SYST/OR 568. Applied Predictive Analytics - Mason Analytics MS

Instructor: Vadim Sokolov
vsokolov@gmu.edu

TA: SeyedOmid HashemiAmiri
ohashem@masonlive.gmu.edu


Syllabus

Course Materials

Introduces predictive analytics with applications in engineering, business, finance,health care, and social economic areas. Topics include time series and cross-sectional data processing, data visualization, correlation, linear and multiple regressions, classification and clustering, time series decomposition, factor models and causal models, predictive modeling performance analysis, and case study. Provides a foundation of basic theory and methodology with applied examples to analyze large engineering, social, and econometric data for predictive decision making. Hands-on experiments with R will be emphasized.

Place and Time

Grades

Grade composition: Grade based entirely on participation in class, homework assignments, in-class midterm and final project.

Textbooks

Links

Deep Learning

Final Project Data

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Notebooks (ISLR labs)

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