Objective is to impart knowledge on the emerging trends in data warehousing and data mining, enabling students understand and appreciate the importance of making meaningful use of large volume of data in decision-making. Participants will have hands-on experience with the data mining software, SPSS-Clementine.
Course Contents:
Data Warehousing (7.5 sessions): Module-1 (2 sessions): Introduction: Introduction, Data Warehousing & Data Mining as a subject, Motivation behind data mining; Data Warehouse: What is a data warehouse?, Definition, Multidimensional data model; Data Warehouse vs Database: Difference between Operational Database Systems and Data Warehouses, Basic elements of the data warehouse, Commercial Importance of data warehouse; Data Warehousing Architecture: DW Architecture, Enterprise Warehouse, Data Marts, Virtual Data Warehouse, Metadata Multidimensional data model: Multidimensional Representation of Data, Dimension Modeling & Hierarchy, Lattice of Cuboids, Summary Measures; OLAP Operations: Slicing & Dicing, Drill-up & Drill-down, Drill within & Drill Across, Pivot Module-2 (2 sessions): Warehouse Schema: Normalization vs Dimensional Modeling, Star Schema, Snowflake Schema and Fact Constellation; OLAP Engine: Specialized SQL Server, ROLAP, MOLAP and HOLAP; Data Warehouse Implementation: Efficient Computation of Data Cubes, Indexing OLAP Data, Backend Processes Module-3 (3.5 sessions): DW in Retail Sales: Retail Schema in action, Retail Schema Extensibility, Promotion Dimension, Degenerate Transaction Number Dimension, Market Basket analysis; DW in Inventory Management: Inventory Periodic snapshot, Inventory Transactions, Inventory Accumulating Snapshot; DW for Procurement: Slowly Changing Dimensions (SCD), How to handle with SCD; DW for CRM: Rapidly Changing Dimensions; DW in Banking, DW in Insurance Data Mining (7.5 sessions): Module-1 (2 sessions): Data Mining:What is data mining, KDD vs Data Mining, DBMS vs DM, DM Techniques, DM Application Areas; Association Rules: Introduction, What is an association rule?, Support, Confidence, and Lift paradigm, Generalized Association Rule (Numeric, categoric, temporal, spatial etc), Case study on use of association rules in Market Basket Analysis and Inventory Management, Methods to discover association rules-Discussion on DM algorithms (A priori, DIC, FP Tree Growth); Module-2 (3 sessions): Clustering: Definitions of clustering & classification, Algorithms for clustering, Case Study on clustering; Classification: Decision Trees based on information entropy, Case Study on building Decision Tree, Classification using Neural Network, Case study of Data Mining using Neural Network; Module-3 (2.5 sessions): Other Topics: Sequence Mining, Feature Selection, Factor Analysis, Data Mining in soft computing paradigm-Genetic Algorithms, Fuzzy and Neuro Fuzzy Approaches, Rough Set, Support Vector Machine, Web Mining, Text Mining Suggested Books: (i) J. Hahn and Micheline Kamber - Data Mining: Concepts and Techniques (Morgan Kaufmann) (ii) R.Kimball - DataWarehouse Toolkit (J.Wiley) (iii) O.P.Rud – Data Mining: Modeling Data for Marketing, Risk and CRM (Wiley) (iv)A.K.Pujari -Data mining (University Press) Evaluation Components:
Created By: Lingaraj Pattanaik on 12/08/2009 at 11:50 AM Category: ExPGP-III Doctype: Document