Data Mining for Business Decisions
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