Overview
In this guide, weβll discuss best practices for organizing projects and ensuring reproducibility in data analysis. Weβll cover topics such as project structure, workflow management, environment configuration, what tools to use to manage them.
The intention behind projects is simple: to make it easier for you to understand, construct, manage, and run your analyses over time. In data science, we often find ourselves in the position of needing to run several different analyses, leveraging different data, code, tools, and conditions. As we add more code to the mix, it can become harder for our squishy human brains to keep track of all of the moving parts. Part of the beauty of programming is that it allows us to automate and abstract the complexity of what we ask the machine to do for us, but that also means we need to be intentional about how we automate and abstract. By making these decisions explicitly and early, we can save ourselves a lot of time and frustration down the line.
Project Structure
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Deep Dives
Click on the cards below to explore specific topics in more depth, with practical examples and recommendations for tools and practices to help you build projects that are robust, reproducible, and ready for collaboration.
π Staging Your Data with stagecoach
All across the Frontier, a familiar rumble can be heard and felt through the land. The stagecoach is arrivingβ¦