Course Overview
Data Mining is one of the most important topics in the data science field. This course discusses theoretical concepts and practical algorithms for both supervised and unsupervised learning techniques. The course provides data mining principles, methods, and applications with a variety of integrated theoretical and practical examples in classification, association analysis, cluster analysis, and anomaly detection. This course also includes applied examples associated with each topic in data mining using R and Python programming languages. 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.