Data Mining for Business Decisions Notes
Subject Overview
The first Management Information Systems group elective, covering data warehousing and business intelligence fundamentals, the data mining process and its various kinds, classification/clustering/association-rule techniques, and prediction and clustering methods using WEKA and R. A 4-credit elective theory paper.
Unit-wise Syllabus
4 units — click WhatsApp below to get the full notes for each
Unit 1: Data warehousing and BI
Strategic information needs, operational vs informational data stores, data warehouse definition/characteristics/role/structure, introduction to business intelligence, OLAP operations, data marts, dimensional modeling and ETL process
Unit 2: Introduction to data mining
Data mining process and functionalities, text/web/pattern/sequence/context-based mining, data visualization, predictive vs descriptive data mining, need for data analytics in business intelligence
Unit 3: Classification and association techniques
Regression and correlation, decision trees, clustering, neural networks, market basket analysis and association rules, genetic algorithms and link analysis, Support Vector Machine, Bayesian classification, k-Nearest Neighbour, case-based reasoning, fuzzy set approach
Unit 4: Prediction and clustering
Linear and multiple regression prediction, cluster analysis data types (interval-scaled, binary, nominal, ordinal, ratio-scaled), partitioning methods (K-Means, K-Medoids), hierarchical agglomerative methods, DBSCAN