Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
It has been recognized that jobs across different domains is becoming more data driven, and many aspects of the economy, society, and daily life depend more and more on data. Undergraduate education offers a critical link in providing more data science and engineering (DSE) exposure to students and expanding the supply of DSE talent. The National Academies have identified that effective DSE education requires both appropriate classwork and hands-on experience with real data and real applications. Currently significant progress has been made in classwork, while progress in hands-on research experience has been lacking. To fill this gap, we have proposedmore »to create data-enabled engineering project (DEEP) modules based on real data and applications, which is currently funded by the National Science Foundation (NSF) under the Improving Undergraduate STEM Education (IUSE) program. To achieve project goal, we have developed two internet-of-things (IoT) enabled laboratory engineering testbeds (LETs) and generated real data under various application scenarios. In addition, we have designed and developed several sample DEEP modules in interactive Jupyter Notebook using the generated data. These sample DEEP modules will also be ported to other interactive DSE learning environments, including Matlab Live Script and R Markdown, for wide and easy adoption. Finally, we have conducted metacognitive awareness gain (MAG) assessments to establish a baseline for assessing the effectiveness of DEEP modules in enhancing students’ reflection and metacognition. The DEEP modules that are currently being developed target students in Chemical Engineering, Electrical Engineering, Computer Science, and MS program in Data Science at xxx University. The modules will be deployed in the Spring of 2021, and we expect to have immediate impact to the targeted classes and students. We also anticipate that the DEEP modules can be adopted without modification to other disciplines in Engineering such as Mechanical, Industrial and Aerospace Engineering. They can also be easily extended to other disciplines in other colleges such as Liberal Arts by incorporating real data and applications from the respective disciplines. In this work, we will share our ideas, the rationale behind the proposed approach, the planned tasks for the project, the demonstration of modules developed, and potential dissemination venues.« less
Free, publicly-accessible full text available July 26, 2022
The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requiresmore »both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education.« less
The democratization of data is transforming our world. Together with the advances in computer and engineering technology, these advancements drive the rapid change in the landscape of jobs and work. There are many reports indicating that industry finds itself constrained by today’s relatively small supply of well-trained data science talent, and hiring demand for data scientists has begun to increase rapidly; some projections forecast that approximately 2.7 million new data science positions will be available by 2020. Unsurprisingly, the data science and engineering (DSE) programs across the nation have grown significantly in the past a few years. DSE education requiresmore »both appropriate classwork and hands-on experience with real data and real applications. While significant progress has been made in the former, one key aspect that yet to be addressed is hands-on experience incorporating real-world applications. In this work, we will review the efforts that explore real data and application based data science education.« less
Full Text Available