Applied Data Mining

Catalog description

Focuses on the practical application of data mining techniques for analyzing real-world datasets and supporting data-driven decision making. Students will learn how to implement and apply common data mining methods using modern programming tools and data analytics libraries. The course emphasizes hands-on experience with the full data analysis workflow, including data cleaning and preprocessing, exploratory data analysis, feature engineering, model implementation, and performance evaluation. Students will apply a variety of techniques such as classification, clustering, anomaly detection, predictive modeling, and introductory deep learning methods to solve practical analytical problems. The course prepares students to use data mining techniques effectively in real-world domains such as business analytics, information systems, and data-driven decision support. Prerequisites: INFO 501 and INFO 502, either or both may be taken concurrently. Typically offered at least once every academic year.