MATH 1126 - PREDICTIVE ANALYTICS 1
Minimum Credits: 3
Maximum Credits: 3
This is an introductory topics course in modern Data Science, including Statistical Learning and Time Series. The topics that will be covered are: Linear Regression (Validation, Resampling Methods, Model Selection and Regularization, Shrinkage, Dimension Reduction, Principal Components), Generalized Linear Models (Logistic and Probit Regression Models, Categorical and Count Response, Measures of Fit), Unsupervised Learning (Decision Trees and Random Forests, Bootstrap, Bagging, Principal Components, Cluster Analysis), Time Series (Random Walk Models, Autoregressive Models, ARCH/GARCH Models, Box-Jenkins Modeling and Forecasting).
Academic Career: Undergraduate
Course Component: Lecture
Grade Component: Letter Grade
Course Requirements: PREQ: MATH 0230 and MATH 1119
Click here for class schedule information.
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