Communication, interpreted broadly as writing, presenting, coding, and designing visualizations, is a key part of both a statistician’s and a data scientist’s job description. However, it can be challenging to teach these skills alongside core material. There is only so much class time and so many assignments that an instructor can give feedback on. How can we efficiently teach these skills? Can we make communication strategies more explicit in assignments and activities we are already using? Can we make small changes to pre-existing structures to give students more exposure to a variety of communication styles and products? Here, I aim to address these questions by considering communication activities that span differing audiences, both formal and informal. Just as there is not a single data analysis to be done, there is not a single statistical argument to be made. By helping students compare and contrast how data communicators approach arguments for different audiences and then by guiding them through employing those techniques themselves across a variety of audiences, we can make learning the process of communicating more explicit, and strengthen data-driven arguments.