| case western data science: Data Science and Digital Business Fausto Pedro García Márquez, Benjamin Lev, 2019-01-04 This book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business. |
| case western data science: Calling Bullshit Carl T. Bergstrom, Jevin D. West, 2021-04-20 Bullshit isn’t what it used to be. Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data. “A modern classic . . . a straight-talking survival guide to the mean streets of a dying democracy and a global pandemic.”—Wired Misinformation, disinformation, and fake news abound and it’s increasingly difficult to know what’s true. Our media environment has become hyperpartisan. Science is conducted by press release. Startup culture elevates bullshit to high art. We are fairly well equipped to spot the sort of old-school bullshit that is based in fancy rhetoric and weasel words, but most of us don’t feel qualified to challenge the avalanche of new-school bullshit presented in the language of math, science, or statistics. In Calling Bullshit, Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data. You don’t need a lot of technical expertise to call out problems with data. Are the numbers or results too good or too dramatic to be true? Is the claim comparing like with like? Is it confirming your personal bias? Drawing on a deep well of expertise in statistics and computational biology, Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization, distinguish between correlation and causation, and examine the susceptibility of science to modern bullshit. We have always needed people who call bullshit when necessary, whether within a circle of friends, a community of scholars, or the citizenry of a nation. Now that bullshit has evolved, we need to relearn the art of skepticism. |
| case western data science: How to Lie with Statistics Darrell Huff, 2010-12-07 If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled. |
| case western data science: Willpower Roy F. Baumeister, John Tierney, 2011-09-01 One of the world's most esteemed and influential psychologists, Roy F. Baumeister, teams with New York Times science writer John Tierney to reveal the secrets of self-control and how to master it. Deep and provocative analysis of people's battle with temptation and masterful insights into understanding willpower: why we have it, why we don't, and how to build it. A terrific read. —Ravi Dhar, Yale School of Management, Director of Center for Customer Insights Pioneering research psychologist Roy F. Baumeister collaborates with New York Times science writer John Tierney to revolutionize our understanding of the most coveted human virtue: self-control. Drawing on cutting-edge research and the wisdom of real-life experts, Willpower shares lessons on how to focus our strength, resist temptation, and redirect our lives. It shows readers how to be realistic when setting goals, monitor their progress, and how to keep faith when they falter. By blending practical wisdom with the best of recent research science, Willpower makes it clear that whatever we seek—from happiness to good health to financial security—we won’t reach our goals without first learning to harness self-control. |
| case western data science: Bioinformatics for Systems Biology Stephen Krawetz, 2008-12-11 Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward. |
| case western data science: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| case western data science: Enlightenment Now Steven Pinker, 2018-02-13 INSTANT NEW YORK TIMES BESTSELLER A NEW YORK TIMES NOTABLE BOOK OF 2018 ONE OF THE ECONOMIST'S BOOKS OF THE YEAR My new favorite book of all time. --Bill Gates If you think the world is coming to an end, think again: people are living longer, healthier, freer, and happier lives, and while our problems are formidable, the solutions lie in the Enlightenment ideal of using reason and science. By the author of the new book, Rationality. Is the world really falling apart? Is the ideal of progress obsolete? In this elegant assessment of the human condition in the third millennium, cognitive scientist and public intellectual Steven Pinker urges us to step back from the gory headlines and prophecies of doom, which play to our psychological biases. Instead, follow the data: In seventy-five jaw-dropping graphs, Pinker shows that life, health, prosperity, safety, peace, knowledge, and happiness are on the rise, not just in the West, but worldwide. This progress is not the result of some cosmic force. It is a gift of the Enlightenment: the conviction that reason and science can enhance human flourishing. Far from being a naïve hope, the Enlightenment, we now know, has worked. But more than ever, it needs a vigorous defense. The Enlightenment project swims against currents of human nature--tribalism, authoritarianism, demonization, magical thinking--which demagogues are all too willing to exploit. Many commentators, committed to political, religious, or romantic ideologies, fight a rearguard action against it. The result is a corrosive fatalism and a willingness to wreck the precious institutions of liberal democracy and global cooperation. With intellectual depth and literary flair, Enlightenment Now makes the case for reason, science, and humanism: the ideals we need to confront our problems and continue our progress. |
| case western data science: Data Reduction and Error Analysis for the Physical Sciences Philip R. Bevington, D. Keith Robinson, 1992 This book is designed as a laboratory companion, student textbook or reference book for professional scientists. The text is for use in one-term numerical analysis, data and error analysis, or computer methods courses, or for laboratory use. It is for the sophomore-junior level, and calculus is a prerequisite. The new edition includes applications for PC use. |
