| data science domain emphasis: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field. |
| data science domain emphasis: Cybersecurity Data Science Scott Mongeau, Andrzej Hajdasinski, 2021-10-01 This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession. This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography. |
| data science domain emphasis: Ready With An Answer John Ankerberg, John Weldon, 2011-08-31 Know What You Believe Why You Believe It & How to Explain It. This powerful sourcebook answers the most important questions skeptics ask about God & Christianity. Along with the authors you'll examine a wide range of evidence for the truth of biblical Christianity & become equipped to evaluate the validity of: Jesus Christ—what sets Him entirely apart from founders of other religions; the resurrection—why lawyers & former skeptics believe it & why skeptics' theories fall short; the reliability of the Bible—how it is proven by the science of archaeology & our manuscript evidence; the miracle of origins—why both creation & evolution require a miracle & why evolution can't be true; reincarnation & Christianity—why they can't coexist; why biblical prophecy proves who the true God is & why the Bible is the only revelation from God; atheists & skeptics—why even they agree they have knowledge about God; & why the biblical evidence strongly argues for an inerrant Bible. Find answers to the toughest questions from creation to salvation & discover the uniqueness of Christianity & man's universal need for the one true God. |
| data science domain emphasis: Digital Humanities Looking at the World Sílvia Araújo, |
| data science domain emphasis: Digital Library Preservation Strategies Nicholas Lawson, 2018-01-29 Special libraries are facing increasing challenges today. The bigfgest challenge before them is how to demonstrate that they are the best source of specialized information despite reliance on the web for information. Special libraries therefore need to change in terms of their collections, roles, services and evolve strategies for managing the change. Preservation refers to the set of activities that aims to prolong the life of a record and relevant metadata, or enhance its value, or improve access to it through noninterventive means. This includes actions taken to influence records creators prior to selection and acquisition. The purpose of preservation is to ensure protection of information of enduring value for access by present and future generations. Libraries and archives have served as the central institutional focus for preservation, and both types of institutions include preservation as one of their core functions. In recent decades, many major libraries and archives have established formal preservation programs for traditional materials which include regular allocation of resources for preservation, preventive measures to arrest deterioration of materials, remedial measures to restore the usability of selected materials, and the incorporation of preservation needs and requirements into overall program planning. This book attempts to provide all basic aspects of digital library in an authentic but simple style. It describes the revolutionary changes brought out by digital libraries in the entire concept of library organisation, managements and operations. It also discusses the challenges emerging due to the adoption of newer technologies. |
| data science domain emphasis: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science domain emphasis: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data science domain emphasis: ICDSMLA 2020 Amit Kumar, Sabrina Senatore, Vinit Kumar Gunjan, 2021-11-08 This book gathers selected high-impact articles from the 2nd International Conference on Data Science, Machine Learning & Applications 2020. It highlights the latest developments in the areas of artificial intelligence, machine learning, soft computing, human–computer interaction and various data science and machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise. |
| data science domain emphasis: Targeted Learning Mark J. van der Laan, Sherri Rose, 2011-06-17 The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies. |
| data science domain emphasis: A Study of Expectations Held by Supervisors Relative to Teacher Aspirations Toward the Objectives in the Cognitive and Affective Domains in Selected Subject Areas Margaret Christine Woods, 1970 |
| data science domain emphasis: Energy Research Abstracts , 1993 |
| data science domain emphasis: Scientific Data Management Arie Shoshani, Doron Rotem, 2009-12-16 Dealing with the volume, complexity, and diversity of data currently being generated by scientific experiments and simulations often causes scientists to waste productive time. Scientific Data Management: Challenges, Technology, and Deployment describes cutting-edge technologies and solutions for managing and analyzing vast amounts of data, helping |
| data science domain emphasis: Introduction to Health Sciences Librarianship M. Sandra Wood, 2013-01-11 Get the foundational knowledge about health sciences librarianship. The general term “health sciences libraries” covers a wide range of areas beyond medical libraries, such as biomedical, nursing, allied health, pharmacy, and others. Introduction to Health Sciences Librarianship provides a sound foundation to all aspects of these types of libraries to students and librarians new to the field. This helpful guide provides a helpful overview of the health care environment, technical services, public services, management issues, academic health sciences, hospital libraries, health informatics, evidence-based practice, and more. This text provides crucial information every beginning and practicing health sciences librarian needs—all in one volume. Introduction to Health Sciences Librarianship presents some of the most respected librarians and educators in the field, each discussing important aspects of librarianship, including technical services, public services, administration, special services, and special collections. This comprehensive