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
Co-Sponsor(s):
Education (04), Next-Gen Manufacturing (T6A), Computational Molecular Science and Engineering Forum (21), Thermodynamics and Transport Properties (01A), Applications of Data Science to Molecules and Materials (T3), Information Management and Intelligent Systems (10E)
Chair:
Martha Grover
School of Chemical & Biomolecular Engineering, NSF/NASA Center for Chemical Evolution, Georgia Tech, Atlanta, GA
Co-Chair:
Phillip Westmoreland
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC
Abstract Submissions Closed
See more of this Group/Topical: Bridging the Skills Gap in Chemical Engineering