Advertisement
| computational data science psu: Mathematical Omnibus D. B. Fuks, Serge Tabachnikov, 2007 The book consists of thirty lectures on diverse topics, covering much of the mathematical landscape rather than focusing on one area. The reader will learn numerous results that often belong to neither the standard undergraduate nor graduate curriculum and will discover connections between classical and contemporary ideas in algebra, combinatorics, geometry, and topology. The reader's effort will be rewarded in seeing the harmony of each subject. The common thread in the selected subjects is their illustration of the unity and beauty of mathematics. Most lectures contain exercises, and solutions or answers are given to selected exercises. A special feature of the book is an abundance of drawings (more than four hundred), artwork by an accomplished artist, and about a hundred portraits of mathematicians. Almost every lecture contains surprises for even the seasoned researcher. |
| computational data science psu: Aspiring Adults Adrift Richard Arum, Josipa Roksa, 2014-09-02 Few books have ever made their presence felt on college campuses—and newspaper opinion pages—as quickly and thoroughly as Richard Arum and Josipa Roksa’s 2011 landmark study of undergraduates’ learning, socialization, and study habits, Academically Adrift: Limited Learning on College Campuses. From the moment it was published, one thing was clear: no university could afford to ignore its well-documented and disturbing findings about the failings of undergraduate education. Now Arum and Roksa are back, and their new book follows the same cohort of undergraduates through the rest of their college careers and out into the working world. Built on interviews and detailed surveys of almost a thousand recent college graduates from a diverse range of colleges and universities, Aspiring Adults Adrift reveals a generation facing a difficult transition to adulthood. Recent graduates report trouble finding decent jobs and developing stable romantic relationships, as well as assuming civic and financial responsibility—yet at the same time, they remain surprisingly hopeful and upbeat about their prospects. Analyzing these findings in light of students’ performance on standardized tests of general collegiate skills, selectivity of institutions attended, and choice of major, Arum and Roksa not only map out the current state of a generation too often adrift, but enable us to examine the relationship between college experiences and tentative transitions to adulthood. Sure to be widely discussed, Aspiring Adults Adrift will compel us once again to re-examine the aims, approaches, and achievements of higher education. |
| computational data science psu: Applied Computational Technologies Brijesh Iyer, Tom Crick, Sheng-Lung Peng, 2022-05-14 This book is a collection of best selected research papers presented at 7th International Conference on Computing in Engineering and Technology (ICCET 2022), organized by Dr. Babasaheb Ambedkar Technological University, Lonere, India, during February 12 – 13, 2022. Focusing on frontier topics and next-generation technologies, it presents original and innovative research from academics, scientists, students, and engineers alike. The theme of the conference is Applied Information Processing System. |
| computational data science psu: Computer Science Handbook Allen B. Tucker, 2004-06-28 When you think about how far and fast computer science has progressed in recent years, it's not hard to conclude that a seven-year old handbook may fall a little short of the kind of reference today's computer scientists, software engineers, and IT professionals need. With a broadened scope, more emphasis on applied computing, and more than 70 chap |
| computational data science psu: 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. |
| computational data science psu: Improving Quality in American Higher Education Richard Arum, Josipa Roksa, Amanda Cook, 2016-05-31 An ambitious, comprehensive reimagining of 21st century higher education Improving Quality in American Higher Education outlines the fundamental concepts and competencies society demands from today's college graduates, and provides a vision of the future for students, faculty, and administrators. Based on a national, multidisciplinary effort to define and measure learning outcomes—the Measuring College Learning project—this book identifies 'essential concepts and competencies' for six disciplines. These essential concepts and competencies represent efforts towards articulating a consensus among faculty in biology, business, communication, economics, history, and sociology—disciplines that account for nearly 40 percent of undergraduate majors in the United States. Contributions from thought leaders in higher education, including Ira Katznelson, George Kuh, and Carol Geary Schneider, offer expert perspectives and persuasive arguments for the need for greater clarity, intentionality, and