IN 303: Data Mining and Data Warehousing

Explores data warehouse architectures, ETL (extract, transform, load) processes, and data mining algorithms. Covers star and snowflake schemas, OLAP cubes, association rule mining, clustering techniques, classification models, and anomaly detection in massive datasets.

• Data warehouse architecture: OLAP cube multidimensional modeling, slice-and-dice, and drill-down operations in IN 303
• ETL pipeline engineering: staging, data transformation rules, incremental loading, and data quality checks for Data Mining and Data Warehousing
• Association rule mining: Apriori algorithm, support, confidence, and lift calculations in IN 303
• Cluster analysis and anomaly detection: k-means clustering, hierarchical clustering, and DBSCAN at Purdue Global