information and data science: Score Higher on the UCAT Kaplan Test Prep, 2020-04-07 The Expert Guide from Kaplan for 2021 entry One test stands between you and a place at the medical school of your dreams: the UCAT. With 1,500 questions, test-like practice exams, a question bank, and online test updates, Kaplan’s Score Higher on the UCAT, sixth edition, will help build your confidence and make sure you achieve a high score. We know it's crucial that you go into your UCAT exam equipped with the most up-to-date information available. Score Higher on the UCAT comes with access to additional online resources, including any recent exam changes, hundreds of questions, an online question bank, and a mock online test with full worked answers to ensure that there are no surprises waiting for you on test day. The Most Practice 1,500 questions in the book and online—more than any other UCAT book Three full-length tests: one mock online test to help you practise for speed and accuracy in a test-like interface, and two tests with worked answers in the book Online question bank to fine-tune and master your performance on specific question types Expert Guidance The authors of Score Higher on the UCAT have helped thousands of students prepare for the exam. They offer invaluable tips and strategies for every section of the test, helping you to avoid the common pitfalls that trip up other UCAT students. We invented test preparation—Kaplan (www.kaptest.co.uk) has been helping students for 80 years. Our proven strategies have helped legions of students achieve their dreams. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: TVA and the Grass Roots Philip Selznick, 2011-02-21 Famous and influential study of politics in action at all levels in the creation and expansion of the Tennessee Valley Authority with all its land use, agricultural, political and human effects. Landmark application of political and social theory coupled with prodigious research and insightful analysis made this a legendary work. Newly republished in print and digital formats in the Classics of the Social Sciences Series from Quid Pro Books, this acclaimed book is presented to a new generation of social scientists and historians with a substantive new Foreword by Berkeley law professor Jonathan Simon. Digital formats feature active TOC, linked notes and tables, and even a fully-linked subject matter index. All formats include embedded page numbers from prior editions for continuity of reference and citation. They are reproduced in modern format with hyperaccurate proofreading of text and notes, and properly formatted tables. |
| berkeley master of information and data science: Thriving in Graduate School Arielle Shanok, Nicole Benedicto Elden, 2021-08-02 Addresses the mental health challenges of graduate school and how students can succeed and thrive. With rates of depression and anxiety six times higher among graduate students than the general population, maintaining emotional wellbeing in graduate school is vital! Students must be prepared with skills that will not only help them perform well but also help them feel well. Thriving in Graduate School: The Expert's Guide to Success and Wellness is the first book on graduate student mental health written by mental health professionals. It promotes psychologically healthy approaches to navigating the graduate school experience and teaches students that they are not alone in their mental health struggles. The authors introduce students to unique perspectives that are key to positive mental health. Additionally, this is the only book of its type to explore issues routinely faced by historically marginalized graduate students. Special sections at the end of each chapter written for faculty, administrators, and mental health professionals augment the book by suggesting ways that each of these groups can help guide and support graduate students through their journey. Featuring vignettes and experiences from actual graduate students, Thriving in Graduate School sheds light on common—but hidden—truths to help students manage the many challenges they will face and even thrive during their graduate school years. Written with compassion and humor, this is a must read for prospective students and those who seek to support them. |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: Graphical Models Michael Irwin Jordan, Terrence Joseph Sejnowski, 2001 This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithm and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models.This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The selections range from foundational papers of historical importance to results at the cutting edge of research. Contributors H. Attias, C. M. Bishop, B. J. Frey, Z. Ghahramani, D. Heckerman, G. E. Hinton, R. Hofmann, R. A. Jacobs, Michael I. Jordan, H. J. Kappen, A. Krogh, R. Neal, S. K. Riis, F. B. Rodríguez, L. K. Saul, Terrence J. Sejnowski, P. Smyth, M. E. Tipping, V. Tresp, Y. Weiss |
