15.074J Predictive Data Analytics and Statistical Modeling
Designed for students who have some acquaintance with probability and/or statistics and want exposure to a broader range of topics and examples. Begins with a brief review of statistics and regression by addressing advanced topics, such as bootstrap resampling, variable selection, data and regression diagnostics, visualization, and Bayesian and robust methods. Goes on to cover data-mining and machine learning, including classification, logistic regression, and clustering. Culminates with time series analysis and forecasting, design of experiments, analysis of variance, and process control. Students use statistical computing systems based on Excel add-ins and stand-alone packages. Includes case studies involving finance, management science, consulting, risk management, and engineering systems. Term project required.
15.074J will not be offered this semester. It will be available in the Spring semester, and will be instructed by R. E. Welsch.
This class counts for a total of 9 credits. This is a graduate-level class.
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