# Difference between revisions of "DSECOP Workshop Maryland 2022"

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* ''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). | * ''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://wiki.socr.umich.edu/images/8/8c/Dinov_APS_DSECOP_UMD_June2022.pdf Slides] | ||

: [https://socr.umich.edu/people/dinov/publications.html Publications] | : [https://socr.umich.edu/people/dinov/publications.html Publications] | ||

: [https://socr.umich.edu Website]. | : [https://socr.umich.edu Website]. |

## Latest revision as of 16:08, 26 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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