Advanced Big Data Analytics and Mining

Catalog description

This course focuses on the advanced analytical techniques required to extract non-trivial, actionable knowledge from massive, complex datasets. While traditional data mining focuses on static, structured data, this course challenges doctoral students to apply machine learning and statistical modeling within distributed environments where data is often unstructured, high-dimensional, and arriving in real-time. Students will explore the full Knowledge Discovery in Databases (KDD) process at scale. The course covers advanced classification, clustering, and association rule mining, specifically adapted for parallel execution. Furthermore, the course dives into specialized areas of mining, including text mining, social network analysis (graph mining), and anomaly detection. By the end of the course, students will be able to evaluate the performance and scalability of analytical models, ensuring they provide robust support for strategic organizational decision-making.