quality in U.S. higher education. College faculty are our best resource for improving the quality of undergraduate education. This book offers a path forward based on faculty perspectives nationwide: Clarify program structure and aims Articulate high-quality learning goals Rigorously measure student progress Prioritize higher order competencies and disciplinarily grounded conceptual understandings A culmination of over two years of efforts by faculty and association leaders from six disciplines, this book distills the national conversation into a delineated set of fundamental ideas and practices, and advocates for the development and use of rigorous assessment tools that are valued by faculty, students, and society. Improving Quality in American Higher Education brings faculty voices to the fore of the conversation and offers an insightful look at the state of higher education, and a realistic strategy for better serving our students. |
| computational data science psu: High Performance Computing for Computational Science - VECPAR 2002 José M.L.M. Palma, Jack Dongarra, Vicente Hernández, A. Augusto Sousa, Marina Waldén, 2003-08-03 The 5th edition of the VECPAR series of conferences marked a change of the conference title. The full conference title now reads VECPAR 2002 — 5th Int- national Conference on High Performance Computing for Computational S- ence. This re?ects more accurately what has been the main emphasis of the conference since its early days in 1993 – the use of computers for solving pr- lems in science and engineering. The present postconference book includes the best papers and invited talks presented during the three days of the conference, held at the Faculty of Engineering of the University of Porto (Portugal), June 26–28 2002. The book is organized into 8 chapters, which as a whole appeal to a wide research community, from those involved in the engineering applications to those interested in the actual details of the hardware or software implementation, in line with what, in these days, tends to be considered as Computational Science and Engineering (CSE). The book comprises a total of 49 papers, with a prominent position reserved for the four invited talks and the two ?rst prizes of the best student paper competition. |
| computational data science psu: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| computational data science psu: Data Science and Visual Computing Rae Earnshaw, John Dill, David Kasik, 2019-08-30 Data science addresses the need to extract knowledge and information from data volumes, often from real-time sources in a wide variety of disciplines such as astronomy, bioinformatics, engineering, science, medicine, social science, business, and the humanities. The range and volume of data sources has increased enormously over time, particularly those generating real-time data. This has posed additional challenges for data management and data analysis of the data and effective representation and display. A wide range of application areas are able to benefit from the latest visual tools and facilities. Rapid analysis is needed in areas where immediate decisions need to be made. Such areas include weather forecasting, the stock exchange, and security threats. In areas where the volume of data being produced far exceeds the current capacity to analyze all of it, attention is being focussed how best to address these challenges. Optimum ways of addressing large data sets across a variety of disciplines have led to the formation of national and institutional Data Science Institutes and Centers. Being driven by national priority, they are able to attract support for research and development within their organizations and institutions to bring together interdisciplinary expertise to address a wide variety of problems. Visual computing is a set of tools and methodologies that utilize 2D and 3D images to extract information from data. Such methods include data analysis, simulation, and interactive exploration. These are analyzed and discussed. |
| computational data science psu: Inferential Network Analysis Skyler J. Cranmer, Bruce A. Desmarais, Jason W. Morgan, 2020-11-19 Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis. |
| computational data science psu: The Computer Is Down Evangelina Vigil-PiÐÑn, 1987-01-01 The Computer is Down is at once a celebration of the crystalline and silvery image of the modern city, its advanced technology and economic power, as well as an iconoclastic questioning of the values attendant to this late twentieth century monument of civilization. The poetÍs eye guides the reader beyond the blinding glitter and the dizzying pace of the ñspace cityî to focus on street and neighborhood life, on the common man in his adaptation ? happy or uneasy ? to what seems to be an increasingly dehumanizing urban environment. In The Computer Is Down, our Virgil leads us down into the bowels of the city, where inhabit the human detritus: the downtrodden, the ignored, the forgotten. And above, at street level, the beauty of people maintaining their culture and traditions, unknowingly resisting dehumanization, resounds above the din of the traffic, the air drill and the wrecking ball. Like the black teens swaggering up the block to their ñghetto blasterî radios and the retired ñrich folksî maids steadily marching to an internal, more profound beat, the common folk shall endure ? longer than the towers of Ozymandias. |
