ECON 475: Predictive Analytics

Course Description

An introduction to the basic methods used in predictive analytics. The supervised learning methods covered in class may include decision trees, the native Bayes model, nearest neighbor methods, neural networks, Markov chains, multiple and logistic regression, and others. The course will provide students the ability to identify when to use each method, how to execute the methods learned, and how to interpret and apply the results. The methods require students to employ computer software when applying the course material to real world applications. Prerequisites:  (IS 301 or IS 318 or CS 221 or CS 262 or instructor consent) AND (ECON 210 or Math 301, with a grade of C or better) AND EITHER (Admitted COB majors or Econ COLS majors and minors) OR (30 credits with no more than 21 credits of 300/400 level College of Business Courses and 2.5 GPA) Special fees may apply.