A model Scientific Computing course for freshman students at liberal arts Colleges

Volume 8, Issue 2 (August 2017), pp. 2–9

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@article{jocse-8-2-1,
  author={Arun K. Sharma},
  title={A model Scientific Computing course for freshman students at liberal arts Colleges},
  journal={The Journal of Computational Science Education},
  year=2017,
  month=aug,
  volume=8,
  issue=2,
  pages={2--9},
  doi={https://doi.org/10.22369/issn.2153-4136/8/2/1}
}

Computing is ubiquitous and perhaps the most common element of our shared experience. However, many students do not seem to recognize the serious applications and implications of computing to the sciences. Wagner College, like many liberal arts colleges, requires a semester of a Computer Science affiliated course to provide students with an exposure to ``technological skills''. Sadly, such courses typically do not delve into high-level computational skills or computational thinking and generally provide instruction in using Microsoft Office\textregistered{} products and rudimentary worldwide web concepts. These courses and approaches were probably valuable a decade ago when computing devices were not quite as prevalent. However, in today's world these courses appear outdated and do not provide relevant skills to the modern undergraduate student. We have created a course called, ``Introduction to Scientific Computing'', to remedy this problem and to provide students with state-of-the-art technological tools. The course provides students with hands-on training on typical work-flows in scientific data analysis and data visualization. Students are trained in the symbolic computing platform, Wolfram Mathematica\textregistered{}, to apply functional programming to develop data analysis and problem solving skills. The course presents computational thinking examples in the framework of various scientific disciplines. This exposure helps students to understand the advantages of technical computing and its direct relevance to their educational goals. The students are also trained to perform molecular visualization using open source software packages to understand secondary and tertiary protein structures, construct molecular animations, and to analyze computer simulation data. These experiences stimulate students to apply these skills across multiple courses and their research endeavors. Student self-assessment data suggests that the course satisfies a unique niche in undergraduate education and enriches the training of future STEM graduates.