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Products related to Data:


  • Digital Teaching and Learning in Higher Education : Culture, Language, Social Issues
    Digital Teaching and Learning in Higher Education : Culture, Language, Social Issues

    Digital collaboration has been established in higher education for many years.But when the Covid-19 pandemic struck, digital learning and virtual mobility became of utmost importance for higher education. In the international project »Digital and International Virtual Academic Cooperation« (DIVA), scholars from Israel, Australia, and Germany focused on intercultural learning and online collaboration.Based on their findings, they show how digital arrangements can be used in higher education, how digital teaching can be theorized, and what potential can be gained for post-pandemic teaching.

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  • Data Culture : Develop An Effective Data-Driven Organization
    Data Culture : Develop An Effective Data-Driven Organization

    Organizations often start their data journey by either procuring the technology or hiring the people.However, without an effective data-driven culture in place, they can struggle to derive value from their investments. Data Culture explores how data leaders can develop and nurture a data-driven culture tailored to their organization's needs.It outlines the types of data leadership and teams needed and the key building blocks for success, such as team recruitment, building and training, leadership, process, behavioural change management, developing, sustaining and measuring a data culture, company values and everyday decision making.It also explores the nuances of how different types of data cultures work with different types of companies, what to avoid and the differences between building a data culture from scratch and changing an existing data culture from within. With this hands-on guide, senior data leader Shorful Islam takes readers through how to successfully establish or change a data culture, sharing his expertise in behavioural change psychology and two decades of experience in fostering data culture in organizations.Supported throughout by real-world examples and cases, this will be an essential read for all data leaders and anyone involved in developing a data-driven organizational culture.

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  • Learning without School : Home Education
    Learning without School : Home Education

    While some people look back on school as the 'best days of your life', for others the experience can be unpleasant and gruelling.Learning without School is a practical handbook for parents who want to educate their children at home but are unsure that they have the skills and know-how required to give their child the best education possible. This book explains what home education is; the advantages and disadvantages of choosing this route; how to begin home educating; what you need to do and how to help your child adjust; and how home education affects children's social skills and friendships.It also covers technical aspects, such as the curriculum, core subjects, exams and timetables.Ross Mountney also considers children with 'learning difficulties' or 'special needs' and how to approach home education differently for this group of children. Each chapter contains a summary of key points, useful websites, hints and tips and real-life case studies.This practical guide offers indispensible support for parents who are considering home education for their child, and includes a broad philosophy of education that will interest all parents and professionals involved in education and child welfare.

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  • Statistics : Learning from Data
    Statistics : Learning from Data

    Learn how to think like a statistician with Peck/Case's STATISTICS: LEARNING FROM DATA, 3rd Edition.This updated edition addresses common obstacles to learning based on the latest research for mastering statistics and probability.The authors use proven methods to carefully explain areas where you are most likely to struggle -- probability, hypothesis testing and selecting an appropriate method of analysis.You strengthen your conceptual understanding, procedural fluency and ability to put knowledge into practice with this edition's learning objectives, real-data examples, updated exercises and technology notes.WebAssign digital resources and Cengage's Statistical Analysis and Learning Tool (SALT) are also available to guide you in thinking statistically.SALT is an easy-to-use data analysis tool that allows you to manipulate data sets in order to visualize statistics and gain a deeper conceptual understanding about the meaning behind the data.

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  • Does Snapchat history display incorrect data?

    Snapchat history does not display incorrect data. The app is designed to accurately record and display the user's activity, such as sent and received snaps, chats, and stories. However, there may be instances where the user's perception of their own activity differs from the data displayed, which could be due to memory lapses or misunderstandings of the app's features. Overall, Snapchat history is intended to provide an accurate record of the user's interactions within the app.

  • Do mobile data save search history?

    Mobile data itself does not save search history. Search history is typically saved by the browser or search engine you are using on your mobile device. When you are connected to mobile data, your browsing history may be stored by the browser or search engine, but the mobile data service provider does not save this information. It is always a good idea to regularly clear your browsing history and cookies to maintain privacy and security.

  • How are data queried using database language?

    Data is queried using database language by writing specific commands known as queries. These queries are written in a structured query language (SQL) that allows users to retrieve, manipulate, and manage data stored in a database. By using SQL commands such as SELECT, INSERT, UPDATE, and DELETE, users can specify the criteria for retrieving data from the database tables. The queries are executed by the database management system, which processes the commands and returns the requested data based on the specified criteria.

  • What is the browsing history of mobile data?

    Browsing history of mobile data refers to the record of websites and online content that a user has accessed on their mobile device. This information includes the URLs of websites visited, the time and date of each visit, and sometimes even the duration of time spent on each site. Mobile data browsing history is typically stored locally on the device and can be accessed by the user or by certain applications for various purposes such as personalization of content or targeted advertising.

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  • Big Data in Education : The digital future of learning, policy and practice
    Big Data in Education : The digital future of learning, policy and practice

    Big data has the power to transform education and educational research.Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include:· The role of learning analytics and educational data science in schools· A critical appreciation of code, algorithms and infrastructures· The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments· Important digital research methods issues for researchers This is essential reading for anyone studying or working in today's education environment!

