Data communication

There really isn’t any point to doing data science unless it is communicated broadly. Communicating well takes practice, but it will always be well served with reproducible analyses (e.g., using Quarto) and version control (e.g., GitHub).

Think of a data science project as a story that includes data acquisition, data exploration, data visualization, data conclusions, and data communication. The more you can pull the observer into your story, the more effective the data science project will be.

In 16  Reproducible examples we describe how to create a minimal reproducible example that will benefit you when you are looking for help. By parsing your task into the smallest components possible, you can isolate the task that you are trying to accomplish. Reproducible examples are additionally helpful when collaborating on teams.