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Data Science plays a vital role in sciences and engineering disciplines to discover meaningful information and predict the outcome of real-world problems. Despite the significance of this field and high demand, knowledge of how to effectively provide data science research experience to STEM students is scarce. This paper focuses on the role of data science and analytics education to improve the students' computing and analytical skills across a range of domain-specific problems. The paper studies four examples of data-intensive STEM projects for supervised undergraduate research experiences (SURE) in Mechanical Engineering, Biomedical science, Quantum Physics, and Cybersecurity. The developed projects include the applications of data science for improving additive manufacturing, automating microscopy image analysis, identifying the quantum optical modes, and detecting network intrusion. The paper aims to provide some guidelines to effectively educate the next generation of STEM undergraduate and graduate students and prepare STEM professionals with interdisciplinary knowledge, skills, and competencies in data science. The paper includes a summary of activities and outcomes from our research and education in the field of data science and machine learning. We will evaluate the student learning outcomes in solving big data interdisciplinary projects to confront the new challenges in a computationally-driven world.
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Teaching Image Processing and Optical Engineering to University Biology Students
ABSTRACT Biophysics is an interdisciplinary pursuit requiring researchers with knowledge and skills in several areas. Optical instruments and computers are fundamental tools in biophysics research to collect and analyze data. We developed a 1-semester Optical Engineering Laboratory course to teach image processing, optical engineering, and research skills to undergraduate students majoring in biology and biochemistry. With the use of development systems on students' laptops and in the cloud, students learned image processing with Python and OpenCV. Each student constructed a microprocessor-based lensless holographic microscope, gaining hands-on experience with optical engineering. The class culminated in original, student-designed research projects. All lectures, hands-on labs, and student research projects were performed both in person and remotely, in response to the COVID-19 pandemic.
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- PAR ID:
- 10465862
- Date Published:
- Journal Name:
- The Biophysicist
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2578-6970
- Page Range / eLocation ID:
- 38 to 57
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.35459/tbp.2022.000240
