Data Warehousing & Mining
Subject Overview
Data Warehousing & Mining covers data warehouse fundamentals and OLAP/OLTP, building a warehouse with multidimensional modeling, association rule mining and classification, and prediction/clustering techniques and data visualization. A 5-credit core theory paper.
Unit-wise Syllabus
4 units — click WhatsApp below to get the full notes for each
Unit 1: Data warehouse fundamentals
Need for data warehousing, operational vs informational data stores, data warehouse characteristics/role/structure, cost of warehousing, OLAP vs OLTP, OLAP operations
Unit 2: Building a data warehouse
Design/technical/implementation considerations, data preprocessing (summarization, cleaning, transformation), concept hierarchy, multidimensional data model, star/snowflake/fact-constellation schemas, data warehouse architecture and design, OLAP three-tier architecture, cube computation, attribute-oriented induction
Unit 3: Association rule mining and classification
Market basket analysis, Apriori algorithm, mining multilevel association rules, association-to-correlation analysis, constraint-based association mining, classification by decision tree, attribute selection measures
Unit 4: Prediction, clustering and visualization
Prediction techniques, classifier accuracy, cross-validation, bootstrap, boosting, bagging, clustering algorithm classification, selecting the right data mining technique, data visualization