Envisioning The Data Science Discipline

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Publisher : Unknown
Page : 68 pages
File Size : 41,9 Mb
Release : 2018-06-27
Category : Education
ISBN : 0309465028

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Envisioning the Data Science Discipline by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Division on Engineering and Physical Sciences,Board on Science Education,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective Pdf

The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation�e(tm)s ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Publisher : National Academies Press
Page : 69 pages
File Size : 41,6 Mb
Release : 2018-03-05
Category : Education
ISBN : 9780309465052

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Envisioning the Data Science Discipline by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective Pdf

The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Publisher : National Academies Press
Page : 139 pages
File Size : 43,6 Mb
Release : 2018-11-11
Category : Education
ISBN : 9780309475594

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Data Science for Undergraduates by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective Pdf

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Author : National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective
Publisher : National Academies Press
Page : 139 pages
File Size : 54,5 Mb
Release : 2018-10-11
Category : Education
ISBN : 9780309475624

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Data Science for Undergraduates by National Academies of Sciences, Engineering, and Medicine,Division of Behavioral and Social Sciences and Education,Board on Science Education,Division on Engineering and Physical Sciences,Committee on Applied and Theoretical Statistics,Board on Mathematical Sciences and Analytics,Computer Science and Telecommunications Board,Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective Pdf

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Author : Orit Hazzan,Koby Mike
Publisher : Springer Nature
Page : 330 pages
File Size : 44,7 Mb
Release : 2023-03-20
Category : Computers
ISBN : 9783031247583

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Guide to Teaching Data Science by Orit Hazzan,Koby Mike Pdf

Data science is a new field that touches on almost every domain of our lives, and thus it is taught in a variety of environments. Accordingly, the book is suitable for teachers and lecturers in all educational frameworks: K-12, academia and industry. This book aims at closing a significant gap in the literature on the pedagogy of data science. While there are many articles and white papers dealing with the curriculum of data science (i.e., what to teach?), the pedagogical aspect of the field (i.e., how to teach?) is almost neglected. At the same time, the importance of the pedagogical aspects of data science increases as more and more programs are currently open to a variety of people. This book provides a variety of pedagogical discussions and specific teaching methods and frameworks, as well as includes exercises, and guidelines related to many data science concepts (e.g., data thinking and the data science workflow), main machine learning algorithms and concepts (e.g., KNN, SVM, Neural Networks, performance metrics, confusion matrix, and biases) and data science professional topics (e.g., ethics, skills and research approach). Professor Orit Hazzan is a faculty member at the Technion’s Department of Education in Science and Technology since October 2000. Her research focuses on computer science, software engineering and data science education. Within this framework, she studies the cognitive and social processes on the individual, the team and the organization levels, in all kinds of organizations. Dr. Koby Mike is a Ph.D. graduate from the Technion's Department of Education in Science and Technology under the supervision of Professor Orit Hazzan. He continued his post-doc research on data science education at the Bar-Ilan University, and obtained a B.Sc. and an M.Sc. in Electrical Engineering from Tel Aviv University.

Author : Renata Rawlings-Goss
Publisher : Springer
Page : 85 pages
File Size : 47,8 Mb
Release : 2019-08-02
Category : Education
ISBN : 9783030224073

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Data Science Careers, Training, and Hiring by Renata Rawlings-Goss Pdf

This book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build.

Author : Judith A. Langer
Publisher : Teachers College Press
Page : 190 pages
File Size : 47,8 Mb
Release : 2015-04-17
Category : Education
ISBN : 9780807770740

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Envisioning Knowledge by Judith A. Langer Pdf

This book by Judith Langer—internationally known scholar in literacy learning—examines how people gain knowledge and become academically literate in the core subjects of English, mathematics, science, and social studies/history. Based on extensive research, it offers a new framework for conceptualizing knowledge development (rather than information collection), and explores how one becomes literate in ways that mark "knowing" in a field. Langer identifies key principles for practice and demonstrates how the framework and the principles together can undergird highly successful instruction across the curriculum. With many examples from middle and high schools, this resource will help educators to plan and implement engaging, exciting, and academically successful programs.

Author : Aakanksha Sharaff,G R Sinha
Publisher : CRC Press
Page : 443 pages
File Size : 51,9 Mb
Release : 2021-08-18
Category : Computers
ISBN : 9781000414004

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Data Science and Its Applications by Aakanksha Sharaff,G R Sinha Pdf

The term "data" being mostly used, experimented, analyzed, and researched, "Data Science and its Applications" finds relevance in all domains of research studies including science, engineering, technology, management, mathematics, and many more in wide range of applications such as sentiment analysis, social medial analytics, signal processing, gene analysis, market analysis, healthcare, bioinformatics etc. The book on Data Science and its applications discusses about data science overview, scientific methods, data processing, extraction of meaningful information from data, and insight for developing the concept from different domains, highlighting mathematical and statistical models, operations research, computer programming, machine learning, data visualization, pattern recognition and others. The book also highlights data science implementation and evaluation of performance in several emerging applications such as information retrieval, cognitive science, healthcare, and computer vision. The data analysis covers the role of data science depicting different types of data such as text, image, biomedical signal etc. useful for a wide range of real time applications. The salient features of the book are: Overview, Challenges and Opportunities in Data Science and Real Time Applications Addressing Big Data Issues Useful Machine Learning Methods Disease Detection and Healthcare Applications utilizing Data Science Concepts and Deep Learning Applications in Stock Market, Education, Behavior Analysis, Image Captioning, Gene Analysis and Scene Text Analysis Data Optimization Due to multidisciplinary applications of data science concepts, the book is intended for wide range of readers that include Data Scientists, Big Data Analysists, Research Scholars engaged in Data Science and Machine Learning applications.

