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| data science advising berkeley: Communicating with Data Deborah Nolan, Sara Stoudt, 2021-03-25 Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component. Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication. |
| data science advising 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 advising 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 advising berkeley: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science advising 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 advising berkeley: Data Structures And Algorithms Shi-kuo Chang, 2003-09-29 This is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. The thirteen chapters, written by an international group of experienced teachers, cover the fundamental concepts of algorithms and most of the important data structures as well as the concept of interface design. The book contains many examples and diagrams. Whenever appropriate, program codes are included to facilitate learning.This book is supported by an international group of authors who are experts on data structures and algorithms, through its website at www.cs.pitt.edu/~jung/GrowingBook/, so that both teachers and students can benefit from their expertise. |
| data science advising berkeley: Making Parents Charis Thompson, 2005 Reproductive technologies, says Thompson, are part of the increasing tendency to turn social problems into biomedical questions and can be used as a lens to see the resulting changes in the relations between science and society.--BOOK JACKET. |
| data science advising berkeley: Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, Computer Science and Telecommunications Board, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on the Growth of Computer Science Undergraduate Enrollments, 2018-04-28 The field of computer science (CS) is currently experiencing a surge in undergraduate degree production and course enrollments, which is straining program resources at many institutions and causing concern among faculty and administrators about how best to respond to the rapidly growing demand. There is also significant interest about what this growth will mean for the future of CS programs, the role of computer science in academic institutions, the field as a whole, and U.S. society more broadly. Assessing and Responding to the Growth of Computer Science Undergraduate Enrollments seeks to provide a better understanding of the current trends in computing enrollments in the context of past trends. It examines drivers of the current enrollment surge, relationships between the surge and current and potential gains in diversity in the field, and the potential impacts of responses to the increased demand for computing in higher education, and it considers the likely effects of those responses on students, faculty, and institutions. This report provides recommendations for what institutions of higher education, government agencies, and the private sector can do to respond to the surge and plan for a strong and sustainable future for the field of CS in general, the health of the institutions of higher education, and the prosperity of the nation. |
| data science advising 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 advising berkeley: Learning Analytics Johann Ari Larusson, Brandon White, 2014-07-04 In education today, technology alone doesn't always lead to immediate success for students or institutions. In order to gauge the efficacy of educational technology, we need ways to measure the efficacy of educational practices in their own right. Through a better understanding of how learning takes place, we may work toward establishing best practices for students, educators, and institutions. These goals can be accomplished with learning analytics. Learning Analytics: From Research to Practice updates this emerging field with the latest in theories, findings, strategies, and tools from across education and technological disciplines. Guiding readers through preparation, design, and examples of implementation, this pioneering reference clarifies LA methods as not mere data collection but sophisticated, systems-based analysis with practical applicability inside the classroom and in the larger world. Case studies illustrate applications of LA throughout academic settings (e.g., intervention, advisement, technology design), and their resulting impact on pedagogy and learning. The goal is to bring greater efficiency and deeper engagement to individual students, learning communities, and educators, as chapters show diverse uses of learning analytics to: Enhance student and faculty performance. Improve student understanding of course material. Assess and attend to the needs of struggling learners. Improve accuracy in grading. Allow instructors to assess and develop their own strengths. Encourage more efficient use of resources at the institutional level. Researchers and practitioners in educational technology, IT, and the learning sciences will hail the information in Learning Analytics: From Research to Practice as a springboard to new levels of student, instructor, and institutional success. |
