Products related to Data:
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The Enterprise Data Catalog : Improve Data Discovery, Ensure Data Governance, and Enable Innovation
Combing the web is simple, but how do you search for data at work?It's difficult and time-consuming, and can sometimes seem impossible.This book introduces a practical solution: the data catalog.Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog.You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog.Written from a data management perspective and from a library and information science perspective, this book helps you:Learn what a data catalog is and how it can help your organizationOrganize data and its sources into domains and describe them with metadataSearch data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphsManage the data in your company via a data catalogImplement a data catalog in a way that exactly matches the strategic priorities of your organizationUnderstand what the future has in store for data catalogs
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Data Management in Large-Scale Education Research
Research data management is becoming more complicated.Researchers are collecting more data, using more complex technologies, all the while increasing the visibility of our work with the push for data sharing and open science practices.Ad hoc data management practices may have worked for us in the past, but now others need to understand our processes as well, requiring researchers to be more thoughtful in planning their data management routines. This book is for anyone involved in a research study involving original data collection.While the book focuses on quantitative data, typically collected from human participants, many of the practices covered can apply to other types of data as well.The book contains foundational context, instructions, and practical examples to help researchers in the field of education begin to understand how to create data management workflows for large-scale, typically federally funded, research studies.The book starts by describing the research life cycle and how data management fits within this larger picture.The remaining chapters are then organized by each phase of the life cycle, with examples of best practices provided for each phase.Finally, considerations on whether the reader should implement, and how to integrate those practices into a workflow, are discussed. Key Features:Provides a holistic approach to the research life cycle, showing how project management and data management processes work in parallel and collaborativelyCan be read in its entirety, or referenced as needed throughout the life cycleIncludes relatable examples specific to education researchIncludes a discussion on how to organize and document data in preparation for data sharing requirementsContains links to example documents as well as templates to help readers implement practices
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Research Data Management and Data Literacies
Research Data Management and Data Literacies help researchers familiarize themselves with RDM, and with the services increasingly offered by libraries.This new volume looks at data-intensive science, or ‘Science 2.0’ as it is sometimes termed in commentary, from a number of perspectives, including the tasks academic libraries need to fulfil, new services that will come online in the near future, data literacy and its relation to other literacies, research support and the need to connect researchers across the academy, and other key issues, such as ‘data deluge,’ the importance of citations, metadata and data repositories. This book presents a solid resource that contextualizes RDM, including good theory and practice for researchers and professionals who find themselves tasked with managing research data.
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Business Data Communications and Networking
Business Data Communications and Networking, 14th Edition presents a classroom-tested approach to the subject, combining foundational concepts, practical exercises, and real-world case studies.The text provides a balanced, well-rounded presentation of data communications while highlighting its importance to nearly every aspect of modern business.This fully-updated new edition helps students understand how networks work and what is required to build and manage scalable, mobile, and secure networks. Clear, student-friendly chapters introduce, explain, and summarize fundamental concepts and applications such as server architecture, network and transport layers, network design processes and tools, wired and wireless networking, and network security and management.An array of pedagogical features teaches students how to select the appropriate technologies necessary to build and manage networks that meet organizational needs, maximize competitive advantage, and protect networks and data from cybersecurity threats.Discussions of real-world management and technical issues, from improving device performance to assessing and controlling costs, provide students with insight into the daily networking operations of actual businesses.
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Do active mobile data consume data volume?
Yes, active mobile data does consume data volume. When your mobile data is turned on and you are using apps, browsing the internet, streaming videos, or downloading files, data is being consumed from your data plan. It is important to monitor your data usage to avoid exceeding your data limit and potentially incurring extra charges from your mobile service provider.
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Do activated mobile data consume data volume?
Yes, activated mobile data does consume data volume. When mobile data is turned on, it allows your device to connect to the internet using your cellular network, and any data used during this connection will be deducted from your data plan. Activities such as browsing the web, streaming videos, or using apps that require an internet connection will all consume data volume when mobile data is activated. It's important to monitor your data usage to avoid exceeding your plan's limits and incurring additional charges.
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What are master data and transactional data?
Master data refers to the core data entities of an organization, such as customer, product, employee, and supplier information. This data is typically static and does not change frequently. Master data is used as a reference point for transactional data. On the other hand, transactional data refers to the detailed records of day-to-day business activities, such as sales orders, purchase orders, invoices, and payments. This data is dynamic and changes frequently as business transactions occur. Transactional data is used to track and record the specific activities and events within an organization.
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Is data volume the same as mobile data?