| case western data science: Algorithms of Oppression Safiya Umoja Noble, 2018-02-20 Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author |
| case western data science: Handbook of Computational Social Science, Volume 1 Uwe Engel, Anabel Quan-Haase, Sunny Liu, Lars E Lyberg, 2021-11-10 The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors. |
| case western data science: Active Learning in College Science Joel J. Mintzes, Emily M. Walter, 2020-02-23 This book explores evidence-based practice in college science teaching. It is grounded in disciplinary education research by practicing scientists who have chosen to take Wieman’s (2014) challenge seriously, and to investigate claims about the efficacy of alternative strategies in college science teaching. In editing this book, we have chosen to showcase outstanding cases of exemplary practice supported by solid evidence, and to include practitioners who offer models of teaching and learning that meet the high standards of the scientific disciplines. Our intention is to let these distinguished scientists speak for themselves and to offer authentic guidance to those who seek models of excellence. Our primary audience consists of the thousands of dedicated faculty and graduate students who teach undergraduate science at community and technical colleges, 4-year liberal arts institutions, comprehensive regional campuses, and flagship research universities. In keeping with Wieman’s challenge, our primary focus has been on identifying classroom practices that encourage and support meaningful learning and conceptual understanding in the natural sciences. The content is structured as follows: after an Introduction based on Constructivist Learning Theory (Section I), the practices we explore are Eliciting Ideas and Encouraging Reflection (Section II); Using Clickers to Engage Students (Section III); Supporting Peer Interaction through Small Group Activities (Section IV); Restructuring Curriculum and Instruction (Section V); Rethinking the Physical Environment (Section VI); Enhancing Understanding with Technology (Section VII), and Assessing Understanding (Section VIII). The book’s final section (IX) is devoted to Professional Issues facing college and university faculty who choose to adopt active learning in their courses. The common feature underlying all of the strategies described in this book is their emphasis on actively engaging students who seek to make sense of natural objects and events. Many of the strategies we highlight emerge from a constructivist view of learning that has gained widespread acceptance in recent years. In this view, learners make sense of the world by forging connections between new ideas and those that are part of their existing knowledge base. For most students, that knowledge base is riddled with a host of naïve notions, misconceptions and alternative conceptions they have acquired throughout their lives. To a considerable extent, the job of the teacher is to coax out these ideas; to help students understand how their ideas differ from the scientifically accepted view; to assist as students restructure and reconcile their newly acquired knowledge; and to provide opportunities for students to evaluate what they have learned and apply it in novel circumstances. Clearly, this prescription demands far more than most college and university scientists have been prepared for. |
| case western data science: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| case western data science: Waste Kate O'Neill, 2019-09-04 Waste is one of the planet’s last great resource frontiers. From furniture made from up-cycled wood to gold extracted from computer circuit boards, artisans and multinational corporations alike are finding ways to profit from waste while diverting materials from overcrowded landfills. Yet beyond these benefits, this “new” resource still poses serious risks to human health and the environment. In this unique book, Kate O’Neill traces the emergence of the global political economy of wastes over the past two decades. She explains how the emergence of waste governance initiatives and mechanisms can help us deal with both the risks and the opportunities associated with the hundreds of millions – possibly billions – of tons of waste we generate each year. Drawing on a range of fascinating case studies to develop her arguments, including China’s role as the primary recipient of recyclable plastics and scrap paper from the Western world, “Zero-Waste” initiatives, the emergence of transnational waste-pickers’ alliances, and alternatives for managing growing volumes of electronic and food wastes, O’Neill shows how waste can be a risk, a resource, and even a livelihood, with implications for governance at local, national, and global levels. |
| case western data science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| case western data science: Reading The Tale of Genji Thomas Harper, Haruo Shirane, 2015-12-01 The Tale of Genji, written one thousand years ago, is a masterpiece of Japanese literature, is often regarded as the best prose fiction in the language. Read, commented on, and reimagined by poets, scholars, dramatists, artists, and novelists, the tale has left a legacy as rich and reflective as the work itself. This sourcebook is the most comprehensive record of the reception of The Tale of Genji to date. It presents a range of landmark texts relating to the work during its first millennium, almost all of which are translated into English for the first time. An introduction prefaces each set of documents, situating them within the tradition of Japanese literature and cultural history. These texts provide a fascinating glimpse into Japanese views of literature, poetry, imperial politics, and the place of art and women in society. Selections include an imagined conversation among court ladies gossiping about their favorite characters and scenes in Genji; learned exegetical commentary; a vigorous debate over the morality of Genji; and an impassioned defense of Genji's ability to enhance Japan's standing among the twentieth century's community of nations. Taken together, these documents reflect Japan's fraught history with vernacular texts, particularly those written by women. |
| case western data science: Big Data Management Fausto Pedro García Márquez, Benjamin Lev, 2016-11-15 This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples. |
| case western data science: Social Sensing Dong Wang, Tarek Abdelzaher, Lance Kaplan, 2015-04-17 Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion. Offers a unique interdisciplinary perspective bridging social networks, big data, cyber-physical systems, and reliability Presents novel theoretical foundations for assured social sensing and modeling humans as sensors Includes case studies and application examples based on real data sets Supplemental material includes sample datasets and fact-finding software that implements the main algorithms described in the book |
| case western data science: Deep Learning and Missing Data in Engineering Systems Collins Achepsah Leke, Tshilidzi Marwala, 2019-02-04 Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation. |
| case western data science: Graduate Education and the Public Good , 2008 |
| case western data science: Graph Data Management George Fletcher, Jan Hidders, Josep Lluís Larriba-Pey, 2018-10-31 This book presents a comprehensive overview of fundamental issues and recent advances in graph data management. Its aim is to provide beginning researchers in the area of graph data management, or in fields that require graph data management, an overview of the latest developments in this area, both in applied and in fundamental subdomains. The topics covered range from a general introduction to graph data management, to more specialized topics like graph visualization, flexible queries of graph data, parallel processing, and benchmarking. The book will help researchers put their work in perspective and show them which types of tools, techniques and technologies are available, which ones could best suit their needs, and where there are still open issues and future research directions. The chapters are contributed by leading experts in the relevant areas, presenting a coherent overview of the state of the art in the field. Readers should have a basic knowledge of data management techniques as they are taught in computer science MSc programs. |
| case western data science: Business Intelligence and Data Mining Anil Maheshwari, 2014-12-31 “This book is a splendid and valuable addition to this subject. The whole book is well written and I have no hesitation to recommend that this can be adapted as a textbook for graduate courses in Business Intelligence and Data Mining.” Dr. Edi Shivaji, Des Moines, Iowa “As a complete novice to this area just starting out on a MBA course I found the book incredibly useful and very easy to follow and understand. The concepts are clearly explained and make it an easy task to gain an understanding of the subject matter.” -- Mr. Craig Domoney, South Africa. Business Intelligence and Data Mining is a conversational and informative book in the exploding area of Business Analytics. Using this book, one can easily gain the intuition about the area, along with a solid toolset of major data mining techniques and platforms. This book can thus be gainfully used as a textbook for a college course. It is also short and accessible enough for a busy executive to become a quasi-expert in this area in a couple of hours. Every chapter begins with a case-let from the real world, and ends with a case study that runs across the chapters. |
| case western data science: Measure for measure. The tempest William Shakespeare, 1902 |
| case western data science: Race, Class and Education (RLE Edu L) Len Barton, Stephen A Walker, 2012-05-04 One problem which continues to absorb social scientists is the way in which so much social deprivation stems from racial or class status. The discussion in this book is developed in two ways: firstly, careful attention is given to an examination of the way minority groups create and maintain collective identities and action. Secondly, the relationship between this movement and such topics as racism in schools, schooling, unemployment and West Indian involvement in sporting rather than academic activities is analysed, together with the nature of the educational experience of different class and gender groups. |
| case western data science: Working Effectively with Legacy Code Michael Feathers, 2004-09-22 Get more out of your legacy systems: more performance, functionality, reliability, and manageability Is your code easy to change? Can you get nearly instantaneous feedback when you do change it? Do you understand it? If the answer to any of these questions is no, you have legacy code, and it is draining time and money away from your development efforts. In this book, Michael Feathers offers start-to-finish strategies for working more effectively with large, untested legacy code bases. This book draws on material Michael created for his renowned Object Mentor seminars: techniques Michael has used in mentoring to help hundreds of developers, technical managers, and testers bring their legacy systems under control. The topics covered include Understanding the mechanics of software change: adding features, fixing bugs, improving design, optimizing performance Getting legacy code into a test harness Writing tests that protect you against introducing new problems Techniques that can be used with any language or platform—with examples in Java, C++, C, and C# Accurately identifying where code changes need to be made Coping with legacy systems that aren't object-oriented Handling applications that don't seem to have any structure This book also includes a catalog of twenty-four dependency-breaking techniques that help you work with program elements in isolation and make safer changes. |
