Some of them are dimensional modelling (Adamson’s #8 is excellent), some are about ETL (Kimball’s #7 is a jewel). #7 to #11 explain Kimball’s theory in more detail. On Oracle, it’s Hobbs (#54) and on Teradata it’s Coffing’s series (#58 to #63). If you are building a DW on SQL Server platform, Mundy’s Toolkit (#2) is a treasure. For ODS design it’s #17 and the newest model is in #6. Devlin’s, Inmon’s and Imhoff’s classics (#3, #4 and #5 in the list) have broaden my horizon on the basic principles of DW design.
Even data warehouse books as important as Inmon’s DW 2.0 was missed because the title doesn’t contain the word “Warehouse”.įor data modelling my all time favorite is the Kimball’s toolkit (#1 in the list). Same thing with Amazon, see Note 1 below. It is totally understandable why Google’s search result don’t include ETL or Dimensional Modeling, for example. Ralph Kimball, "The Data Warehouse Life Cycle Toolkit", John Wiley & Sons Inc., 1998. Fayyad, Gregory Piatetsky - Shapiro, Padhrai Smyth, and Ramasamy Uthurusamy, "Advances In Knowledge Discovery And Data Mining", The M.I.T Press, 1996. Smith, “Data Warehousing, Data Mining, & OLAP”, Tata McGraw- Hill, 2004. Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", Morgan Kaufmann Publishers, 2002.Īlex Berson and Stephen J. Predictive Modeling - Classification And Prediction-Classification By Decision Tree Induction-Bayesian Classification-Prediction-Clusters Analysis: Categorization Of Major Clustering Methods: Partitioning Methods - Hierarchical Methodsĭata Warehousing Components -Multi Dimensional Data Model- Data Warehouse Architecture-Data Warehouse Implementation-Mapping The Data Warehouse To Multiprocessor Architecture- OLAP.Īpplications of Data Mining-Social Impacts Of Data Mining-Tools-WWW-Mining Text Database-Mining Spatial Databases. Introduction - Relation To Statistics, Databases- Data Mining Functionalities-Steps In Data Mining Process-Architecture Of A Typical Data Mining Systemsĭata Preprocessing and Association Rules-Data Cleaning, Integration, Transformation, Reduction, Discretization Concept Hierarchies-Data Generalization And Summarization
Technical knowhow of the Data Mining principles and techniques for real time applications To perform classification and prediction of data. To know the Architecture of a Data Mining system and Data preprocessing Methods. To understand the principles of Data warehousing and Data Mining.
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