High throughput sequencing technologies enable individualized characterization of the microbiome composition and functions. The human microbiome, defined as community of microbes in and on the human body, impacts human health and risk of disease by dynamically interacting with host diet, genetics, metabolism and environment. The resulting data can potentially be used for personalized diagnostic assessment, risk stratification, disease prevention and treatment. Microbiome has become one of the most active areas of research in biomedical sciences. New computational and statistical methods are being developed to understand the function of microbial communities. In this short course, we will give detailed presentations on the statistical and computational methods for measuring various important features of the microbiome based on 16S rRNA and shotgun metagenomic sequencing data, and how these features are used as an outcome of an intervention, as a mediator of a treatment and as a covariate to be controlled for when studying disease/exposure associations. The statistics underlying some of the most popular tools in microbiome data analysis will be presented, including bioBakery tools for meta'omic profiling and tools for microbial community profiling (MetaPhlAn, HUMAnN, Data2, DEMIC, etc), together with advanced methods for compositional data analysis and kernel-based association analysis.