| computational data science psu: Data Science and Big Data Analytics EMC Education Services, 2014-12-19 Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today! |
| computational data science psu: Advances in System Dynamics and Control Azar, Ahmad Taher, Vaidyanathan, Sundarapandian, 2018-02-09 Complex systems are pervasive in many areas of science. With the increasing requirement for high levels of system performance, complex systems has become an important area of research due to its role in many industries. Advances in System Dynamics and Control provides emerging research on the applications in the field of control and analysis for complex systems, with a special emphasis on how to solve various control design and observer design problems, nonlinear systems, interconnected systems, and singular systems. Featuring coverage on a broad range of topics, such as adaptive control, artificial neural network, and synchronization, this book is an important resource for engineers, professionals, and researchers interested in applying new computational and mathematical tools for solving the complicated problems of mathematical modeling, simulation, and control. |
| computational data science psu: Nanoantennas and Plasmonics Douglas H. Werner, Sawyer D. Campbell, Lei Kang, 2020-09-17 This book presents cutting-edge research advances in the rapidly growing areas of nanoantennas and plasmonics as well as their related enabling technologies and applications. It provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments, to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book also covers highly-directive nanoantennas, all-dielectric and tuneable/reconfigurable devices, metasurface optical components, and other related topics. |
| computational data science psu: Research Methods in Building Science and Technology Rahman Azari, Hazem Rashed-Ali, 2021-09-09 This book covers the range of methodological approaches, methods and tools currently used in various areas of building science and technology research and addresses the current lack of research-method literature in this field. The book covers the use of measurement-based methods in which data is collected by measuring the properties and their variations in ‘actual’ physical systems, simulation-based methods which work with ‘models’ of systems or processes to describe, examine and analyze their behaviors, performances and operations, and data-driven methodologies in which data is collected via measurement or simulation to identify and examine the associations and patterns and predict the future in a targeted system. The book presents a survey of key methodologies in various specialized areas of building science and technology research including window systems, building enclosure, energy performance, lighting and daylighting, computational fluid dynamics, indoor and outdoor thermal comfort, and life cycle environmental impacts. Provides advanced insight into the research methods and presents the key methodologies within the field of building science and technology. Reviews simulation-based and experimentation/field-based methods of data collection and analysis in diverse areas of building science and technology, such as energy performance, window and enclosure studies, environmental LCA, daylighting, CFD, and thermal comfort. Provides a range of perspectives from building science faculty and researcher contributors with diverse research interests. Appropriate for use in university courses. |
| computational data science psu: Beyond Objectivism and Relativism Richard J. Bernstein, 2011-09-16 Drawing freely and expertly from Continental and analytic traditions, Richard Bernstein examines a number of debates and controversies exemplified in the works of Gadamer, Habermas, Rorty, and Arendt. He argues that a new conversation is emerging about human rationality—a new understanding that emphasizes its practical character and has important ramifications both for thought and action. |
| computational data science psu: Three Assessments of Science, 1969-1977 National Assessment of Educational Progress (Project), 1979 |