volume provides all types of librarians with helpful general, practical, and theoretical knowledge about this profession. The book’s unique A Day in the Life of . . . feature describes typical days of health sciences librarians working in special areas such as reference or consumer health, and offers anyone new to the field a revealing look at what a regular workday is like. The text is packed with useful figures, screen captures, tables, and references. Topics discussed in Introduction to Health Sciences Librarianship include: overview of health sciences libraries health environment collection development of journals, books, and electronic resources organization of health information access services information services and information retrieval information literacy health informatics management of academic health sciences libraries management and issues in hospital libraries library space planning specialized services Introduction to Health Sciences Librarianship provides essential information for health sciences librarians, medical librarians, beginning and intermediate level health sciences/medical librarians, and any health sciences librarian wishing to review the field. This crucial volume belongs in every academic health sciences library, hospital library, specialized health library, biomedical library, and academic library. |
| data science domain emphasis: The Routledge Companion to Research in the Arts Michael Biggs, Henrik Karlsson, 2010-10-04 The Routledge Companion to Research in the Arts is a major collection of new writings on research in the creative and performing arts by leading authorities from around the world. It provides theoretical and practical approaches to identifying, structuring and resolving some of the key issues in the debate about the nature of research in the arts which have surfaced during the establishment of this subject over the last decade. Contributions are located in the contemporary intellectual environment of research in the arts, and more widely in the universities, in the strategic and political environment of national research funding, and in the international environment of trans-national cooperation and communication. The book is divided into three principal sections – Foundations, Voices and Contexts – each with an introduction from the editors highlighting the main issues, agreements and debates in each section. The Routledge Companion to Research in the Arts addresses a wide variety of concepts and issues, including: the diversity of views on what constitutes arts-based research and scholarship, what it should be, and its potential contribution the trans-national communication difficulties arising from terminological and ontological differences in arts-based research traditional and non-traditional concepts of knowledge, their relationship to professional practice, and their outcomes and audiences a consideration of the role of written, spoken and artefact-based languages in the formation and communication of understandings. This comprehensive collection makes an original and significant contribution to the field of arts-based research by setting down a framework for addressing these, and other, topical issues. It will be essential reading for research managers and policy-makers in research councils and universities, as well as individual researchers, research supervisors and doctoral candidates. |
| data science domain emphasis: Data Science and Its Applications Aakanksha Sharaff, G R Sinha, 2021-08-18 The term data being mostly used, experimented, analyzed, and researched, Data Science and its Applications finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications. |
| data science domain emphasis: Driven by Data Paul Bambrick-Santoyo, 2010-04-12 Offers a practical guide for improving schools dramatically that will enable all students from all backgrounds to achieve at high levels. Includes assessment forms, an index, and a DVD. |
| data science domain emphasis: Data Science and Data Analytics Dinesh Kumar Arivalagan, 2024-07-31 Data Science and Data Analytics explores the foundational concepts, methodologies, and tools that drive data-driven decision-making in various industries. This book provides a comprehensive overview of data collection, processing, analysis, and visualization techniques, emphasizing practical applications and real-world case studies. Readers will gain insights into statistical methods, machine learning algorithms, and the importance of data ethics, equipping them with the knowledge to harness the power of data for informed decision-making and strategic planning in an increasingly data-centric world. |
| data science domain emphasis: GLOBE Program Teacher's Guide , 1996 |
| data science domain emphasis: Mapping Knowledge Domains Proceedings of the National Academy of Sciences, 2004 |
| data science domain emphasis: 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, 2020-10-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| data science domain emphasis: The GLOBE Program Teacher's Guide , 1996 |
| data science domain emphasis: Practical Data Science for Information Professionals David Stuart, 2020-07-24 Practical Data Science for Information Professionals provides an accessible introduction to a potentially complex field, providing readers with an overview of data science and a framework for its application. It provides detailed examples and analysis on real data sets to explore the basics of the subject in three principle areas: clustering and social network analysis; predictions and forecasts; and text analysis and mining. As well as highlighting a wealth of user-friendly data science tools, the book also includes some example code in two of the most popular programming languages (R and Python) to demonstrate the ease with which the information professional can move beyond the graphical user interface and achieve significant analysis with just a few lines of code. After reading, readers will understand: · the growing importance of data science · the role of the information professional in data science · some of the most important tools and methods that information professionals can use. Bringing together the growing importance of data science and the increasing role of information professionals in the management and use of data, Practical Data Science for Information Professionals will provide a practical introduction to the topic specifically designed for the information community. It will appeal to librarians and information professionals all around the world, from large academic libraries to small research libraries. By focusing on the application of open source software, it aims to reduce barriers for readers to use the lessons learned within. |
