| Lecture No. | Description | Lecture By |
|---|---|---|
| Lecture 1 | The Compelling Need for data warehousing | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 2 | Escalating Need for strategic information, Failures of Past decision-support systems | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 3 | Operational versus decision-support systems | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 4 | Data warehousing – the only viable solution, Data warehouse defined | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 5 | Data warehouse – The building Blocks: Defining Features | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 6 | data warehouses and data marts | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 7 | overview of the components | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 8 | metadata in the data warehouse | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 9 | Defining the business requirements: Dimensional analysis | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 10 | OLAP operations | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 11 | Drilldown and roll-up | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 12 | Slice-and-dice or rotation | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 13 | Principles of dimensional modelling | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 14 | The STAR schema | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 15 | STAR Schema Keys | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 16 | Advantages of the STAR Schema | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 17 | Dimensional Modeling | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 18 | Updates to the Dimension tables, Miscellaneous dimensions | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 19 | the snowflake schema, aggregate fact tables | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 20 | families of STARS | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 21 | Design & Construction of Data warehouse : Framework , Architecture | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 22 | Type of OLAP Servers : ROLAP , MOLAP | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 23 | Data warehouse implementation tolls | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 24 | Data warehouse implementation techniques | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 25 | Data Mining Basics: What is Data Mining | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 26 | Knowledge discovery process (KDD) | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 27 | What kind of patterns can be mined | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 28 | OLAP versus data mining | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 29 | data mining and the data warehouse | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 30 | Data mining functionalities | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 31 | classification Systems | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 32 | Data processing : Cleaning | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 33 | Integration & transformation | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 34 | Reduction | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 35 | Data Mining primitives | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 36 | What defines a Data Mining Task | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 37 | Data Mining Query language (DMQL) | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 38 | Cluster Analysis : Partitioning | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 39 | Hierarchical Density | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 40 | Grid & Model based methods | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 41 | Major Data Mining Techniques | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 42 | Cluster detection | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 43 | Decision trees, memory-based reasoning | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 44 | link analysis, neural networks, Genetic algorithms | Lecture by , Lecture by , Lecture by , Lecture by |
| Lecture 45 | moving into data mining, Data Mining Applications, Benefits of data mining & applications | Lecture by , Lecture by , Lecture by , Lecture by |