Peer reviewed
Direct linkERIC Number: EJ1365745
Record Type: Journal
Publication Date: 2022
Pages: 5
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0009-2479
EISSN: EISSN-2165-6428
Available Date: N/A
Teaching Process Data Analytics and Machine Learning at MIT
Chemical Engineering Education, v56 n4 p226-230 Fall 2022
This article describes experiences with teaching process data analytics and machine learning, including in: (1) a joint undergraduate/graduate course for students in chemical and mechanical engineering and engineering management; and (2) an undergraduate chemical engineering concentration in process data analytics. The article also describes challenges in teaching data science to chemical engineers, and strategies for overcoming those challenges.
Descriptors: Teaching Methods, Graduate Students, Undergraduate Students, Chemical Engineering, Engineering Education, Data Analysis, Barriers, Statistics Education, Course Descriptions, Course Evaluation, Student Attitudes, Teaching Experience, Teacher Evaluation, College Faculty
Chemical Engineering Education, Chemical Engineering Division of ASEE. 675 Wolf Ledges Parkway Suite 2459, Akron, OH 44309. Tel: 352-682-2622; e-mail: cee@che.ufl.edu; Web site: https://journals.flvc.org/cee/
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Massachusetts (Cambridge)
Grant or Contract Numbers: N/A
Author Affiliations: N/A
