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The emerging field of Data Science has had a large impact on science and society. This book explores how one distinguishing feature of Data Science – its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded – suggests serious implications for learning and education. Drawing from theories of learning and identity development in the learning sciences, this volume investigates the impacts of these complex relationships on how learners think about, use, and share data, including their understandings of data in light of history, race, geography, and politics. More than just using ‘real world examples’ to motivate students to work with data, this book demonstrates how learners’ relationships to data shape how they approach those data with agency, as part of their social and cultural lives. Together, the contributions offer a vision of how the learning sciences can contribute to a more expansive, socially aware, and transformative Data Science Education. The chapters in this book were originally published as a special issue of the Journal of the Learning Sciences.
This book explores how one distinguishing feature of Data Science - its focus on data collected from social and environmental contexts within which learners often find themselves deeply embedded - suggests serious implications for learning and education.
A fascinating look at Open Science and the democratization of knowledge in international development and social transformation.
The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries
Over the past decade, digital geographies has emerged as a dynamic area of scholarly enquiry, critically examining how the digital has reshaped the geography of our world. Bringing together authors working at the cutting-edge of the field, and grounding abstract ideas in case studies, this Research Agenda looks at the ways in which technology has altered all aspects of society, culture and the environment.
The study of everyday life is fundamental to our understanding of modern society. This agenda-setting book provides a coherent, interdisciplinary way to engage with everyday activities and environments. Arguing for an innovative, ethnographic approach, it uses detailed examples, based in real world and digital research, to bring its theories to life. The book focuses on the sensory, embodied, mobile and mediated elements of practice and place as a route to understanding wider issues. By doing so, it convincingly outlines a robust theoretical and methodological approach to understanding contemporary everyday life and activism. A fresh, timely book, this is an excellent resource for students and researchers of everyday life, activism and sustainability across the social sciences.
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.
In recent decades, there has been a major shift in the way researchers process and understand scientific data. Digital access to data has revolutionized ways of doing science in the biological and biomedical fields, leading to a data-intensive approach to research that uses innovative methods to produce, store, distribute, and interpret huge amounts of data. In Data-Centric Biology, Sabina Leonelli probes the implications of these advancements and confronts the questions they pose. Are we witnessing the rise of an entirely new scientific epistemology? If so, how does that alter the way we study and understand life—including ourselves? Leonelli is the first scholar to use a study of contemporary data-intensive science to provide a philosophical analysis of the epistemology of data. In analyzing the rise, internal dynamics, and potential impact of data-centric biology, she draws on scholarship across diverse fields of science and the humanities—as well as her own original empirical material—to pinpoint the conditions under which digitally available data can further our understanding of life. Bridging the divide between historians, sociologists, and philosophers of science, Data-Centric Biology offers a nuanced account of an issue that is of fundamental importance to our understanding of contemporary scientific practices.
Socio-scientific issues (SSI) are open-ended, multifaceted social issues with conceptual links to science. They are challenging to negotiate and resolve, and they create ideal contexts for bridging school science and the lived experience of students. This book presents the latest findings from the innovative practice and systematic investigation of science education in the context of socio-scientific issues. Socio-scientific Issues in the Classroom: Teaching, Learning and Research focuses on how SSI can be productively incorporated into science classrooms and what SSI-based education can accomplish regarding student learning, practices and interest. It covers numerous topics that address key themes for contemporary science education including scientific literacy, goals for science teaching and learning, situated learning as a theoretical perspective for science education, and science for citizenship. It presents a wide range of classroom-based research projects that offer new insights for SSI-based education. Authored by leading researchers from eight countries across four continents, this book is an important compendium of syntheses and insights for veteran researchers, teachers and curriculum designers eager to advance the SSI agenda.