IE 451 Applied Data Analysis
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Introduction to exploratory data analysis, multivariate regression, semiparametric regression, scatterplot smoothing, linear mixed models, generalized linear models, recursive partitioning, and hidden Markov models through the applications on real data sets using the statistical software R. Applications to consumer choice models, modeling the number of emergency room visits, building e-mail spam filters, detecting fraudulent transactions, and other applications from manufacturing and service systems illustrating big data analytics.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
MATH 260.
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