Microbiome research has created large datasets that require advanced statistical analysis combined with biological education to discern meaningful information. Although highly interdisciplinary, microbial ecology education has not adapted at the same pace of data generation. This is exacerbated by lack of consistency and continuity in analysis when compared between studies. Furthermore, the individual background of someone not trained in the proper methods may shape the analysis and skew results. In this session I discuss what mathematical, biological, and technology skills that should be implemented for production of a robust microbial ecologist.
