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24500 ▼a Data science for undergraduates : ▼b opportunities and options/ ▼c Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, Computer Science and Telecommunications Board, Board on Mathematical Sciences and Analytics, Committee on Applied and Theoretical Statistics, Division on Engineering and Physical Sciences, Board on Science Education, Division of Behavioral and Social Sciences and Education. ▼h [electronic resource].
260 1 ▼a Washington, DC: ▼b National Academies Press, ▼c [2018].
300 ▼a 1 online resource (1 PDF file (xviii, 119 pages)): ▼b illustration.
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4901 ▼a Consensus study report
500 ▼a "A consensus study report of the National Academies of Sciences, Engineering, Medicine."
504 ▼a Includes bibliographical references.
5050 ▼a Introduction -- Knowledge for data scientists -- Data science eduction -- Starting a data science program -- Evolution and evaluation -- Conclusions -- Appendixes.
5203 ▼a 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.
536 ▼a This activity was supported by Award No. 1626983 from the National Science Foundation (Directorate for Computer and Information Science and Engineering; Directorate for Education and Human Resources; Directorate for Mathematical and Physical Sciences/Division of Mathematical Sciences; and Directorate for Social, Behavioral and Economic Sciences). Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.
588 ▼a Description based on online resource; title from PDF title page (viewed February 4, 2019).
590 ▼a Master record variable field(s) change: 650
650 0 ▼a Science ▼x Study and teaching.
650 0 ▼a Data mining ▼x Study and teaching.
650 7 ▼a SCIENCE ▼x Study & Teaching. ▼2 bisacsh
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65012 ▼a Data Science ▼x education.
65022 ▼a Schools ▼x standards.
65022 ▼a Interdisciplinary Placement.
65022 ▼a Teaching ▼x standards.
65022 ▼a Faculty ▼x education.
65022 ▼a Program Evaluation.
651 2 ▼a United States.
655 4 ▼a Electronic books.
7102 ▼a National Academies of Sciences, Engineering, and Medicine (U.S.). ▼b Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, ▼e issuing body.
7102 ▼a National Academies of Sciences, Engineering, and Medicine (U.S.). ▼b Computer Science and Telecommunications Board, ▼e issuing body.
7102 ▼a National Academies of Sciences, Engineering, and Medicine (U.S.). ▼b Board on Mathematical Sciences and Analytics, ▼e issuing body.
7102 ▼a National Academies of Sciences, Engineering, and Medicine (U.S.). ▼b Committee on Applied and Theoretical Statistics, ▼e issuing body.
7102 ▼a National Academies of Sciences, Engineering, and Medicine (U.S.). ▼b Board on Science Education, ▼e issuing body.
77608 ▼i Print version: ▼z 0309475597 ▼z 9780309475594 ▼w (OCoLC)1036273927
830 0 ▼a Consensus study report.
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