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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
Introduction to Data Warehouse
Introduction to Data Warehouse
A data warehouse is mainly a data management system that’s designed to enable and support business intelligence (BI) activities, particularly analytics. Data warehouses are alleged to perform queries, cleaning, manipulating, transforming, and analyzing the data and they also contain large amounts of historical data.
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2 Comments
very good
Useful Learning