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README.md

Python4AnalyticalChemistry

Jupyter Notebooks and supporting files for incorporating Python programming into a lower division analytical chemistry course

This repository contains files associated with the University of San Diego's CHEM 220 course, developed and implimented by Dr. Eleanor Gillette, Dr. David De Haan and Dr. Julia Schafer as described in (preprint, manuscript). Please be patient: this repo is currently a work in progress

Here you will find three interations of our modular integration of Python into our course. In each folder, you will find the Jupyter notebooks associated with each Python assignment, as well as supporting information like sample input files and lab handouts. Instructors interested in completed copies of any notebooks here should contact the authors directly. Each folder contains a README file which includes a contents list for the folder. All activties are organized by module, so that you can easily locate assignments related to the topic you are interested in. The modules are as follows:

Intro Working with Jupyter notebooks

Module 1. Simple statistical methods; including t and F tests, quantifying random error

Module 2. Equilbrium: pH & acid-base concepts/calcs, titration curves

Module 3. Linear Regressions

Module 4. Standard Addition & Uncertainty Analysis (error propogation)

Module 5. Method optimization

There are often more that one assignment per module, and different iterations of the course contain different laboratory excersises that alight with each quantitative topic. Laboratory handouts which go along with each assignment are also available, for your information.

These notebooks can be accessed using Binder by clicking the blue badge below

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Jupyter Notebooks and supporting files for incorporating Python programming into a lower division analytical chemistry course

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