Computational analyses are playing an increasingly central role in research. Journals, funders, and researchers are calling for published research to include associated data and code. However, many involved in research have not received training in best practices and tools for sharing code and data. This course aims to address this gap in training while also providing those who support researchers with curated best practices guidance and tools. This course is unique compared to other reproducibility courses due to its practical, step-by-step design. It is comprised of hands-on exercises to prepare research code and data for computationally reproducible publication. Although the course starts with some brief introductory information about computational reproducibility, the bulk of the course is guided work with data and code. Participants move through preparing research for reuse, organization, documentation, automation, and submitting their code and data to share. Tools that support reproducibility will be introduced (Code Ocean), but all lessons will be platform agnostic. Level: Intermediate Intended audience: The course is targeted at researchers and research support staff who are involved in the preparation and publication of research materials. Anyone with an interest in reproducible publication is welcome. The course is especially useful for those looking to learn practical steps for improving the computational reproducibility of their own research.