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This course provides an introduction to different data mining techniques, with emphasis on practical applications of them by using software such as R. These techniques are particularly useful for the analysis of large data sets, as can arise in clinical, survey, psychometric, genomic and marketing research. The course begins by introducing several examples of supervised and unsupervised learning. Beginning with well-established techniques, we discuss methods such as discriminant analysis, support vector machines, and clustering techniques. The second half of class is devoted to Big Data or high dimensional data analysis using dimension reduction and variable selection techniques. In addition to reading papers demonstrating the use of these techniques in behavioral research, we will provide step-by-step tutori.