| data science advising berkeley: Information, Accountability, and Cumulative Learning Thad Dunning, Guy Grossman, Macartan Humphreys, Susan D. Hyde, Craig McIntosh, Gareth Nellis, 2019-07-11 Throughout the world, voters lack access to information about politicians, government performance, and public services. Efforts to remedy these informational deficits are numerous. Yet do informational campaigns influence voter behavior and increase democratic accountability? Through the first project of the Metaketa Initiative, sponsored by the Evidence in Governance and Politics (EGAP) research network, this book aims to address this substantive question and at the same time introduce a new model for cumulative learning that increases coordination among otherwise independent researcher teams. It presents the overall results (using meta-analysis) from six independently conducted but coordinated field experimental studies, the results from each individual study, and the findings from a related evaluation of whether practitioners utilize this information as expected. It also discusses lessons learned from EGAP's efforts to coordinate field experiments, increase replication of theoretically important studies across contexts, and increase the external validity of field experimental research. |
| data science advising berkeley: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| data science advising berkeley: The Promise of Access Daniel Greene, 2021 Based on fieldwork at three distinct sites in Washington, DC, this book finds that the persistent problem of poverty is often framed as a problem of technology-- |
| data science advising berkeley: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| data science advising berkeley: The Wall Street Professional's Survival Guide Roy Cohen, 2010-05-06 The Wall Street Professional’s Survival Guide: The Secrets of a Career Coach is the only complete, up-to-date, and practical guide for financial industry professionals seeking new or better jobs in today’s brutally competitive environment. Author Roy Cohen spent more than 10 years providing outplacement services to Goldman Sachs’ employees. In this book, he shares finance-specific job-hunting insights you simply won’t find anywhere else. Drawing on his immense experience helping financial industry professionals find and keep outstanding positions, Cohen tells you what to do when and if you’re fired (or ready to move), how to develop a “game plan” and search targets, how to build your “story”, how to move from the sell-side to the buy side, and much more. You’ll find industry-specific guidance on interview strategy, resumes, follow-up, references, and even negotiation with real examples drawn from Cohen’s own practice. |
| data science advising berkeley: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| data science advising berkeley: Risk Terrain Modeling Joel M. Caplan, Leslie W. Kennedy, 2016-06-28 Imagine using an evidence-based risk management model that enables researchers and practitioners alike to analyze the spatial dynamics of crime, allocate resources, and implement custom crime and risk reduction strategies that are transparent, measurable, and effective. Risk Terrain Modeling (RTM) diagnoses the spatial attractors of criminal behavior and makes accurate forecasts of where crime will occur at the microlevel. RTM informs decisions about how the combined factors that contribute to criminal behavior can be targeted, connections to crime can be monitored, spatial vulnerabilities can be assessed, and actions can be taken to reduce worst effects. As a diagnostic method, RTM offers a statistically valid way to identify vulnerable places. To learn more, visit http://www.riskterrainmodeling.com and begin using RTM with the many free tutorials and resources. |
| data science advising 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 advising berkeley: Global Families Catherine Ceniza Choy, 2013-10-11 In the last fifty years, transnational adoption—specifically, the adoption of Asian children—has exploded in popularity as an alternative path to family making. Despite the cultural acceptance of this practice, surprisingly little attention has been paid to the factors that allowed Asian international adoption to flourish. In Global Families, Catherine Ceniza Choy unearths the little-known historical origins of Asian international adoption in the United States. Beginning with the post-World War II presence of the U.S. military in Asia, she reveals how mixed-race children born of Japanese, Korean, and Vietnamese women and U.S. servicemen comprised one of the earliest groups of adoptive children. Based on extensive archival research, Global Families moves beyond one-dimensional portrayals of Asian international adoption as either a progressive form of U.S. multiculturalism or as an exploitative form of cultural and economic imperialism. Rather, Choy acknowledges the complexity of the phenomenon, illuminating both its radical possibilities of a world united across national, cultural, and racial divides through family formation and its strong potential for reinforcing the very racial and cultural hierarchies it sought to challenge. |
| data science advising berkeley: Search User Interfaces Marti A. Hearst, 2009-09-21 The truly world-wide reach of the Web has brought with it a new realisation of the enormous importance of usability and user interface design. In the last ten years, much has become understood about what works in search interfaces from a usability perspective, and what does not. Researchers and practitioners have developed a wide range of innovative interface ideas, but only the most broadly acceptable make their way into major web search engines. This book summarizes these developments, presenting the state of the art of search interface design, both in academic research and in deployment in commercial systems. Many books describe the algorithms behind search engines and information retrieval systems, but the unique focus of this book is specifically on the user interface. It will be welcomed by industry professionals who design systems that use search interfaces as well as graduate students and academic researchers who investigate information systems. |
