Colleagues, please consider submitting to the upcoming session "Teaching Data Science to Students and Teachers," at the AIChE Annual Meeting. This session is part of Topical Area T4: Bridging the Skills Gap in Chemical Engineering. Abstracts are due on March 31.
Title: Teaching Data Science to Students and Teachers
Description:
There is plenty of enthusiasm about the future of "data science" being an essential expertise for chemical engineers. Analytics and machine learning build on statistics and use it to identify and quantify correlated behaviors, enabling decision-making. That's powerful, but both students and their instructors need to know about more than means and standard deviations. Participants in this session will share their experiences with teaching statistics and beyond, aiming to benefit education and other educators.
Sponsor:
Bridging the Skills Gap in Chemical Engineering
Chair:
Martha A. Grover
School of Chemical & Biomolecular Engineering, NSF/NASA Center for Chemical Evolution, Georgia Institute of Technology, Atlanta, GA
Co-Chair:
Phillip R. Westmoreland
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC
The Topical Area T4: Bridging the Skills Gap in Chemical Engineering will also have the invited session:
Practical Application of Process Data Analytics and Machine Learning (Invited Talks)
Description:
Process data analytics and machine learning have positively improved chemical manufacturing in terms of turning massive amount of data into actionable insights. From open source code sharing to on-line self-paced learning, there are a lot of resources for practitioners to learn new analytics methods. It is, however, more difficult to correctly apply these methods. The purpose of this invited session is to bring together process data analytics and machine learning experts from industry and academia to share their practical experience. Common misconceptions (for examples, more data means higher accuracy? correlation implies causation? analytics methods don’t require domain knowledge?) will be addressed. Best practice (for examples, how to frame the analytics problem? how to select the right analytics method? How to avoid overfitting? How good is good enough?) and the importance of foundational concepts (such as statistics, data pre-processing, programmatic thinking) will be highlighted.
Sponsor:
Bridging the Skills Gap in Chemical Engineering
Chair:
Leo Chiang
Data Services/Chemometrics and AI, Dow Inc., Lake Jackson, TX
Co-Chair:
Phillip R. Westmoreland
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC
|
