Course Overview
This course covers a comprehensive introduction to data mining techniques and applications with a variety of integrated theoretical and practical examples in data exploration, preprocessing, modeling, classification, association analysis, cluster analysis, and anomaly detection. This course also includes a wide variety of applied problems associated with each topic in data mining using both Python and R code examples. Students will also conduct a broad and hands-on final project from the covered data mining principles, present, and report the technical results. Prerequisites: ADS 500A and ADS 500B. Both ADS 500A and ADS 500B can be waived by the Academic Director based on an evaluation of the student’s professional background and academic history.
