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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
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Decision Tree
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Naive Bayes Part #2