It is not realistic to expect that all biomedical and health sciences researchers will acquire the skills needed to apply data science techniques to their work. However, these researchers are all going to have to function in a research environment where the use of data science techniques is increasingly important. Collaborations between data scientists and researchers with domain expertise afford new opportunities. However, a lack of researcher awareness about data science can result in missed opportunities for collaboration, and differences in perspective and language can result in failed collaborations. Seeing no existing curricula that met the specific need identified, we developed a class to bridge that gap - Data Science for Non-Data Scientists. The class explains the possibilities, techniques, and terminology of data science, as well as conveying its limitations such as issues of interpretation, implementation and bias. This presentation will describe the motivation for developing the class, outline the approach taken and the elements of the class, describe the different settings in which it has been taught within our institution, and detail the outcomes of the class.