Scientific Computation in Jupyter Notebooks using Python

Volume 15, Issue 2 (November 2024), pp. 24–28

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@article{jocse-15-2-5,
  author={Mark Matlin},
  title={Scientific Computation in Jupyter Notebooks using Python},
  journal={The Journal of Computational Science Education},
  year=2024,
  month=nov,
  volume=15,
  issue=2,
  pages={24--28},
  doi={https://doi.org/10.22369/issn.2153-4136/15/2/5}
}

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. 1 The modules contain multiple features to promote learning, including 'Breakpoint Questions,' recaps of key information, self-reflection prompts, and exercises.