Author : Harald A. Mieg,Elizabeth Ambos,Angela Brew,Dominique Galli,Judith Lehmann
Publisher : Cambridge University Press
Page : 877 pages
File Size : 42,6 Mb
Release : 2022-07-07
Category : Education
ISBN : 9781108874670

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The Cambridge Handbook of Undergraduate Research by Harald A. Mieg,Elizabeth Ambos,Angela Brew,Dominique Galli,Judith Lehmann Pdf

Undergraduate Research (UR) can be defined as an investigation into a specific topic within a discipline by an undergraduate student that makes an original contribution to the field. It has become a major consideration among research universities around the world, in order to advance both academic teaching and research productivity. Edited by an international team of world authorities in UR, this Handbook is the first truly comprehensive and systematic account of undergraduate research, which brings together different international approaches, with attention to both theory and practice. It is split into sections covering different countries, disciplines, and methodologies. It also provides an overview of current research and theoretical perspectives on undergraduate research as well as future developmental prospects of UR. Written in an engaging style, yet wide-ranging in its scope, it is essential reading for anyone wishing to broaden their understanding of how undergraduate research is implemented worldwide.

Author : Eta S. Berner
Publisher : Springer Nature
Page : 308 pages
File Size : 52,8 Mb
Release : 2020-10-19
Category : Medical
ISBN : 9783030538132

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Informatics Education in Healthcare by Eta S. Berner Pdf

This heavily revised second edition defines the current state of the art for informatics education in medicine and healthcare. This field has continued to undergo considerable changes as the field of informatics continues to evolve. The book features extensively revised chapters addressing the latest developments in areas including relevant informatics concepts for those who work in health information technology and those teaching informatics courses in clinical settings, techniques for teaching informatics with limited resources, and the use of online modalities in bioinformatics research education. New topics covered include how to get appropriate accreditation for an informatics program, data science and bioinformatics education, and undergraduate health informatics education. Informatics Education in Healthcare: Lessons Learned addresses the broad range of informatics education programs and available techniques for teaching informatics. It therefore provides a valuable reference for all involved in informatics education.

Author : Petter Nielsen,Honest Christopher Kimaro
Publisher : Springer
Page : 461 pages
File Size : 50,9 Mb
Release : 2019-04-26
Category : Computers
ISBN : 9783030191153

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Information and Communication Technologies for Development. Strengthening Southern-Driven Cooperation as a Catalyst for ICT4D by Petter Nielsen,Honest Christopher Kimaro Pdf

The two volumes IFIP AICT 551 and 552 constitute the refereed proceedings of the 15th IFIP WG 9.4 International Conference on Social Implications of Computers in Developing Countries, ICT4D 2019, held in Dar es Salaam, Tanzania, in May 2019. The 97 revised full papers and 2 short papers presented were carefully reviewed and selected from 185 submissions. The papers present a wide range of perspectives and disciplines including (but not limited to) public administration, entrepreneurship, business administration, information technology for development, information management systems, organization studies, philosophy, and management. They are organized in the following topical sections: communities, ICT-enabled networks, and development; digital platforms for development; ICT for displaced population and refugees. How it helps? How it hurts?; ICT4D for the indigenous, by the indigenous and of the indigenous; local technical papers; pushing the boundaries - new research methods, theory and philosophy in ICT4D; southern-driven human-computer interaction; sustainable ICT, informatics, education and learning in a turbulent world - "doing the safari way”.

Author : Rochelle Tractenberg
Publisher : Ethics International Press
Page : 411 pages
File Size : 45,7 Mb
Release : 2022-10-25
Category : Language Arts & Disciplines
ISBN : 9781804410790

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Ethical Reasoning for a Data-Centered World by Rochelle Tractenberg Pdf

The American Statistical Association (ASA) and the Association of Computing Machinery (ACM) have longstanding ethical practice standards that are explicitly intended to be utilized by all who use statistical practices or computing, or both. Since statistics and computing are critical in any data-centered activity, these practice standards are essential to instruction in the uses of statistical practices or computing across disciplines. Ethical Reasoning For A Data-Centered World is aimed at any undergraduate or graduate students utilizing data. Whether the career goal is research, teaching, business, government, or a combination, this book presents a method for understanding and prioritizing ethical statistics, computing, and data science – featuring the ASA and ACM practice standards. To facilitate engagement, integration with prior learning, and authenticity, the material is organized around seven tasks: Planning/Designing; Data collection; Analysis; Interpretation; Reporting; Documenting; and Engaging in team work. This book is a companion volume to Ethical Practice of Statistics and Data Science, also published by Ethics International Press (2022). These are the first and only books to be based on, and to provide guidance to, the American Statistical Association (ASA) and Association of Computing Machinery (ACM) ethical guideline documents.

Author : United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies
Publisher : Unknown
Page : 1190 pages
File Size : 54,5 Mb
Release : 2017
Category : United States
ISBN : IND:30000159353915

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Commerce, Justice, Science, and Related Agencies Appropriations for 2018: U.S. Census Bureau and the Government Accountability Office; Department of Justice; Federal Bureau of Investigation by United States. Congress. House. Committee on Appropriations. Subcommittee on Commerce, Justice, Science, and Related Agencies Pdf

Author : Chong Ho Alex Yu
Publisher : CRC Press
Page : 290 pages
File Size : 52,7 Mb
Release : 2022-10-27
Category : Business & Economics
ISBN : 9781000777796

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Data Mining and Exploration by Chong Ho Alex Yu Pdf

This book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.

Author : Amit Kumar Tyagi
Publisher : CRC Press
Page : 482 pages
File Size : 51,9 Mb
Release : 2021-09-22
Category : Computers
ISBN : 9781000423198

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Data Science and Data Analytics by Amit Kumar Tyagi Pdf

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity.