Download or read book Veridical Data Science written by Bin Yu and published by MIT Press. This book was released on 2024-10-15 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners

Download or read book Leadership in Statistics and Data Science written by Amanda L. Golbeck and published by Springer Nature. This book was released on 2021-03-22 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited collection brings together voices of the strongest thought leaders on diversity, equity and inclusion in the field of statistics and data science, with the goal of encouraging and steering the profession into the regular practice of inclusive and humanistic leadership. It provides futuristic ideas for promoting opportunities for equitable leadership, as well as tested approaches that have already been found to make a difference. It speaks to the challenges and opportunities of leading successful research collaborations and making strong connections within research teams. Curated with a vision that leadership takes a myriad of forms, and that diversity has many dimensions, this volume examines the nuances of leadership within a workplace environment and promotes storytelling and other competencies as critical elements of effective leadership. It makes the case for inclusive and humanistic leadership in statistics and data science, where there often remains a dearth of women and members of certain racial communities among the employees. Titled and non-titled leaders will benefit from the planning, evaluation, and structural tools offered within to contribute inclusive excellence in workplace climate, environment, and culture.

Download or read book Guide to Intelligent Data Analysis written by Michael R. Berthold and published by Springer Science & Business Media. This book was released on 2010-06-23 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.

Download or read book Communicating with Data written by Deborah Nolan and published by Oxford University Press. This book was released on 2021-03-25 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS written by Giovanni C. Porzio and published by Firenze University Press. This book was released on with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research.

Download or read book Science Reason and Faith written by Fr. Robert Spitzer, SJ and published by Our Sunday Visitor. This book was released on 2023-09-18 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Built into our very nature is a desire to know about the world around us. The big questions of human existence are inescapable: Who am I? Why am I here, and where am I going? Why is there evil in the world? What is the meaning of life? This yearning for truth ultimately leads us to our Creator. God knows the longings of the human heart, and he reveals himself to us through creation, through Scripture, and ultimately through the Incarnation. Because God the Son became man, we have a person to look to in our pursuit of truth: Jesus Christ himself, who is Truth. Christ helps us see that truth is not just the object of science and reason, but what animates the mysterious and loving power of faith. In Science, Reason, and Faith, Fr. Robert Spitzer, SJ, explores in depth the Bible and the intersection of three realms that the secular world tells us are separate and incompatible. Fr. Spitzer draws the modern reader's attention to the many seeming conflicts between science, reason, and Catholic teaching. By tackling these difficult questions, he shows that it is precisely through the integration of science, reason, and faith that we can truly discover ourselves, our world, and our God.

Download or read book Veridical Data Science written by Bin Yu and published by MIT Press. This book was released on 2024-10-15 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners

Download or read book A Small Matter of Programming written by Bonnie A. Nardi and published by MIT Press. This book was released on 1993 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyzes cognitive, social and technical issues of end user programming. Drawing on empirical research on existing end user systems, this text examines the importance of task-specific programming languages, visual application frameworks and collaborative work practices for end user computing.

Download or read book ECKM 2023 24th European Conference on Knowledge Management Vol 2 written by Alvaro Rosa and published by Academic Conferences and publishing limited. This book was released on 2023-09-07 with total page 834 pages. Available in PDF, EPUB and Kindle. Book excerpt: These proceedings represent the work of contributors to the 24th European Conference on Knowledge Management (ECKM 2023), hosted by Iscte – Instituto Universitário de Lisboa, Portugal on 7-8 September 2023. The Conference Chair is Prof Florinda Matos, and the Programme Chair is Prof Álvaro Rosa, both from Iscte Business School, Iscte – Instituto Universitário de Lisboa, Portugal. ECKM is now a well-established event on the academic research calendar and now in its 24th year the key aim remains the opportunity for participants to share ideas and meet the people who hold them. The scope of papers will ensure an interesting two days. The subjects covered illustrate the wide range of topics that fall into this important and ever-growing area of research. The opening keynote presentation is given by Professor Leif Edvinsson, on the topic of Intellectual Capital as a Missed Value. The second day of the conference will open with an address by Professor Noboru Konno from Tama Graduate School and Keio University, Japan who will talk about Society 5.0, Knowledge and Conceptual Capability, and Professor Jay Liebowitz, who will talk about Digital Transformation for the University of the Future. With an initial submission of 350 abstracts, after the double blind, peer review process there are 184 Academic research papers, 11 PhD research papers, 1 Masters Research paper, 4 Non-Academic papers and 11 work-in-progress papers published in these Conference Proceedings. These papers represent research from Australia, Austria, Brazil, Bulgaria, Canada, Chile, China, Colombia, Cyprus, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, India, Iran, Iraq, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Kuwait, Latvia, Lithuania, Malaysia, México, Morocco, Netherlands, Norway, Palestine, Peru, Philippines, Poland, Portugal, Romania, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Tunisia, UK, United Arab Emirates and the USA.

