In cancer research, the associations between genomic, exposure, and outcomes have advanced the scientific cancer community's ability to formulate and test innovative therapies that reduce and eradicate the impact of cancer. The nuanced and unique research questions in modern cancer research have resulted in the development of innovative quantitative approaches to evaluate data, including increased wide-scale collaborations using common data sources. Consequently, statistics and computing have become the sine qua non of current cancer biomedical research. As novel statistical methods and computational tools are developed to address the biomedicine data deluge, so has the urgent need to train biomedical researchers on foundational principles of statistical design that will facilitate a clear understanding of concepts underlying modern analysis approaches. In this talk, we will discuss our recent efforts to build such a statistics curriculum for post-doctoral biomedical scientists at a free-standing cancer center. We will share our experiences in the development of the curriculum and provide insights from the students and their laboratory lead's perspective.