| data science domain emphasis: The Proceedings of the 2023 Conference on Systems Engineering Research Dinesh Verma, |
| data science domain emphasis: Data Science and Machine Learning Dirk P. Kroese, Zdravko Botev, Thomas Taimre, Radislav Vaisman, 2019-11-20 Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code |
| data science domain emphasis: How to Be a High School Superstar Cal Newport, 2010-07-27 Do Less, Live More, Get Accepted What if getting into your reach schools didn’t require four years of excessive A.P. classes, overwhelming activity schedules, and constant stress? In How to Be a High School Superstar, Cal Newport explores the world of relaxed superstars—students who scored spots at the nation’s top colleges by leading uncluttered, low stress, and authentic lives. Drawing from extensive interviews and cutting-edge science, Newport explains the surprising truths behind these superstars’ mixture of happiness and admissions success, including: · Why doing less is the foundation for becoming more impressive. · Why demonstrating passion is meaningless, but being interesting is crucial. · Why accomplishments that are hard to explain are better than accomplishments that are hard to do. These insights are accompanied by step-by-step instructions to help any student adopt the relaxed superstar lifestyle—proving that getting into college doesn’t have to be a chore to survive, but instead can be the reward for living a genuinely interesting life. |
| data science domain emphasis: Medical Data Sharing, Harmonization and Analytics Vasileios Pezoulas, Themis Exarchos, Dimitrios I Fotiadis, 2020-01-05 Medical Data Sharing, Harmonization and Analytics serves as the basis for understanding the rapidly evolving field of medical data harmonization combined with the latest cloud infrastructures for storing the harmonized (shared) data. Chapters cover the latest research and applications on data sharing and protection in the medical domain, cohort integration through the recent advancements in data harmonization, cloud computing for storing and securing the patient data, and data analytics for effectively processing the harmonized data. - Examines the unmet needs in chronic diseases as a part of medical data sharing - Discusses ethical, legal and privacy issues as part of data protection - Combines data harmonization and big data analytics strategies in shared medical data, along with relevant case studies in chronic diseases |
| data science domain emphasis: PISA Science 2006 Rodger W. Bybee, Barry McCrae, 2009 What must we teach students to enable them to fully participate in a world community where science and technology play an increasingly significant role? Comprehensive, thought-provoking, and indispensable, PISA Science 2006, provides educators with a top-down view of where we stand today in science education and what this means for students and educators. |
| data science domain emphasis: Data Analytics with Hadoop Benjamin Bengfort, Jenny Kim, 2016-06 Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib |
| data science domain emphasis: Comparing science content in the National Assessment of Educational Progress (NEAP) 2000 and Trends in International Mathematics and Science Study (TIMSS) 2003 assessments technical report. , 2006 |
| data science domain emphasis: Translating Behavioral Science Into Action United States. National Advisory Mental Health Council. Behavioral Science Workgroup, 2000 |
| data science domain emphasis: History and Precedent in Environmental Design Anatol Rapoport, 2013-06-29 This book is about a new and different way of approaching and studying the history of the built environment and the use of historical precedents in design. However, although what I am proposing is new for what is currently called architectural history, both my approach and even my conclusions are not that new in other fields, as I discovered when I attempted to find supporting evidence. * In fact, of all the disciplines dealing with various aspects of the study of the past, architectural history seems to have changed least in the ways I am advocating. There is currently a revival of interest in the history of architecture and urban form; a similar interest applies to theory, vernacular design, and culture-environment relations. After years of neglect, the study of history and the use of historical precedent are again becoming important. However, that interest has not led to new approaches to the subject, nor have its bases been examined. This I try to do. In so doing, I discuss a more rigorous and, I would argue, a more valid way of looking at historical data and hence of using such data in a theory of the built environment and as precedent in environmental design. Underlying this is my view of Environment-Behavior Studies CEBS) as an emerging theory rather than as data to help design based on current theory. Although this will be the subject of another book, a summary statement of this position may be useful. |
| data science domain emphasis: International Encyclopedia of Information and Library Science John Feather, Paul Sturges, 2003-09-02 This eagerly awaited new edition, has been fully revised and updated to take full account of the many and radical changes which have taken place since the Encyclopedia was originally conceived. |
