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Python is rapidly emerging as the programming language of choice for data analysis in the atmosphere and ocean sciences. By consulting online tutorials and help pages, most researchers in this community are able to pick up the basic syntax and programming constructs (e.g. loops, lists and conditionals). This self-taught knowledge is sufficient to get work done, but it often involves spending hours to do things that should take minutes, reinventing a lot of wheels, and a nagging uncertainty at the end of it all regarding the reliability and reproducibility of the results. To help address these issues, this one day Data Carpentry workshop covers a suite of programming best practices that aren’t so easy to glean from a quick Google search.
Participants must already be using Python for their data analysis. They don't need to be highly proficient, but a strong familiarity with Python syntax and basic constructs such as loops, lists and conditionals (i.e. if statements) is required.
Participants should also read this post prior to the workshop, to familiarise themselves with the most commonly used Python libraries in the atmosphere and ocean sciences and how they relate to one another.
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