No, data volume and mobile data are not the same. Data volume refers to the amount of data being used or transferred, which can include various types of data such as text, images, videos, etc. On the other hand, mobile data specifically refers to the internet data that is used on a mobile device, typically through a cellular network. Mobile data is a subset of data volume, as data volume can also include data used on other devices or networks.
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Big Data for the Public Good : Regulating Access to Public Sector Big Data for Research and Innovation
Can researchers and innovators use UK public sector data to produce knowledge that improves policy making, scrutinises government work and promotes the public interest?This book looks at interactions between UK public sector officials and researchers/innovators to shed light on barriers to data access and use.It asks: what are the frameworks that govern access to public sector big datasets for researchers and innovators?How are these frameworks applied in practice? What are the governance solutions for policy makers interested in harnessing the untapped potential of public sector big data to improve their policies and create public benefit?Public sector data is a valuable resource that can help researchers and innovators create knowledge and solutions that benefit society.As public bodies collect increasingly more data about us, UK policy makers try to maximise the use of public sector big data for the benefit of the public.But accessing this data is not easy. There are many legal, technical, and ethical barriers that prevent the use of public sector data for research and innovation.This book is for researchers and innovators who want to understand and overcome the barriers to accessing UK public sector data.It is also for policy makers who are interested in how public sector data can be used to improve decision-making, scrutinise government work, and promote the public interest.
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Communication Principles for Data Science
This book introduces the basic principles underlying the design and analysis of the digital communication systems that have heralded the information revolution.One major goal of the book is to demonstrate the role of the digital communication principles in a wide variety of data science applications, including community detection, computational biology, speech recognition and machine learning.One defining feature of this book is to make an explicit connection between the communication principles and data science problems, as well as to succinctly deliver the “story” of how the communication principles play a role for trending data science applications.All the key “plots” involved in the story are coherently developed with the help of tightly coupled exercise problem sets, and the associated fundamentals are explored mostly from first principles.Another key feature is that it includes programming implementation of a variety of algorithms inspired by fundamentals, together with a brief tutorial of the used programming tools.The implementation is based on Python and TensorFlow.This book does not follow a traditional book-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent storylines and themes.It serves as a textbook mainly for a junior- or senior-level undergraduate course, yet is also suitable for a first-year graduate course.Readers benefit from having a good background in probability and random processes, and basic familiarity with Python.But the background can be supplemented by almost self-contained materials, as well as by numerous exercise problems intended for elaborating on non-trivial concepts.In addition, Part III for data science applications should provide motivation and insights to students and even professional scientists who are interested in the field.
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The Left Hand of Data : Designing Education Data for Justice
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Big Data for Qualitative Research
Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges.The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments.Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis.It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers.This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research.Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher.
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What do Data Science and Data Engineering mean?
Data Science involves extracting insights and knowledge from data using various techniques such as statistics, machine learning, and data visualization. Data Scientists are responsible for analyzing and interpreting complex data sets to inform business decisions and solve problems. Data Engineering, on the other hand, focuses on the design and construction of systems for collecting, storing, and processing data. Data Engineers build and maintain the infrastructure that enables data to be accessed and utilized by Data Scientists and other stakeholders. They are also responsible for ensuring the reliability, scalability, and efficiency of data pipelines and storage systems.
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How do you compare experimental data with theoretical data?
Experimental data is collected through direct observation or measurement, while theoretical data is derived from mathematical models or theoretical predictions. To compare the two, one can analyze the agreement or disagreement between the experimental and theoretical results. This can involve statistical analysis, such as calculating the percent difference or correlation coefficient, as well as visual comparison through graphs or plots. Additionally, one can also consider the sources of error in the experimental data and assess the validity of the theoretical model in explaining the observed results.
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How can one study data analysis and data management?
One can study data analysis and data management through a variety of methods. This can include enrolling in formal education programs such as a degree in data science, statistics, or computer science. Additionally, there are many online courses and tutorials available that cover data analysis and data management. Practical experience can also be gained through internships or working on real-world projects. It's important to continuously practice and apply the concepts learned through these methods to gain a deeper understanding of data analysis and data management.
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'Fake Fundorado data?'
There is no concrete evidence to suggest that Fundorado data is fake. Fundorado is a German adult entertainment website that offers a variety of adult content to its subscribers. Like any online platform, there may be rumors or speculation about the authenticity of the data provided, but without verified sources or proof, it is difficult to determine if the Fundorado data is indeed fake. It is always important to approach such claims with caution and verify information from reliable sources.
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