Data Warehousing and Data Mining

Course Code: MIT405

Course Title: Data warehousing and Data mining (4 Credits)




Course Contents


Unit-1: Data Warehouse Fundamentals: Introduction, OLTP Systems, Characteristics & Functions of Data Warehouses, Advantages and Applications of Data Warehouse, Top- Down and Bottom-Up Development Methodology, Tools for Data warehouse development, Data Warehouse Types.


Unit-2: Planning and Requirements: Key Issues in Planning a Data Warehouse, Planning and Project Management in Data Warehouse Construction, Data Warehouse Project.


Unit-3: Data Warehouse Architecture: Components of Data Warehouse Architecture, Technical Architectures, Tool Selection, Federated Data Warehouse Architecture.


Unit-4: Dimensional Modeling: E-R Modeling, Dimensional Modeling, E-R Modeling VS Dimensional Modeling, Data Warehouse Schemas, Snowflake Schema, Fact Constellation Schema.


Unit-5: Extract, Transform and Load: ETL Overview, ETL Requirements and Steps, Data Transformation, Data Loading, ETL Tools.


Unit-6: Data Warehouse & OLAP: What is OLAP?, Multidimensional Data, OLAP Architectures ,Data Warehouse and OLAP, Hypercube & Multicubes.


Unit-7: Metadata Management in Data Warehouse: Introduction to Metadata, Categorizing Metadata, Metadata management in practice, Tools for Metadata management.


Unit-8: Introduction to Data Mining:  Meaning and Working of Data Mining, Data, Information and Knowledge, Relation between Data Warehousing and Data Mining, Data Mining and Knowledge Discovery Process, Data Mining and Online Analytical Processing (OLAP), Data Mining and Statistics, Data Mining Technologies, Data Mining Software.


Unit-9: Business Intelligence:  Business Intelligence (BI), Business Intelligence Tools, Business Intelligence Infrastructure, Business Intelligence Applications, BI versus Data, Warehouse, BI versus Data Mining, Future of BI.


Unit-10: Data Preprocessing: Introduction, Data Preprocessing Overview, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation.


Unit-11: Data Mining Techniques – An Overview: Data mining: Various Definitions, Data Mining Versus Database Management System (DBMS), Data Mining Techniques.


Unit-12: Clustering: Clustering, Cluster Analysis, Clustering Methods, Clustering and Segmentation Software, Evaluating Clusters.    


Unit-13: Web Mining: Introduction, Terminologies, Categories of Web Mining, Applications of Web Mining, Agent Based and Data Base Approaches, Web Mining Software.


Unit-14: Applications of Data Mining: Business Applications Using Data Mining, Scientific Applications Using Data Mining, Other Applications.