This module provides an introduction to data warehousing concepts, architectures, and processes. It covers the need for data warehousing, different architectural approaches (Kimball and Inmon), ETL processes, data marts, operational data stores, and cloud-based data warehousing solutions.

This module focuses on data modeling approaches for data warehousing, including multidimensional modeling techniques, star schema, snowflake schema, and modern approaches involving NoSQL, big data, and data lakes.

This module covers the fundamentals of data mining processes and methods, focusing on predictive modeling techniques. It includes an overview of the data mining process, various methods, and algorithms used for predictive modeling.

This module explores unsupervised data mining techniques for explanatory modeling, including clustering, segmentation, and market basket analysis. It covers K-means clustering and association rules for pattern discovery.