Course information

Data warehousing overview and Concepts: Need for data warehousing, basic elements of data warehousing, Trends in data warehousing, Architecture and Infrastructure: Architectural components, Infrastructure and metadata, Data Design and Data Representation: Principles of dimensional modelling, Dimensional modelling advanced topics, data extraction, transformation and loading, data quality. Information Access and Mode of Delivery: Matching information to classes of users, OLAP in data warehouse, Data warehousing and the web. Implementation And Maintenance: Physical design process, data warehouse deployment, growth and maintenance. Data mining: Classification of data mining techniques, Discovery and analysis of patterns, trends, and deviations. Data mining models: decision trees, genetic algorithms, neural nets, etc. Data pre-processing, Data mining primitives, languages and systems, Data mining Algorithms. Descriptive data mining: characterization and comparison, Association analysis, Classification and prediction, Cluster analysis: Clustering, Enabling data mining through data warehouse. Knowledge Discovery: KDD Process, Web Mining: Web Content Mining, Web Structure Mining, Web Usage mining.  Data marts, Multidimensional databases, mining complex types of data, Applications and trends in data mining.