| case western data science: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| case western data science: Biological & Agricultural Index , 1919 |
| case western data science: Science in the Archives Lorraine Daston, 2017-04-04 Archives bring to mind rooms filled with old papers and dusty artifacts. But for scientists, the detritus of the past can be a treasure trove of material vital to present and future research: fossils collected by geologists; data banks assembled by geneticists; weather diaries trawled by climate scientists; libraries visited by historians. These are the vital collections, assembled and maintained over decades, centuries, and even millennia, which define the sciences of the archives. With Science in the Archives, Lorraine Daston and her co-authors offer the first study of the important role that these archives play in the natural and human sciences. Reaching across disciplines and centuries, contributors cover episodes in the history of astronomy, geology, genetics, philology, climatology, medicine, and more—as well as fundamental practices such as collecting, retrieval, and data mining. Chapters cover topics ranging from doxology in Greco-Roman Antiquity to NSA surveillance techniques of the twenty-first century. Thoroughly exploring the practices, politics, economics, and potential of the sciences of the archives, this volume reveals the essential historical dimension of the sciences, while also adding a much-needed long-term perspective to contemporary debates over the uses of Big Data in science. |
| case western data science: Data Science Jianchao Zeng, Pinle Qin, Weipeng Jing, Xianhua Song, Zeguang Lu, 2021-09-10 This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021. The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo. |
| case western data science: The Chief Reinvention Officer Handbook Nadya Zhexembayeva, 2020-07-14 |
| case western data science: Integrating Multiscale Observations of U.S. Waters National Research Council, Division on Earth and Life Studies, Water Science and Technology Board, Committee on Integrated Observations for Hydrologic and Related Sciences, 2008-05-16 Water is essential to life for humans and their food crops, and for ecosystems. Effective water management requires tracking the inflow, outflow, quantity and quality of ground-water and surface water, much like balancing a bank account. Currently, networks of ground-based instruments measure these in individual locations, while airborne and satellite sensors measure them over larger areas. Recent technological innovations offer unprecedented possibilities to integrate space, air, and land observations to advance water science and guide management decisions. This book concludes that in order to realize the potential of integrated data, agencies, universities, and the private sector must work together to develop new kinds of sensors, test them in field studies, and help users to apply this information to real problems. |
| case western data science: Hiring and Firing Public Officials Justin Buchler, 2011-04-20 As Justin Buchler shows, an election is a mechanism by which voters hire and fire public officials. It is not a consumer product market--it is a single employment decision. Thus, the health of democracy depends not on regular competitive elections, but on posing a credible threat to fire public officials who do not perform their jobs well....Thus, competitive elections, by most definitions, are indicative of a failure of the democratic system -- from cover. |
| case western data science: Fostering Integrity in Research National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Committee on Responsible Science, 2018-01-13 The integrity of knowledge that emerges from research is based on individual and collective adherence to core values of objectivity, honesty, openness, fairness, accountability, and stewardship. Integrity in science means that the organizations in which research is conducted encourage those involved to exemplify these values in every step of the research process. Understanding the dynamics that support †or distort †practices that uphold the integrity of research by all participants ensures that the research enterprise advances knowledge. The 1992 report Responsible Science: Ensuring the Integrity of the Research Process evaluated issues related to scientific responsibility and the conduct of research. It provided a valuable service in describing and analyzing a very complicated set of issues, and has served as a crucial basis for thinking about research integrity for more than two decades. However, as experience has accumulated with various forms of research misconduct, detrimental research practices, and other forms of misconduct, as subsequent empirical research has revealed more about the nature of scientific misconduct, and because technological and social changes have altered the environment in which science is conducted, it is clear that the framework established more than two decades ago needs to be updated. Responsible Science served as a valuable benchmark to set the context for this most recent analysis and to help guide the committee's thought process. Fostering Integrity in Research identifies best practices in research and recommends practical options for discouraging and addressing research misconduct and detrimental research practices. |
| case western data science: Psychotherapy and personality change Carl Ransom Rogers, 1969 |
| case western data science: The Art of Statistics David Spiegelhalter, 2019-09-03 In this important and comprehensive guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs. |
