I posted the notes for a lecture on communication in data science that might be interesting/helpful. This lecture provides four general principles1 for communication:

  1. adapt to your audience
  2. maximize the signal to noise ratio
  3. use effective redundancy
  4. consider the trade-offs

and discusses some examples of how they apply to various examples in data science (visualization, code structure and literate programming).

Communications skills are important at all levels of technical pursuits from releasing a software package to conducting research, however, they are under emphasized in STEM education. These notes are from an undergraduate Introduction to Data Science course I taught last semester and are my best attempt to incorporate communication into the curriculum. Any feedback that might improve this lecture (or help me become a better communicator) is welcome!

  1. The first three of these are from Trees, Maps and Theorems