Scientific Computation in Jupyter Notebooks using Python

Computation is a significant part of the work done by many practicing scientists, yet it is not universally taught from a scientific perspective in undergraduate science departments.In response to the need to provide training in scientific computation to our students, we developed a suite of self-paced "modules" in the form of Jupyter notebooks using Python.These modules introduce the basics of Python programming and present a wide variety of scientific applications of computing, ranging from numerical integration and differentiation to Fourier analysis, Monte Carlo methods, parallel processing, and machine learning. 1The modules contain multiple features to promote learning, including "Breakpoint Questions," recaps of key information, self-reflection prompts, and exercises.