Olin College Research Study: Data Science for Engineers

Image credit: Zachary del Rosario

Please forward this message to any engineers you think may be interested in this opportunity.

Data science is the use of computational tools to access and make sense of data. Data science has the potential to accelerate engineering and produce more efficient technology. However, such tools are typically marketed to the biological sciences, financial sector, and “Big Tech”—it is not immediately clear how to use “cat-detector technology” to support serious engineering!

Thanks to a generous grant from the National Science Foundation, I am running a study of practicing engineers. This study includes a free 6-week professional development course on data science in engineering. This course is designed for engineers with a background in structural engineering, such as mechanical, civil, aerospace, and related disciplines.

This course will cover visualizing data, managing complex datasets, and applying statistics to learn from engineering data. The course is designed to be compatible with full-time employment (taught online, outside working hours), will consist of hands-on training with computational tools, and will include live instruction with a professor from Olin College.

While participation in the study is free, space is limited. If you are interested in the study, please fill out this application:


If you are selected for the study, we will contact you at your provided email address with more information. If you would like more information about study, please contact me. Please do not hesitate to contact me if you are interested in learning more about this Institutional Review Board-approved project.

This work is supported by the NSF under Grant Number #2138463. For more information, please see this flyer.

Warm regards,

Zachary del Rosario, Assistant Professor, Olin College

Zachary del Rosario
Zachary del Rosario
Assistant Professor of Engineering and Applied Statistics

Empowering scientists and engineers to reason under uncertainty