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What is Workflow Anyway?
A software development term that is often used and just as often misunderstood.
A data science workflow is a structured, repeatable process for turning raw data into actionable insights, covering everything from data collection to communication. It ensures your work is organized, reproducible, and scalable. It is how you approach a defined task and preferably you are going to do it the same way every time. Although you may want to tweak and improve your way of doing things as circumstances dictate.
If you’re going to be writing a lot of R code, or otherwise doing a lot of data analysis, it’s worth investing some time to plan and edit your basic workflow. This is because it tends to pay great dividends in the long run. It doesn’t just increase the proportion of your time spent writing code, but because you see the results more quickly, it makes the process of writing code more enjoyable, and helps your skills improve more quickly.
Think of it as a way to get immediate feedback on your deliberate practice.
Are you sold on the idea of at least having a rough outline of how you perform routine data analysis tasks? Great! Let’s gooo…. I like to break nine well-known steps down into groups of three. Three phases of three, if that helps.
