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Commentary

Data Science in the Chemical Engineering Curriculum

Department of Chemical Engineering, Ryerson University, Toronto, ON M5B 2K3, Canada
Processes 2019, 7(11), 830; https://doi.org/10.3390/pr7110830
Received: 30 August 2019 / Revised: 30 October 2019 / Accepted: 4 November 2019 / Published: 8 November 2019
(This article belongs to the Special Issue Process Systems Engineering à la Canada)
With the increasing availability of large amounts of data, methods that fall under the term data science are becoming important assets for chemical engineers to use. Methods, broadly speaking, are needed to carry out three tasks, namely data management, statistical and machine learning and data visualization. While claims have been made that data science is essentially statistics, consideration of the three tasks previously mentioned make it clear that it is really broader than just statistics alone and furthermore, statistical methods from a data-poor era are likely insufficient. While there have been many successful applications of data science methodologies, there are still many challenges that must be addressed. For example, just because a dataset is large, does not necessarily mean it is meaningful or information rich. From an organizational point of view, a lack of domain knowledge and a lack of a trained workforce among other issues are cited as barriers for the successful implementation of data science within an organization. Many of the methodologies employed in data science are familiar to chemical engineers; however, it is generally the case that not all the methods required to carry out data science projects are covered in an undergraduate chemical engineering program. One option to address this is to adjust the curriculum by modifying existing courses and introducing electives. Other examples include the introduction of a data science minor or a postgraduate certificate or a Master’s program in data science. View Full-Text
Keywords: data science; big data; statistics; chemical engineering curriculum data science; big data; statistics; chemical engineering curriculum
MDPI and ACS Style

Duever, T.A. Data Science in the Chemical Engineering Curriculum. Processes 2019, 7, 830. https://doi.org/10.3390/pr7110830

AMA Style

Duever TA. Data Science in the Chemical Engineering Curriculum. Processes. 2019; 7(11):830. https://doi.org/10.3390/pr7110830

Chicago/Turabian Style

Duever, Thomas A. 2019. "Data Science in the Chemical Engineering Curriculum" Processes 7, no. 11: 830. https://doi.org/10.3390/pr7110830

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