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  • Understanding Data, Culture and Society
    Understanding Data, Culture and Society

    - How is data shaping our identities?- What was the 'data revolution', and how did it happen?- How will AI change our societies?We live in the age of datafication: every aspect of our lives has been captured and transformed into data, from our sleeping patterns and step counts to our buying habits and political views. In this exciting new textbook, you will discover the intricate ways in which data and society are interwoven. Explaining key concepts such as 'big data' and putting theory into practice throughout, this book will make you a better expert in data and society, offering an interdisciplinary overview of a rapidly evolving field. This textbook tackles the implications of big data for democracy, identity and the global economy, showing how we cannot view our lives as separate from the technologies we have come to rely on. With learning objectives, case studies, further reading and extra resources provided in each chapter, this book is the ideal companion for students in the digital humanities and social sciences looking to deepen their understanding of data, culture and society.

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  • Pop Culture in Language Education : Theory, Research, Practice
    Pop Culture in Language Education : Theory, Research, Practice

    Pop Culture in Language Education provides comprehensive insight on how studies of pop culture can inform language teaching and learning.The volume offers a state-of-the-art overview of empirically informed, cutting-edge research that tackles both theoretical concerns and practical implications.The book focuses on how a diverse array of pop culture artifacts such as pop and rap music, movies and TV series, comics and cartoons, fan fiction, and video games can be exploited for the development of language skills.It establishes the study of pop culture and its language as a serious subfield within language education and applied linguistics and explores how studies of pop culture, its language, and its non-linguistic affordances can inform language education at various levels of proficiency and with various learner populations. Presenting a broad range of quantitative and qualitative research approaches including case studies on how pop culture has been used successfully in language education in and beyond the classroom, this book will be of great interest for academics, researchers, and students in the field of language education, applied linguistics, psycholinguistics, and sociolinguistics, as well as for language teachers and materials developers.

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  • Using Statistics in Small-Scale Language Education Research : Focus on Non-Parametric Data
    Using Statistics in Small-Scale Language Education Research : Focus on Non-Parametric Data

    Assuming no familiarity with statistical methods, this text for language education research methods and statistics courses provides detailed guidance and instruction on principles of designing, conducting, interpreting, reading, and evaluating statistical research done in classroom settings or with a small number of participants.While three different types of statistics are addressed (descriptive, parametric, non-parametric) the emphasis is on non-parametric statistics because they are appropriate when the number of participants is small and the conditions for use of parametric statistics are not satisfied.The emphasis on non-parametric statistics is unique and complements the growing interest among second and foreign language educators in doing statistical research in classrooms.Designed to help students and other language education researchers to identify and use analyses that are appropriate for their studies, taking into account the number of participants and the shape of the data distribution, the text includes sample studies to illustrate the important points in each chapter and exercises to promote understanding of the concepts and the development of practical research skills.Mathematical operations are explained in detail, and step-by-step illustrations in the use of R (a very powerful, online, freeware program) to perform all calculations are provided. A Companion Website extends and enhances the text with PowerPoint presentations illustrating how to carry out calculations and use R; practice exercises with answer keys; data sets in Excel MS-DOS format; and quiz, midterm, and final problems with answer keys.

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  • What is the search history of mobile data?

    The search history of mobile data refers to a record of all the searches and online activities conducted on a mobile device. This includes the websites visited, keywords searched, and any interactions on social media platforms or apps. Mobile data search history can provide valuable insights into a user's interests, preferences, and behavior, which can be used for targeted advertising, personalized recommendations, and improving user experience. It is important to protect the privacy and security of this data to prevent unauthorized access or misuse.

  • Why is history education important in school?

    History education is important in school because it provides students with a deeper understanding of the world around them. By learning about past events, students can gain insight into the causes and effects of historical events, as well as develop critical thinking and analytical skills. Additionally, studying history helps students to appreciate the diversity of human experiences and cultures, and to understand the complexities of the present by examining the past. Ultimately, history education helps students to become informed and engaged citizens who can contribute to a more just and equitable society.

  • What is data protection in school?

    Data protection in school refers to the measures and practices put in place to safeguard students' personal information and ensure it is not misused or accessed by unauthorized individuals. This includes securing data stored on school systems, ensuring only authorized personnel have access to student records, and complying with relevant data protection laws such as the Family Educational Rights and Privacy Act (FERPA) in the United States. Data protection in schools is crucial for maintaining student privacy, building trust with parents and students, and preventing potential data breaches or misuse.

  • Which programming language is well-suited for processing data?

    Python is well-suited for processing data due to its extensive libraries and tools for data analysis, manipulation, and visualization. Libraries such as Pandas, NumPy, and Matplotlib make it easy to work with large datasets and perform complex data operations. Additionally, Python's simplicity and readability make it a popular choice for data processing tasks, as it allows for quick and efficient development of data-related applications.

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