Difference between revisions of "DSECOP Workshop Maryland 2022"
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* ''Presenter'': [https://socr.umich.edu/people/dinov/ Ivo D. Dinov] (University of Michigan) | * ''Presenter'': [https://socr.umich.edu/people/dinov/ Ivo D. Dinov] (University of Michigan) | ||
* ''Title'': ''Integration of Data Science into the STEM Curricula'' | * ''Title'': ''Integration of Data Science into the STEM Curricula'' | ||
| − | * ''Abstract'': Over the past decade, data-intensive research has permeated all scientific domains, from physics, to biology, economics, environmental studies, mathematics, engineering, and humanities. This information-rich wave has impacted most applied and observational studies, as well as the theoretical sciences. I will briefly outline the foundational principles of data science and demonstrate examples of blending mathematical-physics, data science, artificial intelligence, and biomedical physics applications. In addition, I will present details about an R-based electronic markdown notebook framework (Rmd) for embedding physics research, mathematical models, computational statistics, and data science techniques into the undergraduate and graduate curricula, e.g., data science and predictive analytics (DSPA), biomedical physics with applications to disease (BPAD). Joint work with Magdalena I. Ivanova (Michigan). | + | * ''Abstract'': Over the past decade, data-intensive research has permeated all scientific domains, from physics, to biology, economics, environmental studies, mathematics, engineering, and humanities. This information-rich wave has impacted most applied and observational studies, as well as the theoretical sciences. I will briefly outline the foundational principles of data science and demonstrate examples of blending mathematical-physics, data science, artificial intelligence, and biomedical physics applications. In addition, I will present details about an R-based electronic markdown notebook framework (Rmd) for embedding physics research, mathematical models, computational statistics, and data science techniques into the undergraduate and graduate curricula, e.g., [https://dspa2.predictive.space/ data science and predictive analytics (DSPA)], [https://socr.umich.edu/BPAD/ biomedical physics with applications to disease (BPAD)]. Joint work with Magdalena I. Ivanova (Michigan). |
| − | * References | + | * ''References'' |
| − | + | : [https://socr.umich.edu/people/dinov/publications.html Publications] | |
| − | + | : [https://socr.umich.edu Website]. | |
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Revision as of 07:53, 25 May 2022
SOCR News & Events: APS GDS/DSECOP Workshop – June 2022
The Data Science Education Community of Practice (DSECOP) is a program funded by the APS Innovation Fund and led by the APS Group on Data Science (GDS).
Logistics
- Event Dates: June 22–24, 2022, 3-days(US Eastern Time)
- Location: Institute for Physical Science and Technology, University of Maryland, College Park, MD
- Format: In-person event
- RSVP (required): preregistration is required for participation (space capacity is limited)
- Contacts: DSECOP contacts
- Event Page.
Outline
The primary goal of this workshop is to develop a community of practice around incorporating data science into the undergraduate physics curriculum. This workshop will include lectures from faculty who are already engaged in this process and presentations from fellows in our program developing modules for inclusion in the existing curriculum. We also expect participation from the industry. There will be discussions and contributed talks.
Talk
- Presenter: Ivo D. Dinov (University of Michigan)
- Title: Integration of Data Science into the STEM Curricula
- Abstract: Over the past decade, data-intensive research has permeated all scientific domains, from physics, to biology, economics, environmental studies, mathematics, engineering, and humanities. This information-rich wave has impacted most applied and observational studies, as well as the theoretical sciences. I will briefly outline the foundational principles of data science and demonstrate examples of blending mathematical-physics, data science, artificial intelligence, and biomedical physics applications. In addition, I will present details about an R-based electronic markdown notebook framework (Rmd) for embedding physics research, mathematical models, computational statistics, and data science techniques into the undergraduate and graduate curricula, e.g., data science and predictive analytics (DSPA), biomedical physics with applications to disease (BPAD). Joint work with Magdalena I. Ivanova (Michigan).
- References
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