| data science advising berkeley: Metabase Up and Running TIM. ABRAHAM, 2020-09-30 Ask questions of your data and gain insights to make better business decisions using the open source business intelligence tool, Metabase Key Features Deploy Metabase applications to let users across your organization interact with it Learn to create data visualizations, charts, reports, and dashboards with the help of a variety of examples Understand how to embed Metabase into your website and send out reports automatically using email and Slack Book Description Metabase is an open source business intelligence tool that helps you use data to answer questions about your business. This book will give you a detailed introduction to using Metabase in your organization to get the most value from your data. You'll start by installing and setting up Metabase on your local computer. You'll then progress to handling the administration aspect of Metabase by learning how to configure and deploy Metabase, manage accounts, and execute administrative tasks such as adding users and creating permissions and metadata. Complete with examples and detailed instructions, this book shows you how to create different visualizations, charts, and dashboards to gain insights from your data. As you advance, you'll learn how to share the results with peers in your organization and cover production-related aspects such as embedding Metabase and auditing performance. Throughout the book, you'll explore the entire data analytics process-from connecting your data sources, visualizing data, and creating dashboards through to daily reporting. By the end of this book, you'll be ready to implement Metabase as an integral tool in your organization. What you will learn Explore different types of databases and find out how to connect them to Metabase Deploy and host Metabase securely using Amazon Web Services Use Metabase's user interface to filter and aggregate data on single and multiple tables Become a Metabase admin by learning how to add users and create permissions Answer critical questions for your organization by using the Notebook editor and writing SQL queries Use the search functionality to search through tables, dashboards, and metrics Who this book is for This book is for business analysts, data analysts, data scientists, and other professionals who want to become well-versed with business intelligence and analytics using Metabase. This book will also appeal to anyone who wants to understand their data to extract meaningful insights with the help of practical examples. A basic understanding of data handling and processing is necessary to get started with this book. |
| data science advising berkeley: 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. |
| data science advising berkeley: The Evolution of U.S. Foreign Policy Willis M. Smyser, 1960 |
| data science advising berkeley: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data. |
| data science advising berkeley: Graduate Admissions Essays, Fifth Edition Donald Asher, 2024-07-16 The fully updated fifth edition of the go-to guide for crafting winning essays for any type of graduate program or scholarship, including PhD, master's, MD, JD, Rhodes, and postdocs, with brand-new essays and the latest hot tips and secret techniques. Based on thousands of interviews with successful grad students and admissions officers, Graduate Admissions Essays deconstructs and demystifies the ever-challenging application process for getting into graduate and scholarship programs. The book presents: Sample essays in a comprehensive range of subjects, including some available from no other source: medical residencies, postdocs, elite fellowships, academic autobiographies, and more! The latest on AI, the GRE, and diversity and adversity essays. Detailed strategies that have proven successful for some of the most competitive graduate programs in the country (learn how to beat 1% admissions rates!). How to get strong letters of recommendation, how to get funding when they say they have no funding, and how to appeal for more financial aid. Brand-new sample supplemental application letters, letters to faculty mentors, and letters of continuing interest. Full of Dr. Donald Asher's expert advice, this is the perfect graduate application resource whether you're fresh out of college and eager to get directly into graduate school or decades into your career and looking for a change. |
| data science advising berkeley: Proofs that Really Count Arthur T. Benjamin, Jennifer J. Quinn, 2022-09-21 Mathematics is the science of patterns, and mathematicians attempt to understand these patterns and discover new ones using a variety of tools. In Proofs That Really Count, award-winning math professors Arthur Benjamin and Jennifer Quinn demonstrate that many number patterns, even very complex ones, can be understood by simple counting arguments. The book emphasizes numbers that are often not thought of as numbers that count: Fibonacci Numbers, Lucas Numbers, Continued Fractions, and Harmonic Numbers, to name a few. Numerous hints and references are given for all chapter exercises and many chapters end with a list of identities in need of combinatorial proof. The extensive appendix of identities will be a valuable resource. This book should appeal to readers of all levels, from high school math students to professional mathematicians. |
