Training Energy Data Scientists

Traditional power engineering curriculum has been heavily focusing on the engineering aspects of power systems, such as power flow, state estimation, stability and control. Data science has never been a focus in the past. I saw that gap 5 years ago, predicted the shortage of data scientists in the power industry, and left a great place to work to come back to academia. Nowadays, when other business sectors are offering 6-figure salaries to fresh graduates, utilities are having a hard time to compete on the analytics talents. Recently I had the opportunity to collaborate with Prof. David Wenzhong Gao from University of Denver and three other utility executives to put our thoughts in a paper.

Citation

Tao Hong, David Wernzhong Gao, Tom Laing, Dale Kruchten, and Jorge Calzada, "Training energy data scientists: universities and industry need to work together to bridge the talent gap," IEEE Power and Energy Magazine, vol.16, no.3, pp 66-73, May-June 2018. (IEEE Xplore)

Training Energy Data Scientists 

Universities and Industry Need to Work Together to Bridge the Talent Gap

Tao Hong, David Gao, Tom Laing, Dale Kruchten, and Jorge Calzada

Abstract

The workforce crisis is nothing new to the U.S. power industry. It has been a growing concern of both governments and industry organizations since the early 2000s. Meanwhile, the growth of data during the past decade has led to a demand surge for data analytics across all business sectors. The shortage of an electricity workforce and the increasing demand for data analytics present an emerging challenge as well as opportunity for university power engineering programs to bridge the data analytics talent gap. After gathering various perspectives from members of academia, industry, and government, we propose an interdisciplinary and entrepreneurial approach to revising the traditional power engineering curriculum for training the next generation of energy data scientists.