Improving Equity In Data Science

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Improving Equity In Data Science

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Author : Colby Tofel-Grehl
language : en
Publisher: Taylor & Francis
Release Date : 2024-06-03

Improving Equity In Data Science written by Colby Tofel-Grehl and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-03 with Education categories.

Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms.

Data Science In Education Using R

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Author : Ryan A. Estrellado
language : en
Publisher: Routledge
Release Date : 2020-10-26

Data Science In Education Using R written by Ryan A. Estrellado and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-26 with Education categories.

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.

Advancing Data Science Education In K 12

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Author : Victor R. Lee
language : en
Publisher: Taylor & Francis
Release Date : 2025-02-24

Advancing Data Science Education In K 12 written by Victor R. Lee and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-24 with Education categories.

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.

Big Data Science And Analytics For Smart Sustainable Urbanism

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Author : Simon Elias Bibri
language : en
Publisher: Springer
Release Date : 2019-05-30

Big Data Science And Analytics For Smart Sustainable Urbanism written by Simon Elias Bibri and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-30 with Social Science categories.

We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holisticand integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.

Driving Quality Education Through Ai And Data Science

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Author : Murugan, Thangavel
language : en
Publisher: IGI Global
Release Date : 2025-02-13

Driving Quality Education Through Ai And Data Science written by Murugan, Thangavel and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-13 with Computers categories.

Artificial intelligence (AI) and data science have the potential to address the challenges the education field faces. By integrating AI into the educational system, such as through personalized learning experiences to intelligent tutoring systems, AI can help tailor educational content to individual students' needs, improving engagement and outcomes. Data science can be used to analyze educational data, uncover insights, and inform decision-making. The result is that teachers may be given the tools and knowledge they need to excel in the classroom. This shift not only improves educational outcomes but also prepares students for a data-driven future. Driving Quality Education Through AI and Data Science explores how advancements in AI and data science can be utilized to enhance the quality of education. It provides insights, strategies, and best practices for leveraging AI and data science technologies to enhance teaching and learning. Covering topics such as data-driven decisions, at-risk students, and student performance prediction, this book is an excellent resource for educators, policymakers, professionals, researchers, scholars, academicians, and more.

Roundtable On Data Science Postsecondary Education

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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2020-09-02

Roundtable On Data Science Postsecondary Education written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-02 with Education categories.

Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting.

Data Science And Business Intelligence For Corporate Decision Making

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Author : Dr. P. S. Aithal
language : en
Publisher: Srinivas Publication, Mangalore
Release Date : 2024-02-09

Data Science And Business Intelligence For Corporate Decision Making written by Dr. P. S. Aithal and has been published by Srinivas Publication, Mangalore this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-09 with Art categories.

About the Book: A comprehensive book plan on "Data Science and Business Intelligence for Corporate Decision-Making" with 15 chapters, each with several sections: Chapter 1: Introduction to Data Science and Business Intelligence Chapter 2: Foundations of Data Science Chapter 3: Business Intelligence Tools and Technologies Chapter 4: Data Visualization for Decision-Making Chapter 5: Machine Learning for Business Intelligence Chapter 6: Big Data Analytics Chapter 7: Data Ethics and Governance Chapter 8: Data-Driven Decision-Making Process Chapter 9: Business Intelligence in Marketing Chapter 10: Financial Analytics and Business Intelligence Chapter 11: Operational Excellence through Data Analytics Chapter 12: Human Resources and People Analytics Chapter 13: Case Studies in Data-Driven Decision-Making Chapter 14: Future Trends in Data Science and Business Intelligence Chapter 15: Implementing Data Science Strategies in Corporations Each chapter dives deep into the concepts, methods, and applications of data science and business intelligence, providing practical insights, real-world examples, and case studies for corporate decision-making processes.

Big Data And Social Science

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Author : Ian Foster
language : en
Publisher: CRC Press
Release Date : 2020-11-17

Big Data And Social Science written by Ian Foster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-17 with Mathematics categories.

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features: Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHub New to the Second Edition: Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Data Science Exam Preparation

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Author : cybellim
language : en
Publisher: Cybellium Ltd
Release Date : 2024-10-26

Data Science Exam Preparation written by cybellim and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-26 with Study Aids categories.

Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com

Recent Developments In Data Science And Business Analytics

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Author : Madjid Tavana
language : en
Publisher: Springer
Release Date : 2018-03-27

Recent Developments In Data Science And Business Analytics written by Madjid Tavana and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-27 with Business & Economics categories.

This edited volume is brought out from the contributions of the research papers presented in the International Conference on Data Science and Business Analytics (ICDSBA- 2017), which was held during September 23-25 2017 in ChangSha, China. As we all know, the field of data science and business analytics is emerging at the intersection of the fields of mathematics, statistics, operations research, information systems, computer science and engineering. Data science and business analytics is an interdisciplinary field about processes and systems to extract knowledge or insights from data. Data science and business analytics employ techniques and theories drawn from many fields including signal processing, probability models, machine learning, statistical learning, data mining, database, data engineering, pattern recognition, visualization, descriptive analytics, predictive analytics, prescriptive analytics, uncertainty modeling, big data, data warehousing, data compression, computer programming, business intelligence, computational intelligence, and high performance computing among others. The volume contains 55 contributions from diverse areas of Data Science and Business Analytics, which has been categorized into five sections, namely: i) Marketing and Supply Chain Analytics; ii) Logistics and Operations Analytics; iii) Financial Analytics. iv) Predictive Modeling and Data Analytics; v) Communications and Information Systems Analytics. The readers shall not only receive the theoretical knowledge about this upcoming area but also cutting edge applications of this domains.