| computational data science psu: Optimization Methods in Metabolic Networks Costas D. Maranas, Ali R. Zomorrodi, 2016-02-23 Provides a tutorial on the computational tools that use mathematical optimization concepts and representations for the curation, analysis and redesign of metabolic networks Organizes, for the first time, the fundamentals of mathematical optimization in the context of metabolic network analysis Reviews the fundamentals of different classes of optimization problems including LP, MILP, MLP and MINLP Explains the most efficient ways of formulating a biological problem using mathematical optimization Reviews a variety of relevant problems in metabolic network curation, analysis and redesign with an emphasis on details of optimization formulations Provides a detailed treatment of bilevel optimization techniques for computational strain design and other relevant problems |
| computational data science psu: Handbook of Big Data Analytics Wolfgang Karl Härdle, Henry Horng-Shing Lu, Xiaotong Shen, 2018-07-20 Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science. |
| computational data science psu: R and Data Mining Yanchang Zhao, 2012-12-31 R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work |
| computational data science psu: Engineering Problems William Macgregor Wallace, 1914 |
| computational data science psu: Judging School Discipline Richard. ARUM, Richard Arum, 2009-06-30 Reprimand a class comic, restrain a bully, dismiss a student for brazen attire--and you may be facing a lawsuit, costly regardless of the result. This reality for today's teachers and administrators has made the issue of school discipline more difficult than ever before--and public education thus more precarious. This is the troubling message delivered in Judging School Discipline, a powerfully reasoned account of how decades of mostly well-intended litigation have eroded the moral authority of teachers and principals and degraded the quality of American education. Judging School Discipline casts a backward glance at the roots of this dilemma to show how a laudable concern for civil liberties forty years ago has resulted in oppressive abnegation of adult responsibility now. In a rigorous analysis enriched by vivid descriptions of individual cases, the book explores 1,200 cases in which a school's right to control students was contested. Richard Arum and his colleagues also examine several decades of data on schools to show striking and widespread relationships among court leanings, disciplinary practices, and student outcomes; they argue that the threat of lawsuits restrains teachers and administrators from taking control of disorderly and even dangerous situations in ways the public would support. Table of Contents: Preface 1. Questioning School Authority 2. Student Rights versus School Rules With Irenee R. Beattie 3. How Judges Rule With Irenee R. Beattie 4. From the Bench to the Paddle With Richard Pitt and Jennifer Thompson 5. School Discipline and Youth Socialization With Sandra Way 6. Restoring Moral Authority in American Schools Appendix: Tables Notes Index Reviews of this book: This interesting study casts a critical eye on the American legal system, which [Arum] sees as having undermined the ability of teachers and administrators to socialize teenagers...Arum, it must be pointed out, is adamantly opposed to such measures as zero tolerance, which, he insists, often results in unfair and excessive punishment. What he wisely calls for is not authoritarianism, but for school folks to regain a sense of moral authority so that they can act decisively in matters of school discipline without having to look over their shoulders. --David Ruenzel, Teacher Magazine Reviews of this book: Arum's book should be compulsory reading for the legal profession; they need to recognise the long-term effects of their judgments on the climate of schools and the way in which judgments in favour of individual rights can reduce the moral authority of schools in disciplining errant students. But the author is no copybook conservative, and he is as critical of the Right's get-tough, zero-tolerance authoritarianism as he is of what he eloquently describes as the 'marshmallow effect' of liberal reformers, pushing the rules to their limits and tolerating increased misconduct. --John Dunford, Times Educational Supplement [UK] Reviews of this book: [Arum] argues that discipline is often ineffective because schools' legitimacy and moral authority have been eroded. He holds the courts responsible, because they have challenged schools' legal and moral authority, supporting this claim by examining over 6,200 state and federal appellate court decisions from 1960 to 1992. In describing the structure of these decisions, Arum provides interesting insights into school disciplinary practices and the law. --P. M. Socoski, Choice Reviews of this book: Arum's careful analysis of school discipline becomes so focused and revealing that the ideological boundaries of the debate seem almost to have been suspended. The result is a rich and original book, bold, important, useful, and--as this combination of attributes might suggest--surprising...Many years in the making, Judging School Discipline weds historical, theoretical, and statistical research within the problem-solving stance of a teacher working to piece together solutions in the interest of his students. The result is a book that promises to shape research as well as practice through its demonstration that students are liberated, as well as oppressed, by school discipline. --Steven L. VanderStaay, Urban Education Reviews of this book: [Arum's] break with education-school dogma on student rights is powerful and goes far toward explaining why so many teachers dread their students--when they are not actually fighting them off. --Heather MacDonald, Wall Street Journal |
