An overview of two open interactive computing environments useful for data science education

OBJECTIVE: To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education. METHODS AND MATERIALS: The features of two online data science platforms are outlined. Both are useful for new data projects and both are integrated with common programming languages used for data analysis. One platform focuses more on data exploration and the other focuses on containerizing, visualization, and sharing code repositories. RESULTS: Both data science platforms are open, free, and allow for collaboration. Both are capable of visual, descriptive, and predictive analytics DISCUSSION: Data science education benefits by having affordable open and collaborative platforms to conduct a variety of data analyses. CONCLUSION: Open collaborative data science platforms are particularly useful for teaching data science skills to clinical and nonclinical informatics students. Commercial data science platforms exist but are cost-prohibitive and generally limited to specific programming languages.