Products related to Data:
-
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
Price: 58.99 £ | Shipping*: 0.00 £ -
Data Analytics and Adaptive Learning : Research Perspectives
Data Analytics and Adaptive Learning offers new insights into the use of emerging data analysis and adaptive techniques in multiple learning settings.In recent years, both analytics and adaptive learning have helped educators become more responsive to learners in virtual, blended, and personalized environments.This set of rich, illuminating, international studies spans quantitative, qualitative, and mixed-methods research in higher education, K–12, and adult/continuing education contexts.By exploring the issues of definition and pedagogical practice that permeate teaching and learning and concluding with recommendations for the future research and practice necessary to support educators at all levels, this book will prepare researchers, developers, and graduate students of instructional technology to produce evidence for the benefits and challenges of data-driven learning.
Price: 145.00 £ | Shipping*: 0.00 £ -
Knowledge Guided Machine Learning : Accelerating Discovery using Scientific Knowledge and Data
Given their tremendous success in commercial applications, machine learning (ML) models are increasingly being considered as alternatives to science-based models in many disciplines.Yet, these "black-box" ML models have found limited success due to their inability to work well in the presence of limited training data and generalize to unseen scenarios.As a result, there is a growing interest in the scientific community on creating a new generation of methods that integrate scientific knowledge in ML frameworks.This emerging field, called scientific knowledge-guided ML (KGML), seeks a distinct departure from existing "data-only" or "scientific knowledge-only" methods to use knowledge and data at an equal footing.Indeed, KGML involves diverse scientific and ML communities, where researchers and practitioners from various backgrounds and application domains are continually adding richness to the problem formulations and research methods in this emerging field.Knowledge Guided Machine Learning: Accelerating Discovery using Scientific Knowledge and Data provides an introduction to this rapidly growing field by discussing some of the common themes of research in KGML using illustrative examples, case studies, and reviews from diverse application domains and research communities as book chapters by leading researchers.KEY FEATURES First-of-its-kind book in an emerging area of research that is gaining widespread attention in the scientific and data science fields Accessible to a broad audience in data science and scientific and engineering fields Provides a coherent organizational structure to the problem formulations and research methods in the emerging field of KGML using illustrative examples from diverse application domains Contains chapters by leading researchers, which illustrate the cutting-edge research trends, opportunities, and challenges in KGML research from multiple perspectives Enables cross-pollination of KGML problem formulations and research methods across disciplines Highlights critical gaps that require further investigation by the broader community of researchers and practitioners to realize the full potential of KGML
Price: 44.99 £ | Shipping*: 0.00 £ -
Machine Learning Pocket Reference : Working with Structured Data in Python
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover:Classification, using the Titanic datasetCleaning data and dealing with missing dataExploratory data analysisCommon preprocessing steps using sample dataSelecting features useful to the modelModel selectionMetrics and classification evaluationRegression examples using k-nearest neighbor, decision trees, boosting, and moreMetrics for regression evaluationClusteringDimensionality reductionScikit-learn pipelines
Price: 23.99 £ | Shipping*: 3.99 £
-
What are reference data types in Java?
Reference data types in Java are data types that store references to objects in memory rather than the actual data itself. These data types include classes, interfaces, arrays, and other reference types. When a reference data type is created, it only stores the memory address of the object it refers to, allowing multiple variables to refer to the same object. This is in contrast to primitive data types, which directly store the actual data values.
-
Does anyone do genealogy research? How do you obtain your information and data?
Yes, many people do genealogy research to trace their family history. There are various ways to obtain information and data for genealogy research, including using online databases, visiting local libraries and archives, interviewing family members, and utilizing DNA testing services. Online resources such as Ancestry.com, FamilySearch.org, and MyHeritage.com provide access to historical records, census data, and other genealogical information. Additionally, many people also use social media and networking platforms to connect with distant relatives and share family history information.
-
How can one falsify data information?
One can falsify data information by deliberately altering or fabricating data, manipulating the analysis or results, or selectively omitting certain data points. This can be done through various means such as tampering with measurements, changing values, or misrepresenting the source of the data. Falsifying data information is a serious ethical violation and can have severe consequences, including loss of credibility, legal repercussions, and damage to one's professional reputation. It is important to adhere to ethical standards and ensure the accuracy and integrity of data information.
-
Do the original data information get lost?
No, the original data information does not get lost when it is used to create a summary. The original data is still preserved and can be referred back to if needed. The summary is just a condensed version of the original data that highlights the key points and trends. It is important to keep the original data for reference and to ensure the accuracy of the summary.