| computational data science psu: Enhanced Telemedicine and e-Health Gonçalo Marques, Akash Kumar Bhoi, Isabel de la Torre Díez, Begonya Garcia-Zapirain, 2021-05-09 In recent years, new applications on computer-aided technologies for telemedicine have emerged. Therefore, it is essential to capture this growing research area concerning the requirements of telemedicine. This book presents the latest findings on soft computing, artificial intelligence, Internet of Things and related computer-aided technologies for enhanced telemedicine and e-health. Furthermore, this volume includes comprehensive reviews describing procedures and techniques, which are crucial to support researchers in the field who want to replicate these methodologies in solving their related research problems. On the other hand, the included case studies present novel approaches using computer-aided methods for enhanced telemedicine and e-health. This volume aims to support future research activities in this domain. Consequently, the content has been selected to support not only academics or engineers but also to be used by healthcare professionals. |
| computational data science psu: Emerging Non-Volatile Memories Seungbum Hong, Orlando Auciello, Dirk Wouters, 2014-11-18 This book is an introduction to the fundamentals of emerging non-volatile memories and provides an overview of future trends in the field. Readers will find coverage of seven important memory technologies, including Ferroelectric Random Access Memory (FeRAM), Ferromagnetic RAM (FMRAM), Multiferroic RAM (MFRAM), Phase-Change Memories (PCM), Oxide-based Resistive RAM (RRAM), Probe Storage, and Polymer Memories. Chapters are structured to reflect diffusions and clashes between different topics. Emerging Non-Volatile Memories is an ideal book for graduate students, faculty, and professionals working in the area of non-volatile memory. This book also: Covers key memory technologies, including Ferroelectric Random Access Memory (FeRAM), Ferromagnetic RAM (FMRAM), and Multiferroic RAM (MFRAM), among others. Provides an overview of non-volatile memory fundamentals. Broadens readers’ understanding of future trends in non-volatile memories. |
| computational data science psu: Computational Science and Its Applications - ICCSA 2003 Vipin Kumar, 2003-05-08 The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003. The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science. |
| computational data science psu: Data Science in R Deborah Nolan, Duncan Temple Lang, 2015-04-21 Effectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts |
| computational data science psu: Computational Science – ICCS 2008 , 2008 |
| computational data science psu: Advances in Data Science and Information Engineering Robert Stahlbock, Gary M. Weiss, Mahmoud Abou-Nasr, Cheng-Ying Yang, Hamid R. Arabnia, Leonidas Deligiannidis, 2021-10-29 The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students. |
| computational data science psu: Black Software Charlton D. McIlwain, 2020 Black Software, for the first time, chronicles the long relationship between African Americans, computing technology, and the Internet. Through new archival sources and the voices of many of those who lived and made this history, the book centralizes African Americans' role in the Internet's creation and evolution, illuminating both the limits and possibilities for using digital technology to push for racial justice in the United States and across the globe. |
| computational data science psu: Privacy-Preserving Data Publishing Bee-Chung Chen, Daniel Kifer, Ashwin Machanavajjhala, Kristen LeFevre, 2009-10-14 This book is dedicated to those who have something to hide. It is a book about privacy preserving data publishing -- the art of publishing sensitive personal data, collected from a group of individuals, in a form that does not violate their privacy. This problem has numerous and diverse areas of application, including releasing Census data, search logs, medical records, and interactions on a social network. The purpose of this book is to provide a detailed overview of the current state of the art as well as open challenges, focusing particular attention on four key themes: RIGOROUS PRIVACY POLICIES Repeated and highly-publicized attacks on published data have demonstrated that simplistic approaches to data publishing do not work. Significant recent advances have exposed the shortcomings of naive (and not-so-naive) techniques. They have also led to the development of mathematically rigorous