This course is designed to train students in best practices in genomic data management and documentation, including data storage requirements, file formats, and HIPAA considerations. Students will learn theories and practices of data visualization, experiencing making figures and graphics (i.e., Circos plots, Manhattan plots, genome browser tracks, haplotype networks, heat maps, etc.) to represent the results of various genomic analyses through various visualization technologies. Students will also practice making analysis pipelines and protocol figures or infographics for publication and educational materials. Additionally, students will learn and refresh their knowledge of how to use Python as a programming language to automate routine data management tasks in genomics research. The main topics of this course include basic concepts of information visualization, best practices for data extraction, transformation and loading process, fundamentals of data preparation and understanding, and an overview of the data analysis pipeline.