Prereq: 15.060, 15.075, or permission of instructor. Introduction to data mining, data science, and machine learning, methods that assist in recognizing patterns, developing models and predictive analytics, and making intelligent use of massive amounts of data collected via the internet, e-commerce, electronic banking, pointof-sale devices, bar-code readers, medical databases, and other sources. Topics include logistic regression, association rules, treestructured classification and regression, cluster analysis, discriminant analysis, and neural network methods. Presents examples of successful applications in credit ratings, fraud detection, marketing, customer relationship management, investments, and synthetic clinical trials. Introduces data-mining software focusing on R. Term project required. Meets with 15.062 when offered concurrently. Expectations and evaluation criteria differ for students taking graduate version; consult syllabus or instructor for specific details.