Products related to Data:
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Teachers Learning : Professional Development and Education
A collaborative series with the University of Cambridge Faculty of Education highlighting leading-edge research across Teacher Education, International Education Reform and Language Education. Teachers Learning focuses on continuing teacher's professional development through accounts of recent enquiries.From presenting evidence and arguments about the teacher's role and profession, knowledge and knowledge creation, other organisations in the educational landscape, issues of power, status and development, this book highlights the crucial importance and effectiveness of teachers' learning benefitting pupils and classroom learning.
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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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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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Inference and Learning from Data
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application.The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities.The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods.A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code.Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
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How does growth progress?
Growth progresses through a series of stages, starting from infancy and continuing through childhood, adolescence, and adulthood. During these stages, individuals experience physical, cognitive, emotional, and social development. Factors such as genetics, environment, nutrition, and experiences all play a role in shaping an individual's growth trajectory. As individuals grow, they acquire new skills, knowledge, and perspectives that contribute to their overall development and well-being.
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How does language development progress?
Language development progresses in stages, starting with cooing and babbling in infancy, then moving on to single words, two-word phrases, and eventually more complex sentences. Children learn language through exposure to spoken language, imitation, and reinforcement from caregivers. As they grow, they continue to expand their vocabulary, grammar, and communication skills through interactions with others and practice in various social contexts. Language development is a gradual process that is influenced by both biological factors and environmental experiences.
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How does human development progress?
Human development progresses through a series of stages that are influenced by both biological and environmental factors. These stages include physical, cognitive, emotional, and social development. As individuals grow and mature, they acquire new skills, knowledge, and experiences that shape their understanding of the world and their place in it. Development is a lifelong process that continues from infancy through adulthood and is influenced by a combination of genetics, environment, and personal experiences.
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How does sensory development progress?
Sensory development progresses through a series of stages, beginning with basic sensory experiences such as touch, taste, and smell in infancy. As children grow, they develop more refined sensory abilities, such as improved vision and hearing, and begin to integrate sensory information to make sense of their environment. This process continues into adolescence and adulthood, with individuals becoming more adept at interpreting and responding to sensory stimuli. Overall, sensory development progresses from basic sensory experiences to more complex and integrated sensory abilities as individuals mature.
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Learning Data Mining with Python
COM051360
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Training Data for Machine Learning
Your training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data.But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process.This hands-on guide explains how to work with and scale training data.Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts and other shareholders Successfully maintain, operate, and improve your system
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The Left Hand of Data : Designing Education Data for Justice
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Data + Journalism : A Story-Driven Approach to Learning Data Reporting
Taking a hands-on and holistic approach to data, Data + Journalism provides a complete guide to reporting data-driven stories.This book offers insights into data journalism from a global perspective, including datasets and interviews with data journalists from countries around the world.Emphasized by examples drawn from frequently updated sets of open data posted by authoritative sources like the FBI, Eurostat and the US Census Bureau, the authors take a deep dive into data journalism’s "heavy lifting" – searching for, scraping and cleaning data.Combined with exercises, video training supplements and lists of tools and resources at the end of each chapter, readers will learn not just how to crunch numbers but also how to put a human face to data, resulting in compelling, story-driven news stories based on solid analysis.Written by two experienced journalists and data journalism teachers, Data + Journalism is essential reading for students, instructors and early career professionals seeking a comprehensive introduction to data journalism skills.
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How do I secure data and game progress when changing phones?
To secure your data and game progress when changing phones, you can use cloud storage services like Google Drive or iCloud to backup your game data. Many games also offer account systems that allow you to save your progress online. Make sure to link your game account to an email or social media account to easily transfer your progress to your new phone. Additionally, you can use third-party apps specifically designed for transferring data between phones to ensure a smooth transition.
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Is the Apriori algorithm associated with machine learning or data mining?
The Apriori algorithm is associated with data mining. It is a classic algorithm used for association rule mining in large datasets. It is specifically used to identify frequent itemsets in transactional databases. While it is often used in the context of machine learning tasks, its primary application is in data mining for discovering patterns and relationships in data.
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Are data science and machine learning just trends from AI hype?
Data science and machine learning are not just trends from AI hype, but rather essential fields that have become increasingly important in various industries. Data science involves extracting insights and knowledge from data, while machine learning focuses on developing algorithms that can learn from and make predictions based on data. Both fields have proven to be valuable in solving complex problems and making data-driven decisions, making them more than just passing trends from AI hype.
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Does anyone have experience with further education in data analysis?
Yes, I have experience with further education in data analysis. I have completed a certification program in data analysis and have also taken advanced courses in statistics, programming, and data visualization. This education has provided me with the skills and knowledge necessary to work with large datasets, perform complex analysis, and communicate insights effectively. I have also gained practical experience through projects and internships, which have further solidified my understanding of data analysis concepts and techniques.
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