| data science advising berkeley: Gendering the Trans-Pacific World , 2017-03-06 As the inaugural volume of the new Brill book series Gendering the Trans-Pacific World: Diaspora, Empire, and Race, this anthology presents an emergent interdisciplinary and multidisciplinary field that highlights the inextricable link between gender and the trans-Pacific world. The anthology features twenty-one chapters by new and established scholars and writers. They collectively examine the geographies of empire, the significance of intimacy and affect, the importance of beauty and the body, and the circulation of culture. This is an ideal volume to introduce advanced undergraduate and graduate students to Transpacific Studies and gender as a category of analysis. Gendering the Trans-Pacific World: Diaspora, Empire, and Race is now available in paperback for individual customers. |
| data science advising berkeley: Thermodynamics Enrico Fermi, 2012-04-25 In this classic of modern science, the Nobel laureate presents a clear treatment of systems, the First and Second Laws of Thermodynamics, entropy, thermodynamic potentials, and much more. Calculus required. |
| data science advising berkeley: World Inequality Report 2022 Lucas Chancel, Thomas Piketty, Emmanuel Saez, Gabriel Zucman, 2022-11 World Inequality Report 2022 is the most authoritative and comprehensive account of global trends in inequality, providing cutting-edge information about income and wealth inequality and also pioneering data about the history of inequality, gender inequality, environmental inequalities, and trends in international tax reform and redistribution. |
| data science advising berkeley: The Loss of Hindustan Manan Ahmed Asif, 2020-11-24 Shortlisted for the Cundill History Prize “Remarkable and pathbreaking...A radical rethink of colonial historiography and a compelling argument for the reassessment of the historical traditions of Hindustan.” —Mahmood Mamdani “The brilliance of Asif’s book rests in the way he makes readers think about the name ‘Hindustan’...Asif’s focus is Indian history but it is, at the same time, a lens to look at questions far bigger.” —Soni Wadhwa, Asian Review of Books “Remarkable...Asif’s analysis and conclusions are powerful and poignant.” —Rudrangshu Mukherjee, The Wire “A tremendous contribution...This is not only a book that you must read, but also one that you must chew over and debate.” —Audrey Truschke, Current History Did India, Pakistan, and Bangladesh have a shared regional identity prior to the arrival of Europeans in the late fifteenth century? Manan Ahmed Asif tackles this contentious question by inviting us to reconsider the work and legacy of the influential historian Muhammad Qasim Firishta, a contemporary of the Mughal emperors Akbar and Jahangir. Inspired by his reading of Firishta and other historians, Asif seeks to rescue our understanding of the region from colonial narratives that emphasize difference and division. Asif argues that a European understanding of India as Hindu has replaced an earlier, native understanding of India as Hindustan, a home for all faiths. Turning to the subcontinent’s medieval past, he uncovers a rich network of historians of Hindustan who imagined, studied, and shaped their kings, cities, and societies. The Loss of Hindustan reveals how multicultural Hindustan was deliberately eclipsed in favor of the religiously partitioned world of today. A magisterial work with far reaching implications, it offers a radical reinterpretation of how India came to its contemporary political identity. |
| data science advising berkeley: City of Walls Teresa P. R. Caldeira, 2000 This is an extraordinary treatment of a difficult problem. . . . Much more than a conventional comparative study, City of Walls is a genuinely transcultural, transnational work—the first of its kind that I have read.—George E. Marcus, author of Ethnography Through Thick & Thin Caldeira's work is wonderfully ambitious-theoretically bold, ethnographically rich, historically specific. Anyone who cares about the condition and future of cities, of democracy, of human rights should read this book.—Thomas Bender, Director of the Project on Cities and Urban Knowledges City of Walls is a brilliant analysis of the dynamics of urban fear. The sophistication of Caldeira's arguments should stimulate new discussion of cities and urban life. Its significance goes far beyond the borders of Brazil.—Margaret Crawford, Professor of Urban Planning and Design Theory, Graduate School of Design, Harvard University Caldeira's insight illuminates the geography of the city as well as the boundaries—or the lack of boundaries—of violence.—Paul Chevigny, author of Edge of the Knife: Police Violence in the Americas An extraordinary account of violence in the city. . . . Caldeira brings to this task a rare depth of knowledge and understanding.—Saskia Sassen, author of Globalization and Its Discontents An outstanding contribution to understanding authoritarian continuity under political reform. Caldeira has written a brilliant and bleak analysis on the many challenges and obstacles which government and civil society face in new democracies.—Paulo Sérgio Pinheiro, Director of the Center for the Study of Violence, University of São Paulo and Member of the United Nations Sub-Commission for the Promotion and Protection of Human Rights |
