Here we will cover the advanced techniques being used by data analysis experts in the life sciences. These methods are required to analyze some of the more complex datasets, such as those found in genomics. We will cover several topics including statistical modeling, multiple test correction, clustering, prediction methods, factor analysis and empirical Bayes methods. We will also elaborate on the use of R markdown to conduct reproducible research. Topics: Statistical modeling Multiple testing Distance and clustering Prediction Factor analysis (batch effects) Empirical Bayes (hierarchical modeling) This class was supported in part by NIH grant R25GM114818. This course is part of a larger set of 8 total courses: * Registration open through 27 April 2015 PH525.1x: Statistics and R for the Life Sciences PH525.2x: Introduction to Linear Models and Matrix Algebra PH525.3x: Advanced Statistics for the Life Sciences PH525.4x: Introduction to Bioconductor PH525.5x: Case study: RNA-seq data analysis PH525.6x: Case study: Variant Discovery and Genotyping PH525.7x: Case study: ChIP-seq data analysis PH525.8x: Case study: DNA methylation data analysis