Cancer is driven by a set of genetic mutations accumulated throughout the lifespan of a person. Every cancer patient has a different set of mutations. Genetic testing characterizes the DNA mutation profile of each patient and allows treatment tailored to individuals, which is the so-called Precision Medicine. Large-scale genetic testing and clinical management are enabled by a perfect marriage of biology and computational methods that provide actionable solutions by mining massive heterogeneous data, such as next-generation sequencing (NGS), patient health information (PHI), and biomedical literature. Computers can learn through the training of such biomedical data to recommend treatment and clinical decision options to doctors. In this talk, I will introduce how data science is employed to facilitate cancer genetic testing and clinical management. From analysis of NGS data, clinical literature mining, to recommendation of drugs, data science constantly play a crucial role in helping cancer biologists and clinicians to characterize tumors and suggest optimal treatment plans.