We have revised a first-semester undergraduate physical chemistry laboratory course, replacing Microsoft Excel with Python as the course's primary data workup tool. The course consisted of two to three one-hour lectures per week and two three-hour laboratory sessions per week. The course taught chemical and instrumental methods of analysis, including fluorescence spectroscopy, electrochemistry, UV-vis absorption spectroscopy, Fourier-transform infrared spectroscopy, and gas chromatography. In parallel with this hands-on work, students learned data analysis methods such as linear and non-linear least squares fitting, how to establish best-fit parameter confidence intervals, statistical hypothesis-testing tools such as the chi-squared test and the Student's t test, and error propagation. Our goal, in replacing Excel with Python, was to empower students with the tools of modern data science. Surprisingly few students in the undergraduate physical chemistry laboratory course had any prior experience with programming. To reach our goal, we thus needed to address the challenge of gently introducing chemistry students to programming. To avoid students having to install Python on their personal computers, they worked up their data online using Google Colab Jupyter notebooks. Another challenge, it turned out, was importing data from a wide variety of idiosyncratic laboratory instruments into Google Colab. In my talk I will show how to import and workup physical chemistry laboratory data in Google Colab Jupyter notebooks, and I will relate lessons learned in teaching students to work up data in Python in an undergraduate physical chemistry laboratory course.

Revising a one-semester undergraduate physical chemistry laboratory course to analyze data using Jupyter notebooks running in the cloud

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.