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Course Overview
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Index
- Introduction to Data Warehouse
- Meta Data
- Data Mart
- Data Warehouse Architecture
- How to draw star schema
- Olap Operations
- OLAP VS OLTP
- K-Mean
- Introduction to Data Mining
- Naive Bayes Part #1
- Apriori algorithm
- Agglomerative Clustering
- Knowledge Discovery in Database (KDD)
- Extract Transform and Load (ETL)
- FP-Tree
- Decision Tree
- K -Medoids
- Naive Bayes Part #2
- Agglomerative Adjacency Matrix
- DBSCAN
- Design Strategy of Data warehouse and Data Mining
- Types of Attribute for Data Exploration
- K mean clustering Sum – Type 2 where K=2
- Data Preprocessing Part #1
- Data Preprocessing Part #2
- Data Visualization Part #1
- Data Visualization Part #2
- Schema Design – Dimension Modeling Part #1
- Schema Design – Dimension Modeling Part #2
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Data Warehouse and Data Mining NOTES and Importance
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Data Warehousing and Mining Viva Question
Meta Data
Meta Data
Metadata is simply defined as data about data. The data that is used to represent other data is known as metadata. For example, the index of a book serves as metadata for the contents in the book. Metadata in a data warehouse defines the warehouse objects. Metadata acts as a directory.
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