The availability of voluminous, high resolution data in both the spatial and temporal dimensions, coupled with increasingly fast, distributed computational resources offers enormous opportunities for tackling complex engineering and science challenges in urban settings. These data can also play an important role in interdisciplinary problem solving and have increasingly high value to multiple communities of scientists and engineers. However, research in the optimal instruction mechanisms to develop data science skills is still emerging. This is particularly true for engineering graduate students, who are a highly selected, technologically sophisticated population with the ability to quickly master material.
The project piloted, tested, and compared modes of data science instruction. The testbed project provides critical new information to inform the development of new learning platforms designed to cultivate robust computational, statistical, and data reasoning skills in engineering graduate students.
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