| berkeley master of information and data science: Berkeley's Philosophy of Mathematics Douglas M. Jesseph, 1993-09-15 In this first modern, critical assessment of the place of mathematics in Berkeley's philosophy and Berkeley's place in the history of mathematics, Douglas M. Jesseph provides a bold reinterpretation of Berkeley's work. Jesseph challenges the prevailing view that Berkeley's mathematical writings are peripheral to his philosophy and argues that mathematics is in fact central to his thought, developing out of his critique of abstraction. Jesseph's argument situates Berkeley's ideas within the larger historical and intellectual context of the Scientific Revolution. Jesseph begins with Berkeley's radical opposition to the received view of mathematics in the philosophy of the late seventeenth and early eighteenth centuries, when mathematics was considered a science of abstractions. Since this view seriously conflicted with Berkeley's critique of abstract ideas, Jesseph contends that he was forced to come up with a nonabstract philosophy of mathematics. Jesseph examines Berkeley's unique treatments of geometry and arithmetic and his famous critique of the calculus in The Analyst. By putting Berkeley's mathematical writings in the perspective of his larger philosophical project and examining their impact on eighteenth-century British mathematics, Jesseph makes a major contribution to philosophy and to the history and philosophy of science. |
| berkeley master of information and data science: Financing a Graduate Education United States. Office of Education, Richard C. McKee, 1964 |
| berkeley master of information and data science: 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. |
| berkeley master of information and data science: It's Complicated Danah Boyd, 2014-02-25 Surveys the online social habits of American teens and analyzes the role technology and social media plays in their lives, examining common misconceptions about such topics as identity, privacy, danger, and bullying. |
| berkeley master of information and data science: Mechanical Engineering , 1984 History of the American society of mechanical engineers. Preliminary report of the committee on Society history, issued from time to time, beginning with v. 30, Feb. 1908. |
| berkeley master of information and data science: Multimedia Computing Gerald Friedland, Ramesh Jain, 2014-07-28 This innovative textbook presents an experiential, holistic approach to multimedia computing along with practical algorithms. |
| berkeley master of information and data science: Think Bayes Allen Downey, 2013-09-12 If you know how to program with Python, and know a little about probability, you're ready to tackle Bayesian statistics. This book shows you how to use Python code instead of math to help you learn Bayesian fundamentals. Once you get the math out of the way, you'll be able to apply these techniques to real-world problems. |
| berkeley master of information and data science: Machine Learning for Cybersecurity Cookbook Emmanuel Tsukerman, 2019-11-25 Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book. |
| berkeley master of information and data science: Creativity in Business Michael Ray, Rochelle Myers, 1988-12-24 This exploration of innovative thinking in companies of all kinds shows us how creativity in business can enrich us, and those who work with us. -- Spencer Johnson, co-author, The One Minute Manager |
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Berkeley is home to some of the world’s greatest minds leading more than 130 academic departments and 80 interdisciplinary research units and addressing the world’s most pertinent …
Admissions - University of California, Berkeley
The University of California, Berkeley, is the No. 1 public university in the world. Over 40,000 students attend classes in 15 colleges and schools, offering over 300 degree programs. Set …
Real-Time Interactive Earthquake Map - University of California, …
Legend. Earthquakes are shown as circles sized by magnitude (red, < 1 hour; blue, < 1 day, yellow, < 1 week). Click or tap on a circle to view more details about an earthquake, such as …
Home - Office of Undergraduate Admissions
Start your UC Berkeley journey by learning more about the basic admissions requirements that make Berkeley students stand out.
2024-25 Berkeley Academic Guide | Berkeley Academic Guide
Compare programs, find detailed degree requirements, discover faculty research specialties, and learn more about the unparalleled academic opportunities available to you at UC Berkeley.
Welcome to the UC Berkeley Library
The UC Berkeley Library helps current and future users find, evaluate, use and create knowledge to better the world.
Academics - University of California, Berkeley
Berkeley offers over 300 degree programs through its academic departments. Search with our A to Z guide.
Research - University of California, Berkeley
From expeditions to Egypt in the late 1800s to stem cell research and artificial intelligence today, Berkeley has been at the forefront of research throughout its history. Here students can work …
Schools & colleges - University of California, Berkeley
The Berkeley School of Education includes master’s and doctoral programs, teacher preparation, undergraduate minor program and leadership training.
Berkeley Global | UC Berkeley - University of California, Berkeley
Study abroad at UC Berkeley. Immerse yourself in campus life for a semester or academic year.
