Conference Presentation
Training All Chemical Engineers in Computing and Data Science
November 11, 2019. Originally submitted on November 10, 2019
In this contribution, I will discuss ongoing efforts to retool the sophomore-level “Numerical and Statistical Analysis” course (required) to provide a scaffolding for all students to develop core competencies in computing, applied statistics, and mathematical modeling throughout their undergraduate experience and profession careers. Beginning in Spring 2019, we are transitioning from MATLAB to Python for several reasons including consistency with “Chemical Process Control” (junior, required) and college-wide electives in data science and statistical computing that already use Python. I will also share experiences using Jupyter notebooks and cloud-based computing platforms such as Colaboratory to incorporate active learning into lectures and tutorials and to remove technical barriers for students. Content and assignments have been reorganized to emphasize mastery of foundational skills in preference over content breadth. For example, students are now required to submit hand-written pseudocode for all assignments to reinforce essential programming habits. We now emphasize statistical reasoning over more procedural approaches to data analysis, which required adding lectures on probability theory, a new topic for most students. In ten optional “extension assignments”, students watch/listen to recent TED talks, podcasts, etc. focused on statistics, big data, and computing to build a broad appreciation for concepts introduced in the course. (As an incentive, students earn a homework “drop” by completing these assignments.) By developing fundamental skills, we seek to better position students to continue learning about numerical methods, data analysis, or scientific computing, either through technical electives at Notre Dame or other means as practicing professionals.

As part of the retooling process, lectures are being organized into 5 to 15-minute segments (instruction, activities, etc.) and will be recorded in front of a live classroom in Fall 2019. These 75-minute lecture recording will then be processed into short videos to flip the classroom starting in Spring 2020, thus allowing the two weekly 75-minute meetings to become structured problem-solving sessions.
Active Learning, Multivariate Statistics, Numerical Methods, Python, Undergraduate Education
Suggested Citation
Dowling A. Training All Chemical Engineers in Computing and Data Science. (2019). LAPSE:2019.1133
Author Affiliations
Dowling A: University of Notre Dame [ORCID] [Google Scholar]
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Conference Title
AIChE Annual Meeting
Conference Place
Orlando, FL
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Minor update to slides
Other Meta
Session: "Steal this Course!: Electives and Novel Course Offerings"
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Nov 11, 2019
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[v3] (Minor update to slides)
Nov 11, 2019
[v2] (Revision of Version 1)
Nov 10, 2019
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Nov 10, 2019
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Successor Works
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