| data science advising 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 advising berkeley: Empire of Care Catherine Ceniza Choy, 2003-01-31 In western countries, including the United States, foreign-trained nurses constitute a crucial labor supply. Far and away the largest number of these nurses come from the Philippines. Why is it that a developing nation with a comparatively greater need for trained medical professionals sends so many of its nurses to work in wealthier countries? Catherine Ceniza Choy engages this question through an examination of the unique relationship between the professionalization of nursing and the twentieth-century migration of Filipinos to the United States. The first book-length study of the history of Filipino nurses in the United States, Empire of Care brings to the fore the complicated connections among nursing, American colonialism, and the racialization of Filipinos. Choy conducted extensive interviews with Filipino nurses in New York City and spoke with leading Filipino nurses across the United States. She combines their perspectives with various others—including those of Philippine and American government and health officials—to demonstrate how the desire of Filipino nurses to migrate abroad cannot be reduced to economic logic, but must instead be understood as a fundamentally transnational process. She argues that the origins of Filipino nurse migrations do not lie in the Philippines' independence in 1946 or the relaxation of U.S. immigration rules in 1965, but rather in the creation of an Americanized hospital training system during the period of early-twentieth-century colonial rule. Choy challenges celebratory narratives regarding professional migrants’ mobility by analyzing the scapegoating of Filipino nurses during difficult political times, the absence of professional solidarity between Filipino and American nurses, and the exploitation of foreign-trained nurses through temporary work visas. She shows how the culture of American imperialism persists today, continuing to shape the reception of Filipino nurses in the United States. |
| data science advising berkeley: Statistical Physics and Thermodynamics Jochen Rau, 2017 Statistical physics and thermodynamics describe the behaviour of systems on the macroscopic scale. Their methods are applicable to a wide range of phenomena, from neutron stars to heat engines, or from chemical reactions to phase transitions. The pertinent laws are among the most universal ones of all laws of physics. |
| data science advising berkeley: Effective Personal Tutoring in Higher Education Dave Lochtie, Emily McIntosh, Andrew Stork, Ben W Walker, 2018-10-08 This is an important title for all academic and professional staff within higher education (HE) who have a personal tutoring, student support or advising role. It examines key topics in relation to tutoring including definitions, coaching, core values and skills, boundaries, monitoring students, undertaking group and individual tutorials and the need to measure impact. Throughout, the text encourages reflection and the need to think critically about the role of the personal tutor. A scholarly and practical text, it comprehensively brings together relevant academic literature to inform tutoring practice as well as contextualising the role within the HE policy and quality assurance landscape. Please also see the forthcoming The Higher Education Personal Tutor’s and Advisor’s Companion where the themes of this book are illustrated by real life case studies form universities around the UK. |
| data science advising berkeley: Do Zombies Dream of Undead Sheep? Timothy Verstynen, Bradley Voytek, 2016-10-04 A look at the true nature of the zombie brain Even if you've never seen a zombie movie or television show, you could identify an undead ghoul if you saw one. With their endless wandering, lumbering gait, insatiable hunger, antisocial behavior, and apparently memory-less existence, zombies are the walking nightmares of our deepest fears. What do these characteristic behaviors reveal about the inner workings of the zombie mind? Could we diagnose zombism as a neurological condition by studying their behavior? In Do Zombies Dream of Undead Sheep?, neuroscientists and zombie enthusiasts Timothy Verstynen and Bradley Voytek apply their neuro-know-how to dissect the puzzle of what has happened to the zombie brain to make the undead act differently than their human prey. Combining tongue-in-cheek analysis with modern neuroscientific principles, Verstynen and Voytek show how zombism can be understood in terms of current knowledge regarding how the brain works. In each chapter, the authors draw on zombie popular culture and identify a characteristic zombie behavior that can be explained using neuroanatomy, neurophysiology, and brain-behavior relationships. Through this exploration they shed light on fundamental neuroscientific questions such as: How does the brain function during sleeping and waking? What neural systems control movement? What is the nature of sensory perception? Walking an ingenious line between seriousness and satire, Do Zombies Dream of Undead Sheep? leverages the popularity of zombie culture in order to give readers a solid foundation in neuroscience. |
| data science advising berkeley: Machine Learning and Data Sciences for Financial Markets Agostino Capponi, Charles-Albert Lehalle, 2023-04-30 Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory. |
