MCA-402 Data Warehousing and Data Mining
MCA-402 Data Warehousing and Data Mining !
Introduction: The Evolution Of Data Warehousing (The Historical Context), The Data Warehouse – A Brief History, Characteristics, Operational Database Systems and Data Warehouse(OLTP & OLAP),Today’s Development Environment, Data Marts,Metadata.
Multidimensional Data Models: Types of Data and Their Uses,from Tables and Spreadsheetsto Data Cubes,Identifying Facts and Dimensions,Designing Fact Tables, Designing Dimension Table,Data Warehouse Schemas, OLAP Operations.
Principles of Data Warehousing(Architecture and Design Techniques):System Processes, Data WareHousing Components,Architecture for a warehouse, Three-tier Data Warehouse Architecture, Steps for the design and construction of Data Warehouses, Conceptual Data Architecture, Logical Architectures, Design Techniques.
Unlocking the Data Asset for End Users (The Use of Business Information): Designing, Business Information Warehouses, Populating Business information Warehouses, User Access to Information, Information Data in Context.
Implementation: Methods for the implementation of Data Warehouse Systems.
Tools for Data Warehousing.
Data Mining: Introduction: Motivation, Importance,Knowledge Discovery Process, KDD and Data Mining, Data Mining vs. Query Tools, Kind of Data data mining, kind of data, Functionalities, interesting patterns, Classification of data mining systems, Major issues,from Data warehousing to data Mining.
Data Preparation:Preprocess, data Cleaning, Data Integration and Transformation, Data Reduction.
Data Warehouse and OLAP Technology for Data Mining: data warehouse, operational database systems and data warehouses, Architecture, Implementation, development of data cube technology, data warehousing to data mining, Data warehouse usage.
Data Preparation: Preprocess Data cleaning, Data integration and transformation, Data reduction
Data Mining Primitives, Languages, and System Architectures, graphical user interfaces.
Concept Description: An Overview of Descriptive Data Mining, Predictive Mining, Methods for Concept Description.
Mining Association Rules:Association Rule Mining,Market Basket Analysis,Types of Association Rules,Methods for Mining Association Rules in Transaction Databases, Relational databases and Data Warehouses.
Classification and Prediction: Methods for Data Classification and Prediction.
Applications of Data Mining.
Tools for Data Mining.