Similar search terms for Data:
-
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.
Price: 60.95 £ | Shipping*: 0.00 £ -
Food Information, Communication and Education : Eating Knowledge
Food Information, Communication and Education analyses the role of different media in producing and transforming knowledge about food. ‘Eating knowledge’, or knowledge about food and food practice, is a central theme of cooking classes, the daily press, school textbooks, social media, popular magazines and other media.In addition, a wide variety of actors have taken on the responsibility of informing and educating the public about food, including food producers, advertising agencies, celebrity chefs, teachers, food bloggers and government institutions. Featuring a range of European case studies, this interdisciplinary collection advances our understanding of the processes of mediatization, circulation and reception of knowledge relating to food within specific social environments.Topics covered include: popularized knowledge about food carried over from past to present; the construction of trustworthy knowledge in today’s food risk society; critical assessment of nutrition education initiatives for children; and political and ideological implications of food information policy and practice.
Price: 90.00 £ | Shipping*: 0.00 £ -
Advancing Data Science Education in K-12 : Foundations, Research, and Innovations
Advancing Data Science Education in K-12 offers a highly accessible, research-based treatment of the foundations of data science education and its increasingly vital role in K-12 instructional content. As federal education initiatives and developers of technology-enriched curricula attempt to incorporate the study of data science—the generation, capture, and computational analysis of data at large scale—into schooling, a new slate of skills, literacies, and approaches is needed to ensure an informed, effective, and unproblematic deployment for young learners.Friendly to novices and experts alike, this book provides an authoritative synthesis of the most important research and theory behind data science education, its implementation into K-12 curricula, and clarity into the distinctions between data literacy and data science.Learning with and about data hold equal and interdependent importance across these chapters, conveying the variety of issues, situations, and decision-making integral to a well-rounded, critically minded perspective on data science education. Students and faculty in teaching, leadership, curriculum development, and educational technology programs will come away with essential insights into the breadth of our current and future engagements with data; the real-world opportunities and challenges data holds when taught in conjunction with other subject matter in formal schooling; and the nature of data as a human and societal construct that demands new competencies of today’s learners.
Price: 145.00 £ | Shipping*: 0.00 £ -
Data Pipelines Pocket Reference : Moving and Processing Data for Analytics
Data pipelines are the foundation for success in data analytics.Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it.This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy.This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn:What a data pipeline is and how it worksHow data is moved and processed on modern data infrastructure, including cloud platformsCommon tools and products used by data engineers to build pipelinesHow pipelines support analytics and reporting needsConsiderations for pipeline maintenance, testing, and alerting
Price: 23.99 £ | Shipping*: 3.99 £
-
What are information and data in computer science?
In computer science, information refers to processed data that has meaning and context. It is the result of organizing, manipulating, and presenting data in a way that is useful to users. Data, on the other hand, is raw facts and figures that are collected and stored in a computer system. It lacks context and meaning until it is processed into information. Both information and data are fundamental concepts in computer science and are essential for various computing tasks and applications.
-
What is the difference between data and information?
Data refers to raw facts and figures that have not been processed or analyzed. It is the unorganized, unprocessed facts that are collected. On the other hand, information is the processed and organized data that is meaningful and useful. It is the result of analyzing, interpreting, and organizing data to make it useful for decision-making and problem-solving. In summary, data is the raw material, while information is the processed and organized data that is useful for decision-making.
-
What is the homework for data and information?
The homework for data and information may involve tasks such as collecting and analyzing data, creating visual representations of data using tools like Excel or Tableau, conducting research on data management best practices, or writing a report on a specific data-related topic. It may also include practicing data entry and manipulation skills, learning about data security and privacy, or exploring different data storage and retrieval methods. Overall, the homework for data and information is designed to help students understand the importance of data in decision-making and develop practical skills for working with data in various contexts.
-
By which methods do opinion research institutes collect their data?
Opinion research institutes collect their data through various methods, including surveys, interviews, focus groups, and observational studies. Surveys are commonly conducted through phone, online, or in-person questionnaires to gather information from a large sample of individuals. Interviews involve one-on-one discussions with participants to delve deeper into their opinions and perspectives. Focus groups bring together a small group of individuals to discuss and provide feedback on specific topics. Observational studies involve researchers directly observing and recording behaviors or opinions in real-life settings.
* All prices are inclusive of VAT and, if applicable, plus shipping costs. The offer information is based on the details provided by the respective shop and is updated through automated processes. Real-time updates do not occur, so deviations can occur in individual cases.