| data science advising berkeley: Innovation Engineering Ikhlaq Sidhu, 2019-09-12 Innovation Engineering is a practical guide to creating anything new - whether in a large firm, research lab, new venture or even in an innovative student project. As an executive, are you happy with the return on investment of your innovative projects? As an innovator, do you feel confident that you can navigate obstacles and achieve success with your innovative project? The reality is that most innovation projects fail. The challenge in developing any new technology, application, or venture is that the innovator must be able to execute while also learning. Innovation Engineering, developed and used at UC Berkeley, provides the tactical process, leadership, and behaviors necessary for successful innovation projects. Our validation tests have shown that teams which properly use Innovation Engineering accomplished their innovative projects approximately 4X faster than and with higher quality results. They also on-board new team members faster, they have much fewer unnecessary meetings, and they even report a more positive outlook on the project itself. Inter-woven between the chapters are real-life case studies with some of the world's most successful innovators to provide context, patterns, and playbooks that you can follow. Highly applied, and very realistic, Innovation Engineering builds on 30 years of technology innovation projects within large firms, advanced development labs, and new ventures at UC Berkeley, in Silicon Valley, and globally. If your goal is to create something new and have it successfully used in real life, this book is for you. |
| data science advising berkeley: The Product Manager Interview Lewis C. Lin, 2017-11-06 NOTE: This is the NEWER 3rd edition for the book formerly titled PM Interview Questions. -- 164 Actual PM Interview Questions From the creator of the CIRCLES Method(TM), The Product Manager Interview is a resource you don't want to miss. The world's expert in product management interviews, Lewis C. Lin, gives readers 164 practice questions to gain product management (PM) proficiency and master the PM interview including: Google Facebook Amazon Uber Dropbox Microsoft Fully Solved Solutions The book contains fully solved solutions so readers can learn, improve and do their best at the PM interview. Here are questions and sample answers you'll find in the book: Product Design How would you design an ATM for elderly people? Should Google build a Comcast-like TV cable service? Instagram currently supports 3 to 15 second videos. We're considering supporting videos of unlimited length. How would you modify the UX to accommodate this? Pricing How would you go about pricing UberX or any other new Uber product? Let's say Google created a teleporting device: which market segments would you go after? How would you price it? Metrics Imagine you are the Amazon Web Services (AWS) PM in Sydney. What are the top three metrics you'd look at? Facebook users have declined 20 percent week over week. Diagnose the problem. How would you fix the issue? Ideal Complement to Decode and Conquer Many of you have read the PM interview frameworks revealed in Decode and Conquer, including the CIRCLES(TM), AARM(TM) and DIGS(TM) Methods. The Product Manager Interview is the perfect complement to Decode and Conquer. With over 160 practice questions, you'll see what the best PM interview responses look and feel like. Brand New Third Edition Many of the sample answers have been re-written from scratch. The sample answers are now stronger and easier to follow. In total, thousands of changes have made in this brand new third edition of the book. Preferred by the World's Top Universities Here's what students and staff have to say about the Lewis C. Lin: DUKE UNIVERSITY I was so touched by your presentation this morning. It was really helpful. UNIVERSITY OF MICHIGAN I can say your class is the best that I have ever attended. I will definitely use knowledge I learned today for future interviews. COLUMBIA UNIVERSITY I'd like to let you know that your workshop today is super awesome! It's the best workshop I have been to since I came to Columbia Business School. Thank you very much for the tips, frameworks, and the very clear and well-structured instruction! UNIVERSITY OF TEXAS AT AUSTIN I wanted to reiterate how much I enjoyed your workshops today. Thank you so much for taking time out and teaching us about these much-needed principles and frameworks. I actually plan to print out a few slides and paste them on my walls! CARNEGIE MELLON UNIVERSITY I'm a very big admirer of your work. We, at Tepper, follow your books like the Bible. As a former associate product manager, I was able to connect your concepts back to my work experience back and Pragmatic Marketing training. I'm really looking forward to apply your teachings. |
| data science advising berkeley: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-09-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnational ...
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a broader scientific community to benefit from the identified …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time delay; Understandable in a way that allows …
Belmont Forum Adopts Open Data Principles for Environmental Change Research
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in June, 2015. “A Place to Stand” is the …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, prevents fraud and thereby builds trust in …
