15.0741 Predictive Data Analytics and Statistical Modeling
Provides a brief review of statistics and regression drawn from advanced topics, such as bootstrap resampling, variable selection, data and regression diagnostics, visualization, and Bayesian and robust methods. Covers 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. Uses statistical computing systems based on application add-ins and stand-alone packages. Case studies involve finance, management science, consulting, risk management, and engineering systems. Term project required.
This class has 6.041B as a prerequisite.
15.0741 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.
You can find more information at the http://www.google.com/search?&q=MIT+%2B+15.0741&btnG=Google+Search&inurl=https site.
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