WDA 306 - Predictive Analytics

1 course unit

The goal of this course is to explore a variety of statistical techniques useful in making predictions about future events. The culmination of the course will lead students to employ predictive analytics to assist in decision making and transforming statistics into useful prescriptive analytics. The course will cover Data Analysis (simple visualization, graphing, etc.) and Model Building. Statistical models will include Simple Linear & Multiple Linear Regression Analysis consisting of an examination of dummy variables, Non-Linear Regression, residual analysis, multicollinearity, and forecasting. Additional models including Logit & Probit Regression, Poisson Regression, Ordinal Regression, Survival Analysis (time to event and hazard rate), Data Segmentation (k-means clustering), and Time Series Analysis will also be utilized as predictive techniques. Emerging concepts of machine learning and cognitive analytics will be explored. The emerging topics of Autoregressive Models (AR & ARMA) and Regression Trees will also be explored.