Introduction To Data Mining

Catalog description

This course introduces the data mining pipeline for extracting meaningful patterns and insights from large datasets. Students learn the end-to-end process of data mining, including data preprocessing, exploratory data analysis (EDA), model development, and evaluation. The course covers both supervised and unsupervised learning approaches commonly used in data mining. In the supervised learning component, students study traditional classification and predictive modeling techniques for discovering patterns and making predictions from labeled data. In the unsupervised learning component, the course introduces clustering methods and anomaly detection techniques for identifying hidden structures and unusual patterns in unlabeled data. The course also provides an overview of modern AI-oriented approaches that increasingly influence data mining practice. In addition, students learn how to interpret and evaluate mining results, perform post-processing to refine discovered knowledge, and apply techniques for mining unstructured or semi-structured data such as text. Through practical examples and applications, students gain experience applying data mining techniques to real-world analytical problems. Prerequisites: CS 375 and MATH 304 and either MATH 327 or MATH 448; or equivalents. Term and frequency of offering varies.