definitions of privacy that publishing techniques must satisfy; METRICS FOR DATA UTILITY While it is necessary to enforce stringent privacy policies, it is equally important to ensure that the published version of the data is useful for its intended purpose. The authors provide an overview of diverse approaches to measuring data utility; ENFORCEMENT MECHANISMS This book describes in detail various key data publishing mechanisms that guarantee privacy and utility; EMERGING APPLICATIONS The problem of privacy-preserving data publishing arises in diverse application domains with unique privacy and utility requirements. The authors elaborate on the merits and limitations of existing solutions, based on which we expect to see many advances in years to come. |
| computational data science psu: The Data Science Handbook Field Cady, 2017-02-28 A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon. |
| computational data science psu: Next-Gen Technologies in Computational Intelligence R. Anandan, M. Senthil Kumar, Biji C. L., Vicente García Díaz, Souvik Pal, 2024-06-07 The Proceeding includes the research contribution from the International Conference on Next-Gen Technologies in Computational Intelligence (NGTCA 2023) held on March 24th 2023 at Vels Institute of Science, Technology and Advanced Studies. NGCTA 2023 is the flagship conference of the Computer Society of India (Region 7). Computer Society of India (CSI) is the largest association of IT professionals in India. CSI is a non-profit organization established in 1965 and its members are committed to the advancement of theory and practice of Computer Engineering and Technology Systems. The Mission of CSI is to facilitate research, knowledge sharing, learning, and career enhancement for all categories of IT professionals, while simultaneously inspiring and nurturing new entrants into the industry and helping them to integrate into the IT community. At present, CSI has 76chapters across India, over 550 student branches with 1,00,000 plus members. It serves its members through technical events, seminars, workshops, conferences, publications & journals, research projects, competitions, special interest groups, awards & recognitions, etc. Various CSI chapters conduct Research Convention every year. |
| computational data science psu: Advances in Computational Intelligence Systems Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity, 2018-08-10 This book presents the latest trends in and approaches to computational intelligence research and its application to intelligent systems. It covers a long list of interconnected research areas, such as fuzzy systems, neural networks, evolutionary computation, clustering and classification, machine learning, data mining, cognition and robotics, and deep learning. The individual chapters are based on peer-reviewed contributions presented at the 18th Annual UK Workshop on Computational Intelligence (UKCI-2018), held in Nottingham, UK on September 5-7, 2018. The book puts a special emphasis on novel methods and reports on their use in a wide range of applications areas, thus providing both academics and professionals with a comprehensive and timely overview of new trends in computational intelligence. |
| computational data science psu: Data Analytics and Management Ashish Khanna, Deepak Gupta, Zdzisław Pólkowski, Siddhartha Bhattacharyya, Oscar Castillo, 2021-01-04 This book includes original unpublished contributions presented at the International Conference on Data Analytics and Management (ICDAM 2020), held at Jan Wyzykowski University, Poland, during June 2020. The book covers the topics in data analytics, data management, big data, computational intelligence, and communication networks. The book presents innovative work by leading academics, researchers, and experts from industry which is useful for young researchers and students. |
| computational data science psu: Big Copyright Versus the People Martin Skladany, 2018-06-07 Extreme copyright produces extreme consumption: ten hours a day, lost to screens. This book takes back our culture and creativity. |
| computational data science psu: The Selected Works of George E. Andrews George E. Andrews, 2013 This volume provides George Andrews' background commentary and comprehensive assessment of years of research and developments within the field of integer partitions. |