| case western data science: Case Studies for Ethics in Academic Research in the Social Sciences Leisa Reinecke Flynn, Ronald E. Goldsmith, 2013 This book provides a basis for class discussion about the responsible conduct of social science research. These 16 brief research ethics cases describe situations in which ethical dilemmas arise and present the student with the opportunity to think through the different implications for researchers. The cases emphasize different types of ethical dilemmas involving faculty, students, participants, and stakeholders. Students can discuss what happened, why it was or was not unethical, and what should be the consequences for the actors. Included are the original cases complete with learning objectives, teaching notes, and questions for discussion. |
| case western data science: Beethoven and the Age of Revolution Frida Knight, 1973 |
| case western data science: The Immortal Life of Henrietta Lacks Rebecca Skloot, 2010-02-02 #1 NEW YORK TIMES BESTSELLER • “The story of modern medicine and bioethics—and, indeed, race relations—is refracted beautifully, and movingly.”—Entertainment Weekly NOW A MAJOR MOTION PICTURE FROM HBO® STARRING OPRAH WINFREY AND ROSE BYRNE • ONE OF THE “MOST INFLUENTIAL” (CNN), “DEFINING” (LITHUB), AND “BEST” (THE PHILADELPHIA INQUIRER) BOOKS OF THE DECADE • ONE OF ESSENCE’S 50 MOST IMPACTFUL BLACK BOOKS OF THE PAST 50 YEARS • WINNER OF THE CHICAGO TRIBUNE HEARTLAND PRIZE FOR NONFICTION NAMED ONE OF THE BEST BOOKS OF THE YEAR BY The New York Times Book Review • Entertainment Weekly • O: The Oprah Magazine • NPR • Financial Times • New York • Independent (U.K.) • Times (U.K.) • Publishers Weekly • Library Journal • Kirkus Reviews • Booklist • Globe and Mail Her name was Henrietta Lacks, but scientists know her as HeLa. She was a poor Southern tobacco farmer who worked the same land as her slave ancestors, yet her cells—taken without her knowledge—became one of the most important tools in medicine: The first “immortal” human cells grown in culture, which are still alive today, though she has been dead for more than sixty years. HeLa cells were vital for developing the polio vaccine; uncovered secrets of cancer, viruses, and the atom bomb’s effects; helped lead to important advances like in vitro fertilization, cloning, and gene mapping; and have been bought and sold by the billions. Yet Henrietta Lacks remains virtually unknown, buried in an unmarked grave. Henrietta’s family did not learn of her “immortality” until more than twenty years after her death, when scientists investigating HeLa began using her husband and children in research without informed consent. And though the cells had launched a multimillion-dollar industry that sells human biological materials, her family never saw any of the profits. As Rebecca Skloot so brilliantly shows, the story of the Lacks family—past and present—is inextricably connected to the dark history of experimentation on African Americans, the birth of bioethics, and the legal battles over whether we control the stuff we are made of. Over the decade it took to uncover this story, Rebecca became enmeshed in the lives of the Lacks family—especially Henrietta’s daughter Deborah. Deborah was consumed with questions: Had scientists cloned her mother? Had they killed her to harvest her cells? And if her mother was so important to medicine, why couldn’t her children afford health insurance? Intimate in feeling, astonishing in scope, and impossible to put down, The Immortal Life of Henrietta Lacks captures the beauty and drama of scientific discovery, as well as its human consequences. |
| case western data science: Transforming Trajectories for Women of Color in Tech National Academies of Sciences, Engineering, and Medicine, National Academies Of Sciences Engineeri, Policy and Global Affairs, Board on Higher Education and Workforce, Committee on Addressing the Underrepresentation of Women of Color in Tech, 2022-09-09 Demand for tech professionals is expected to increase substantially over the next decade, and increasing the number of women of color in tech will be critical to building and maintaining a competitive workforce. Despite years of efforts to increase the diversity of the tech workforce, women of color have remained underrepresented, and the numbers of some groups of women of color have even declined. Even in cases where some groups of women of color may have higher levels of representation, data show that they still face significant systemic challenges in advancing to positions of leadership. Research evidence suggests that structural and social barriers in tech education, the tech workforce, and in venture capital investment disproportionately and negatively affect women of color. Transforming Trajectories for Women of Color in Tech uses current research as well as information obtained through four public information-gathering workshops to provide recommendations to a broad set of stakeholders within the tech ecosystem for increasing recruitment, retention, and advancement of women of color. This report identifies gaps in existing research that obscure the nature of challenges faced by women of color in tech, addresses systemic issues that negatively affect outcomes for women of color in tech, and provides guidance for transforming existing systems and implementing evidence-based policies and practices to increase the success of women of color in tech. |
| case western data science: Data Science Careers, Training, and Hiring Renata Rawlings-Goss, 2019-08-02 This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build. |
| case western data science: Data Science Pinle Qin, Hongzhi Wang, Guanglu Sun, Zeguang Lu, 2020-08-20 This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020. The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education. |
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