Data Science for Undergraduates: Opportunities and Options
2018

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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.

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138 pages · 6 x 9 · paperback
ISBN Paperback: 0-309-47559-7
ISBN Ebook: 0-309-47560-0
DOI: https://doi.org/10.17226/25104

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National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options. Washington, DC: The National Academies Press.

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Front Matter i-xviii
Summary 1-5
1 Introduction 6-11
2 Knowledge for Data Scientists 12-34
3 Data Science Education 35-59
4 Starting a Data Science Program 60-71
5 Evolution and Evaluation 72-86
6 Conclusions 87-92
Appendixes 93-94
Appendix A: Biographies of the Committee 95-104
Appendix B: Meetings and Presentations 105-110
Appendix C: Contributing Individuals 111-116
Appendix D: Data Science Oath 117-120

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Data Science for Undergraduates: Opportunities and Options (2018)

Chapter: 2 Knowledge for Data Scientists

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Publication information

Suggested citation

My Academies

Data Science for Undergraduates: Opportunities and Options (2018)

Chapter: 2 Knowledge for Data Scientists