This course teaches principles and methodologies of learning for data analytics which is a core technology to extract useful information from data. Various data analytic methodologies used in data mining, machine learning and deep learning are summarized, and background principles and theories governing these methodologies are studied. Topics including likelihood principle, estimation of probability density, non-parametric regression, classification and decision theory, shrinkage and regularization, sparse learning, decision trees and ensembles, SVM and deep learning are covered and related theories including concentration of measure and minimax optimality are treated.