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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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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.
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Research Design in Social Data Science
Using big data and computational methods comes with unique challenges, and this book shows you how to overcome these in a core element of research: design. It equips you with the information you need to ask good research questions and shape your research interests into an ethical, well-developed research proposal.At an approachable pace, it:Demonstrates how design touches every aspect of research, from finding literature to determining data analysis techniques. Offers step-by-step advice on how to produce a high quality research proposal, whether as an end-goal or part of a wider study. Helps you map the research field, covering qualitative, quantitative and mixed methodologies and concepts. Grounded in examples of computational social science research, this book helps you develop your research design skills and confidently plan your research project. Relevant case studies and journal articles will allow you to see the concepts you're learning put into practice, and will help you to explore related literature in your discipline.For students across courses relating to social data science and computational social science, this book is a step-by-step guide to help anyone producing a research proposal and project.
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Handbook of Research on Science Teacher Education
This groundbreaking handbook offers a contemporary and thorough review of research relating directly to the preparation, induction, and career long professional learning of K–12 science teachers. Through critical and concise chapters, this volume provides essential insights into science teacher education that range from their learning as individuals to the programs that cultivate their knowledge and practices.Each chapter is a current review of research that depicts the area, and then points to empirically based conclusions or suggestions for science teacher educators or educational researchers.Issues associated with equity are embedded within each chapter.Drawing on the work of over one hundred contributors from across the globe, this handbook has 35 chapters that cover established, emergent, diverse, and pioneering areas of research, including: Research methods and methodologies in science teacher education, including discussions of the purpose of science teacher education research and equitable perspectives; Formal and informal teacher education programs that span from early childhood educators to the complexity of preparation, to the role of informal settings such as museums; Continuous professional learning of science teachers that supports building cultural responsiveness and teacher leadership; Core topics in science teacher education that focus on teacher knowledge, educative curricula, and working with all students; and Emerging areas in science teacher education such as STEM education, global education, and identity development. This comprehensive, in-depth text will be central to the work of science teacher educators, researchers in the field of science education, and all those who work closely with science teachers.
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Could science fiction technology become real?
Yes, science fiction technology could become real in the future. Many of the technological advancements that were once considered science fiction, such as video calling, self-driving cars, and virtual reality, have already become a reality. With continued advancements in science and technology, it is possible that other futuristic technologies, such as teleportation or advanced artificial intelligence, could also become a reality in the future. While some science fiction technologies may still be far-fetched, it is important to remember that many of today's realities were once considered impossible in the past.
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Should I study Data Science?
Studying Data Science can be a great choice if you are interested in working with data, analyzing trends, and deriving insights to make informed decisions. It is a rapidly growing field with high demand for skilled professionals. By studying Data Science, you can acquire valuable skills in programming, statistics, and machine learning that are applicable across various industries. However, it is important to consider your interests, career goals, and the time and effort required to excel in this field before making a decision.
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What is Natural Science and Technology (NWT)?
Natural Science and Technology (NWT) is an interdisciplinary subject that combines elements of natural sciences, technology, and engineering. It focuses on understanding the natural world, how technology impacts our lives, and how we can use scientific knowledge to solve real-world problems. NWT encourages students to explore the connections between science and technology and how they influence each other in our daily lives. Through hands-on experiments, research projects, and critical thinking activities, students develop a deeper understanding of the world around them and the role of science and technology in shaping our future.
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How are natural science and technology related?
Natural science and technology are closely related as they both involve the study and understanding of the natural world. Natural science seeks to understand the fundamental principles and laws that govern the natural world, while technology applies this understanding to create practical solutions to real-world problems. Technology often builds upon the knowledge gained from natural science to develop new tools, materials, and processes that improve our lives and our understanding of the natural world. In this way, natural science and technology work hand in hand to advance our understanding and utilization of the natural world.
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Communication Science Theory and Research : An Advanced Introduction
This volume provides a graduate-level introduction to communication science, including theory and scholarship for masters and PhD students as well as practicing scholars.The work defines communication, reviews its history, and provides a broad look at how communication research is conducted.It also includes chapters reviewing the most frequently addressed topics in communication science.This book presents an overview of theory in general and of communication theory in particular, while offering a broad look at topics in communication that promote understanding of the key issues in communication science for students and scholars new to communication research.The book takes a predominantly "communication science" approach but also situates this approach in the broader field of communication, and addresses how communication science is related to and different from such approaches as critical and cultural studies and rhetoric.As an overview of communication science that will serve as a reference work for scholars as well as a text for the introduction to communication graduate studies course, this volume is an essential resource for understanding and conducting scholarship in the communication discipline.
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Pioneering Progress : American Science, Technology, and Innovation Policy
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Science Communication
The volume gives a multi-perspective overview of scholarly and science communication, exploring its diverse functions, modalities, interactional structures, and dynamics in a rapidly changing world.In addition, it provides a guide to current research approaches and traditions on communication in many disciplines, including the humanities, technology, social and natural sciences, and on forms of communication with a wide range of audiences.
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Data Science in Education Using R
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.
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Which Arab country is the most advanced in terms of education, technology, and science?
It is difficult to determine which Arab country is the most advanced in terms of education, technology, and science as different countries excel in different areas. However, countries like the United Arab Emirates, Qatar, and Saudi Arabia have made significant investments in these sectors and have seen rapid advancements in recent years. The UAE, for example, has established itself as a hub for innovation and technology with initiatives like Dubai's Smart City project and the establishment of world-class universities. Qatar has also made strides in education and research with institutions like Qatar Foundation and Education City.
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Which computer science education makes sense?
Computer science education that is practical, hands-on, and relevant to real-world applications makes the most sense. This type of education should focus on teaching programming languages, problem-solving skills, and critical thinking, as well as providing opportunities for students to work on projects and collaborate with others. Additionally, a curriculum that is updated regularly to keep pace with advancements in technology and industry trends is essential for preparing students for successful careers in the field. Ultimately, a well-rounded computer science education that combines theoretical knowledge with practical skills is the most effective in preparing students for the demands of the industry.
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Which computer science education is meaningful?
Computer science education that is meaningful is one that goes beyond just teaching coding skills. It should focus on problem-solving, critical thinking, and creativity, as well as ethical considerations and real-world applications. Meaningful computer science education should also be inclusive and accessible to all students, regardless of their background or prior experience. Ultimately, a meaningful computer science education should empower students to use technology to make a positive impact on the world around them.
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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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