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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Multidisciplinary Research and Teaching by Means of Employing FTIR Spectroscopic Imaging System and Characterization Techniques
This paper focuses on discussing the efforts made to engage students in multi-disciplinary research and integrate teaching and research in the areas of FTIR Spectro- microscopy and image processing and analysis. The author (PI) and co-PIs acquired a Fourier Transform Infrared (FTIR) Spectroscopic Imaging equipment through the National Science Foundation- Major Research Instrumentation (NSF- MRI) grant (#1827134). This project aims to use the equipment to conduct undergraduate and graduate research projects and teach undergraduate and graduate classes. The NSF awarded the California State University Chico (CSU Chico) $175,305 to acquire an FTIR spectrometer and microscope, which are important tools for chemical characterization of samples with infrared active molecules. FTIR Spectroscopic Imaging System especially provides accurate chemical images that reveal the variations in images’ pixels which are mappings of constituent materials of samples rather than a single visible image with slight variations. By employing this equipment in research and the Image Processing course, students can learn how to collect, process and analyze the imaging data of samples and the corresponding spectral data. The students not only will learn how to process a single chemical image, but also will work with the data cubes to consider the pixel intensities along the IR spectrum, experience working with big data, hone the skills to design experiments, analyze larger data sets, develop pre- and post-image processing techniques, and apply and refine math and programming skills. Image processing course conventionally is based on math, digital signal and systems, and requires programming skills such as Matlab, C++, and Python. along with the mentioned knowledge. Additionally, the research conducted by this equipment promotes collaboration between engineering major students and science major students. In this paper, the author will explain how collecting data through running experiments with the FTIR Spectroscopic Imaging equipment helps students visualize theory and relate it to real world problems. This paper also discusses the results of engaging undergraduate students from various majors in research. Moreover, it will discuss some of the projects that were conducted by undergraduate students and their learning outcomes. The objective of the research projects was material characterization towards contribution to health by employing FTIR Spectroscopic Imaging System.
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- Award ID(s):
- 1827134
- PAR ID:
- 10210387
- Date Published:
- Journal Name:
- 2020 ASEE Virtual Annual Conference Content Access Proceedings
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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