| computational data science psu: Computational Science – ICCS 2019 João M. F. Rodrigues, Pedro J. S. Cardoso, Jânio Monteiro, Roberto Lam, Valeria V. Krzhizhanovskaya, Michael H. Lees, Jack J. Dongarra, Peter M.A. Sloot, 2019-06-07 The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Matrix Methods in Artificial Intelligence and Machine Learning; Track of Architecture, Languages, Compilation and Hardware Support for Emerging and Heterogeneous Systems Part III: Track of Biomedical and Bioinformatics Challenges for Computer Science; Track of Classifier Learning from Difficult Data; Track of Computational Finance and Business Intelligence; Track of Computational Optimization, Modelling and Simulation; Track of Computational Science in IoT and Smart Systems Part IV: Track of Data-Driven Computational Sciences; Track of Machine Learning and Data Assimilation for Dynamical Systems; Track of Marine Computing in the Interconnected World for the Benefit of the Society; Track of Multiscale Modelling and Simulation; Track of Simulations of Flow and Transport: Modeling, Algorithms and Computation Part V: Track of Smart Systems: Computer Vision, Sensor Networks and Machine Learning; Track of Solving Problems with Uncertainties; Track of Teaching Computational Science; Poster Track ICCS 2019 Chapter “Comparing Domain-decomposition Methods for the Parallelization of Distributed Land Surface Models” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. |
| computational data science psu: New Statistical Developments in Data Science Alessandra Petrucci, Filomena Racioppi, Rosanna Verde, 2019-08-20 This volume collects the extended versions of papers presented at the SIS Conference “Statistics and Data Science: new challenges, new generations”, held in Florence, Italy on June 28-30, 2017. Highlighting the central role of statistics and data analysis methods in the era of Data Science, the contributions offer an essential overview of the latest developments in various areas of statistics research. The 35 contributions have been divided into six parts, each of which focuses on a core area contributing to “Data Science”. The book covers topics including strong statistical methodologies, Bayesian approaches, applications in population and social studies, studies in economics and finance, techniques of sample design and mathematical statistics. Though the book is mainly intended for researchers interested in the latest frontiers of Statistics and Data Analysis, it also offers valuable supplementary material for students of the disciplines dealt with here. Lastly, it will help Statisticians and Data Scientists recognize their counterparts’ fundamental role. |
| computational data science psu: Improving Learning Environments Richard Arum, Melissa Velez, 2012-06-13 Improving Learning Environments provides the first systematic comparative cross-national study of school disciplinary climates. In this volume, leading international social science researchers explore nine national case studies to identify the institutional determinants of variation in school discipline, the possible links between school environments and student achievement, as well as the implications of these findings for understanding social inequality. As the book demonstrates, a better understanding of school discipline is essential to the formation of effective educational policies. Ultimately, to improve a school's ability to contribute to youth socialization and student internalization of positive social norms and values, any changes in school discipline must not only be responsive to behavior problems but should also work to enhance the legitimacy and moral authority of school actors. |
| computational data science psu: Handbook of Convex Optimization Methods in Imaging Science Vishal Monga, 2017-10-27 This book covers recent advances in image processing and imaging sciences from an optimization viewpoint, especially convex optimization with the goal of designing tractable algorithms. Throughout the handbook, the authors introduce topics on the most key aspects of image acquisition and processing that are based on the formulation and solution of novel optimization problems. The first part includes a review of the mathematical methods and foundations required, and covers topics in image quality optimization and assessment. The second part of the book discusses concepts in image formation and capture from color imaging to radar and multispectral imaging. The third part focuses on sparsity constrained optimization in image processing and vision and includes inverse problems such as image restoration and de-noising, image classification and recognition and learning-based problems pertinent to image understanding. Throughout, convex optimization techniques are shown to be a critically important mathematical tool for imaging science problems and applied extensively. Convex Optimization Methods in Imaging Science is the first book of its kind and will appeal to undergraduate and graduate students, industrial researchers and engineers and those generally interested in computational aspects of modern, real-world imaging and image processing problems. |
DATA SCIENCES UNDERGRADUATE HANDBOOK
Computational Data Sciences, offered only through the Department of Computer Science and Engineering, focuses on the computational foundations of data science, including the design, …
(Computational Option) - engr.psu.edu
The program provides students with the technical fundamentals of data sciences and helps them develop the knowledge and skills needed to manage and analyze large-scale, unstructured data.
EXPLORE Data Sciences - Pennsylvania State University
Data scientists employ innovation and creative thinking to design and build software that analyzes large amounts of data. They find new applications for using data and develop new ways in …
DATA SCIENCES, B.S. (SCIENCE) - bulletins.psu.edu
Data Sciences integrate aspects of Computer Science, Informatics, and Statistics to yield powerful data science methods, systems, tools, and best practices that find applications across a broad …
DATA SCIENCES UNDERGRADUATE HANDBOOK
Computational Data Sciences, offered only through the Department of Computer Science and Engineering, focuses on the computational foundations of data science, including the design, …
(DataScienceOption–Corecoursesonly)
Learning in Data Science CMPSC463(F) Design and Analysis of Algorithms CMPSC132 Programming & Computation II CMPSC330(F/S) Adv. Prog. in C++ CMPSC441(F) Artificial …
COMPUTATIONAL DATA SCIENCE - Pennsylvania State …
Apr 14, 2022 · bulletins.psu.edu CMPSC 442* Or DS 442* [3 Credits] CMPSC 455~* [3 Credits] OPTION A (See Below) DEPARTMENT LIST ELECTIVE [3 Credits] Author: Matthew Stumpf …
DATA SCIENCES UNDERGRADUATE HANDBOOK
Computational Data Sciences, offered only through the Department of Computer Science and Engineering, focuses on the computational foundations of data science, including the design, …
Computational Sciences, Minor - Pennsylvania State University
The Computational Sciences minor provides the necessary skills to use computers to study and solve scientific, engineering and data-centric problems across a wide range of disciplines.
DATA SCIENCES, B.S. (ABINGTON) - bulletins.psu.edu
Data Sciences integrate aspects of Computer Science, Informatics, and Statistics to yield powerful data science methods, systems, tools, and best practices that find applications across a broad …
DATA SCIENCES UNDERGRADUATE HANDBOOK - eecs.psu.edu
Computational Data Sciences, offered only through the Department of Computer Science and Engineering, focuses on the computational foundations of data science, including the design, …
Data Sciences, B.S. (Science) - undergraduate.bulletins.psu.edu
list of computational data sciences courses (https:// www.eecs.psu.edu/students/undergraduate/Data-Sciences.aspx) Statistical Modeling Data …
Data Sciences, B.S. (Engineering) - Pennsylvania State University
Data Sciences is a field of study concerned with developing, applying, and validating methods, processes, systems, and tools for drawing useful knowledge, justifiable conclusions, and …
Data Science - Pennsylvania State University
Computational Data Sciences, offered only through the Department of Computer Science and Engineering, focuses on the computational foundations of data science, including the design, …
Data Sciences, B.S. (Information Sciences and Technology)
Integrate statistical concepts/methods and computational/ machine learning methods to discover the structure of data and build predictive models. Apply the principles of data management to …
Data Sciences, B.S. (Information Sciences and Technology)
Data Sciences integrate aspects of Computer Science, Informatics, and Statistics to yield powerful data science methods, systems, tools, and best practices that find applications across a …
Data Sciences, B.S. (Engineering) - Pennsylvania State University
To access previous years' suggested academic plans, please visit the archive (https:// bulletins.psu.edu/undergraduate/archive/) to view the appropriate Undergraduate Bulletin …
Data Sciences, B.S. (Engineering) - Pennsylvania State University
Integrate statistical concepts/methods and computational/ machine learning methods to discover the structure of data and build predictive models. Apply the principles of data management to …
Data Sciences, B.S. (Science) - undergraduate.bulletins.psu.edu
Data Sciences integrate aspects of Computer Science, Informatics, and Statistics to yield powerful data science methods, systems, tools, and best practices that find applications across a broad …
2018-19 Annual Report - Pennsylvania State Universi…
seeing new growth emerging in computational and data sciences. The computational capacity of the ICS-ACI …
The Cyber Science of Interdependence in Govern…
Project 2: Computational Challenges I Methodology involves computationally evaluating the detection of network …
ICDS-ACI Policy Series - icds.psu.edu
Computational and Data Sciences ICDS-ACI-P020 Data Protection and Retention 4 1.0 Overview The intent of this …
Wednesday, April 6, 2016 11:10 a.m. 202 IST Buildin…
biology from Virginia Tech. Arjun’s research interests lie in developing computational genomic approaches …
YesiWell: Human Behavior Modeling in Health Social N…
data and social activities of human subjects. Experimental results conducted on a real health social …
