Scientific computing projects and all research require well-designed software and applications of mathematics or statistics. See subpages for information about Python, R, Matlab, Unix (Bash Shell), Version Control, and more.
Please visit deisdata.github.io for asynchronous materials for on Python, Unix, Version Control, and more.
Research best practices are essential to protecting data and analysis and producing replicable science.
Computing workflows need to follow the same practices as lab projects and notebooks, with organized data, documented steps, and the project structured for reproducibility. The links and summary below present a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill.
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