| data science domain emphasis: Teaching Undergraduate Political Methodology Brown, Mitchell, Nordyke, Shane, Thies, Cameron G., 2022-08-12 Providing expert advice from established scholars in the field of political science, this engaging book imparts informative guidance on teaching research methods across the undergraduate curriculum. Written in a concise yet comprehensive style, it illustrates practical and conceptual advice, alongside more detailed chapters focussing on the different aspects of teaching political methodology. |
| data science domain emphasis: Information Systems with Emphasis on Business Rules for Demographics Sripriya Rajamani, 2005 |
| data science domain emphasis: Advances in Information Systems Development Gabor Magyar, Gabor Knapp, Gregory Wojtkowski, Jože Zupancic, 2007-08-28 This monograph details the proceedings of the 15th International Conference on Information Systems Development. ISD is progressing rapidly, continually creating new challenges for the professionals involved. New concepts, approaches and techniques of systems development emerge constantly in this field. Progress in ISD comes from research as well as from practice. The aim of the Conference was to provide an international forum for the exchange of ideas and experiences between academia and industry, and to stimulate the exploration of new solutions. |
| data science domain emphasis: Metadata and Semantic Research Emmanouel Garoufallou, Sirje Virkus, Rania Siatri, Damiana Koutsomiha, 2017-11-22 This book constitutes the thoroughly refereed proceedings of the 11th International Conference on Metadata and Semantic Research, MTSR 2017 2017, held in Tallinn, Estonia, November 28th to December 1st, 2017. The 18 full and 13 short papers presented were carefully reviewed and selected from 58 submissions. They focus on the Internet of Things (IoT) and the practical implementation of ontologies and linked data. Further topics are theoretical and foundational principles of metadata; ontologies and information organization; applications of linked data, open data, big data and user-generated metadata; digital interconnectedness; metadata standardization; authority control and interoperability in digital libraries and research data repositories; emerging issues in RDF, OWL, SKOS, schema.org, BIBFRAME, metadata and ontology design; linked data applications for e-books; digital publishing and Content Management Systems (CMSs); content discovery services, search, information retrieval and data visualization applications. |
| data science domain emphasis: The Elements of Statistical Learning Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2013-11-11 During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting. |
| data science domain emphasis: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science domain emphasis: Digital Technology Advancements in Knowledge Management Gyamfi, Albert, Williams, Idongesit, 2021-06-18 Knowledge management has always been about the process of creating, sharing, using, and applying knowledge within and between organizations. Before the advent of information systems, knowledge management processes were manual or offline. However, the emergence and eventual evolution of information systems created the possibility for the gradual but slow automation of knowledge management processes. These digital technologies enable data capture, data storage, data mining, data analytics, and data visualization. The value provided by such technologies is enhanced and distributed to organizations as well as customers using the digital technologies that enable interconnectivity. Today, the fine line between the technologies enabling the technology-driven external pressures and data-driven internal organizational pressures is blurred. Therefore, how technologies are combined to facilitate knowledge management processes is becoming less standardized. This results in the question of how the current advancement in digital technologies affects knowledge management processes both within and outside organizations. Digital Technology Advancements in Knowledge Management addresses how various new and emerging digital technologies can support knowledge management processes within organizations or outside organizations. Case studies and practical tips based on research on the emerging possibilities for knowledge management using these technologies is discussed within the chapters of this book. It both builds on the available literature in the field of knowledge management while providing for further research opportunities in this dynamic field. This book highlights topics such as human-robot interaction, big data analytics, software development, keyword extraction, and artificial intelligence and is ideal for technology developers, academics, researchers, managers, practitioners, stakeholders, and students who are interested in the adoption and implementation of new digital technologies for knowledge creation, sharing, aggregation, and storage. |
| data science domain emphasis: Informatics Education in Healthcare Eta S. Berner, 2013-09-02 This book reviews and defines the current state of the art for informatics education in medicine and health care. This field has undergone considerable change as the field of informatics itself has evolved. Twenty years ago almost the only individuals involved in health care who had even heard the term “informatics” were those who identified themselves as medical or nursing informaticians. Today, we have a variety of subfields of informatics including not just medical and nursing informatics, but informatics applied to specific health professions (such as dental or pharmacy informatics), as well as biomedical informatics, bioinformatics and public health informatics. The book addresses the broad range of informatics education programs available today. The Editor and experienced internationally recognized informatics educators who have contributed to this work have made the tacit knowledge explicit and shared some of the lessons they have learned. This book therefore represents the key reference for all involved in the informatics education whether they be trainers or trainees. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
