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090 UC263
245 00 Empowering the defense acquisition workforce to improve
mission outcomes using data science /|cCommittee on
Improving Defense Acquisition Workforce Capability in Data
Use ; Board on Mathematical Sciences and Analytics,
Committee on Applied and Theoretical Statistics, Air Force
Studies Board, Computer Science and Telecommunications
Board, Division on Engineering and Physical Sciences ;
Board on Higher Education and Workforce, Policy and Global
Affairs ; Committee on National Statistics, Division of
Behavioral and Social Sciences and Education.
264 1 Washington, DC :|bThe National Academies Press,|c[2021]
264 4 |c©2021
300 1 online resource (xx, 134 pages) :|billustrations.
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file|2rdaft
490 1 Consensus study report of the National Academies of
Sciences, Engineering, Medicine
504 Includes bibliographical references.
505 0 Defense acquisition process data, and workforce: the short
version -- Data science and the data life cycle: the short
version -- Data science in DoD acquisition -- Data life
cycle mindset, skillset, and toolset: roles and teams --
Preparing and sustaining a data-capable defence
acquisition workforce -- Finding, conclusions, and
recommendations -- Appendix A. Meeting and workshop
agendas -- Appendix B. Defence acquisition notes --
Appendix C. Data science case studies in defense
acquisition -- Appendix D. Skills for data science mastery
-- Appendix E. Glossary of terms, abbreviations, and
acronyms -- Appendix F. Committee member biographies
520 "The effective use of data science (the science and
technology of extracting value from data) improves,
enhances, and strengthens acquisition decision-making and
outcomes. Using data science to support decision making is
not new to the defense acquisition community; its use by
the acquisition workforce has enabled acquisition and thus
defense successes for decades. Still, more consistent and
expanded application of data science will continue
improving acquisition outcomes, and doing so requires
coordinated efforts across the defense acquisition system
and its related communities and stakeholders. Central to
that effort is the development, growth, and sustainment of
data science capabilities across the acquisition
workforce. At the request of the Under Secretary of
Defense for Acquisition and Sustainment, this book
assesses how data science can improve acquisition
processes and develops a framework for training and
educating the defense acquisition workforce to better
exploit the application of data science. This report
identifies opportunities where data science can improve
acquisition processes, the relevant data science skills
and capabilities necessary for the acquisition workforce,
and relevant models of data science training and
education"--|cProvided by the publisher
588 0 Online resource; title from title screen (viewed December
2, 2021).
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
610 10 United States.|bDepartment of Defense|0https://id.loc.gov/
authorities/names/n79021946|xProcurement.|0https://
id.loc.gov/authorities/subjects/sh00006774
610 17 United States.|bDepartment of Defense.|2fast|0https://
id.worldcat.org/fast/1852447
650 0 Defense contracts|zUnited States.|0https://id.loc.gov/
authorities/subjects/sh2008102128
650 0 Public contracts|zUnited States.|0https://id.loc.gov/
authorities/subjects/sh85108630
650 0 Government purchasing|zUnited States.|0https://id.loc.gov/
authorities/subjects/sh2008105373
650 0 Data integration (Computer science)|0https://id.loc.gov/
authorities/subjects/sh2010014388
650 7 Defense contracts.|2fast|0https://id.worldcat.org/fast/
889611
650 7 Public contracts.|2fast|0https://id.worldcat.org/fast/
1082170
650 7 Government purchasing.|2fast|0https://id.worldcat.org/fast
/945538
650 7 Data integration (Computer science)|2fast|0https://
id.worldcat.org/fast/1763872
650 7 Armed Forces|xProcurement.|2fast|0https://id.worldcat.org/
fast/814624
651 7 United States.|2fast|0https://id.worldcat.org/fast/1204155
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bBoard on Mathematical Sciences and Analytics,
|0https://id.loc.gov/authorities/names/n2019180879
|eissuing body.
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bCommittee on Applied and Theoretical Statistics,
|0https://id.loc.gov/authorities/names/n2016187127
|eissuing body.
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bAir Force Studies Board,|0https://id.loc.gov/
authorities/names/nb2021003855|eissuing body.
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bComputer Science and Telecommunications Board,
|0https://id.loc.gov/authorities/names/no2016104865
|eissuing body.
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bDivision on Engineering and Physical Sciences,
|0https://id.loc.gov/authorities/names/no2016010727
|eissuing body.
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bCommittee on National Statistics,|0https://
id.loc.gov/authorities/names/nb2016019866|eissuing body.
710 2 National Academies of Sciences, Engineering, and Medicine
(U.S.).|bDivision of Behavioral and Social Sciences and
Education,|0https://id.loc.gov/authorities/names/
n2015190784|eissuing body.
776 08 |iPrint version:|aNational Academies of Sciences,
Engineering, and Medicine.|tEmpowering the Defense
Acquisition Workforce to Improve Mission Outcomes Using
Data Science.|dWashington, D.C. : National Academies Press,
©2021|z9780309684934
830 0 Consensus study report.|0https://id.loc.gov/authorities/
names/n2017188206
856 40 |uhttps://rider.idm.oclc.org/login?url=https://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=3077688|zOnline ebook via EBSCO. Access
restricted to current Rider University students, faculty,
and staff.
856 42 |3Instructions for reading/downloading the EBSCO version
of this ebook|uhttp://guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20230922|cEBSCO |tebscoebooksacademic NEW JULY Quarterly
6516|lridw
994 92|bRID