Download or read book High Performance Big Data Computing written by Dhabaleswar K. Panda and published by MIT Press. This book was released on 2022-08-02 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.

Download or read book Knowledge Graphs written by Mayank Kejriwal and published by MIT Press. This book was released on 2021-03-30 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rigorous and comprehensive textbook covering the major approaches to knowledge graphs, an active and interdisciplinary area within artificial intelligence. The field of knowledge graphs, which allows us to model, process, and derive insights from complex real-world data, has emerged as an active and interdisciplinary area of artificial intelligence over the last decade, drawing on such fields as natural language processing, data mining, and the semantic web. Current projects involve predicting cyberattacks, recommending products, and even gleaning insights from thousands of papers on COVID-19. This textbook offers rigorous and comprehensive coverage of the field. It focuses systematically on the major approaches, both those that have stood the test of time and the latest deep learning methods.

Download or read book Urban Computing written by Yu Zheng and published by MIT Press. This book was released on 2019-02-05 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications. Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field.

Download or read book The Simple Genetic Algorithm written by Michael D. Vose and published by MIT Press. This book was released on 1999 with total page 650 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content Description #"A Bradford book."#Includes bibliographical references (p.) and index.

Download or read book Computability and Complexity written by Neil D. Jones and published by MIT Press. This book was released on 1997 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computability and complexity theory should be of central concern to practitioners as well as theorists. Unfortunately, however, the field is known for its impenetrability. Neil Jones's goal as an educator and author is to build a bridge between computability and complexity theory and other areas of computer science, especially programming. In a shift away from the Turing machine- and G�del number-oriented classical approaches, Jones uses concepts familiar from programming languages to make computability and complexity more accessible to computer scientists and more applicable to practical programming problems. According to Jones, the fields of computability and complexity theory, as well as programming languages and semantics, have a great deal to offer each other. Computability and complexity theory have a breadth, depth, and generality not often seen in programming languages. The programming language community, meanwhile, has a firm grasp of algorithm design, presentation, and implementation. In addition, programming languages sometimes provide computational models that are more realistic in certain crucial aspects than traditional models. New results in the book include a proof that constant time factors do matter for its programming-oriented model of computation. (In contrast, Turing machines have a counterintuitive "constant speedup" property: that almost any program can be made to run faster, by any amount. Its proof involves techniques irrelevant to practice.) Further results include simple characterizations in programming terms of the central complexity classes PTIME and LOGSPACE, and a new approach to complete problems for NLOGSPACE, PTIME, NPTIME, and PSPACE, uniformly based on Boolean programs. Foundations of Computing series

Download or read book Probabilistic Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2022-03-01 with total page 858 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.

Download or read book Crowdsourced Health written by Elad Yom-Tov and published by MIT Press. This book was released on 2016-03-18 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: "What if the [online] data generated by our searches could reveal information about health that would be difficult to gather in other ways? In this book, Elad Yom-Tov argues that Internet data could change the way medical research is done, supplementing traditional tools to provide insights not otherwise available. He describes how studies of Internet searches have, among other things, already helped researchers to track side effects of prescription driugs, to understand the information needs of cancer patients and their families, and to recognize some of the causes of anorexia. Yom-Tov shows that the information collected can benefit humanity without sacrificing individual privacy"--Jacket.

Download or read book Spotting and Discovering Terms Through Natural Language Processing written by Christian Jacquemin and published by MIT Press. This book was released on 2001 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: The acquired parsed terms can then be applied for precise retrieval and assembly of information."--BOOK JACKET.