Statistical competitions like ASA DataFest and the Women in Data Science (WiDS) Datathon give students valuable experience working with real, challenging data. By participating, students practice important statistics and data science skills including data wrangling, visualization, modeling, communication, and teamwork. However, while advanced students may have already acquired these skills over the course of their undergraduate program, students with less experience often need additional preparation to participate. In this article, we discuss strategies and targeted activities for helping lower-level students feel comfortable and prepared to compete in events like DataFest. We also share how we used these tools to create a low-stakes DataFest preparation course at our institution. Supplementary materials for this article are available online.