15.074[J] Predictive Data Analytics and Statistical Modeling

Class Info

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.

This class has 6.431, and 15.060 as prerequisites.

15.074[J] will not be offered this semester. It will be instructed by R. E. Welsch.

This class counts for a total of 9 credits. This is a graduate-level class.

In the Spring 2013 Subject Evaluations, 15.074[J] was rated 6.0 out of 7.0. You can find more information at the Statistics at MIT - Classes site.

MIT 15.074[J] Predictive Data Analytics and Statistical Modeling Related Textbooks
MIT 15.074[J] Predictive Data Analytics and Statistical Modeling On The Web
Statistics at MIT - Classes
statistics models methods catalog regression markov monte carlo kalman bayesian

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