The Emergence and Future of Public Health Data Science
Data science is a newly‐formed and, as yet, loosely‐defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term “data science,” particularly as it relates to public health; to identify ways that data scien...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-04-01
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| Series: | Public Health Reviews |
| Subjects: | |
| Online Access: | https://www.ssph-journal.org/articles/10.3389/phrs.2021.1604023/full |
| _version_ | 1818607491640983552 |
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| author | Jeff Goldsmith Yifei Sun Linda P. Fried Jeannette Wing Gary W. Miller Kiros Berhane |
| author_facet | Jeff Goldsmith Yifei Sun Linda P. Fried Jeannette Wing Gary W. Miller Kiros Berhane |
| author_sort | Jeff Goldsmith |
| collection | DOAJ |
| description | Data science is a newly‐formed and, as yet, loosely‐defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term “data science,” particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective. |
| first_indexed | 2024-12-16T14:27:36Z |
| format | Article |
| id | doaj.art-671cd6eb875449d99cbf51953c9eba68 |
| institution | Directory Open Access Journal |
| issn | 2107-6952 |
| language | English |
| last_indexed | 2024-12-16T14:27:36Z |
| publishDate | 2021-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Public Health Reviews |
| spelling | doaj.art-671cd6eb875449d99cbf51953c9eba682022-12-21T22:28:19ZengFrontiers Media S.A.Public Health Reviews2107-69522021-04-014210.3389/phrs.2021.16040231604023The Emergence and Future of Public Health Data ScienceJeff Goldsmith0Yifei Sun1Linda P. Fried2Jeannette Wing3Gary W. Miller4Kiros Berhane5Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, United StatesDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, United StatesColumbia University Mailman School of Public Health, New York, NY, United StatesData Science Institute, Columbia University, New York, NY, United StatesDepartment of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, United StatesDepartment of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, United StatesData science is a newly‐formed and, as yet, loosely‐defined discipline that has nonetheless emerged as a critical component of successful scientific research. We seek to provide an understanding of the term “data science,” particularly as it relates to public health; to identify ways that data science methods can strengthen public health research; to propose ways to strengthen education for public health data science; and to discuss issues in data science that may benefit from a public health perspective.https://www.ssph-journal.org/articles/10.3389/phrs.2021.1604023/fullmachine learningbig datacomputational methodsethicsreproducibilityinterdisciplinary science |
| spellingShingle | Jeff Goldsmith Yifei Sun Linda P. Fried Jeannette Wing Gary W. Miller Kiros Berhane The Emergence and Future of Public Health Data Science Public Health Reviews machine learning big data computational methods ethics reproducibility interdisciplinary science |
| title | The Emergence and Future of Public Health Data Science |
| title_full | The Emergence and Future of Public Health Data Science |
| title_fullStr | The Emergence and Future of Public Health Data Science |
| title_full_unstemmed | The Emergence and Future of Public Health Data Science |
| title_short | The Emergence and Future of Public Health Data Science |
| title_sort | emergence and future of public health data science |
| topic | machine learning big data computational methods ethics reproducibility interdisciplinary science |
| url | https://www.ssph-journal.org/articles/10.3389/phrs.2021.1604023/full |
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