Datawarehouse and Data Mining Lectures (Hindi)

   Thursday, October 15, 2020   

Starting a new Lecture Notes Series on Datawarehouse and Data Mining Lectures

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Lecture Description of this above Topic:

Lecture 1: Association Rule Mining – Solved Numerical Question on Apriori Algorithm

Lecture 2: Solved Numerical Question 2 on Apriori Algorithm

Lecture 3: Data warehouse Features Lecture

Lecture 4: 3 Layer Architecture of Data Warehouse with Diagram

Lecture 5: KNN Classification– Solved Numerical Question

Lecture 6: KNN Classification– Solved Numerical Question(2)

Lecture 7: FP Growth Algorithm - Solved Numerical Problem 1 on How to Generate FP Tree

Lecture 8: Agglomerative Clustering Algorithm - Plot Dendogram Solved Numerical Question 1

Lecture 9: Agglomerative Clustering Algorithm– Solved Numerical Question 2

Lecture 10: Agglomerative Clustering Algorithm– Solved Numerical Question 3

Lecture 11: FP Growth Algorithm – Solved Numerical Question 2

Lecture 12: K-Means Clustering Algorithm – Solved Numerical Question 1

Lecture 13: K-Means Clustering Algorithm – Solved Numerical Question 2

Lecture 14: Naive Bayes Classification Algorithm – Solved Numerical Question 1

Lecture 15: Naive Bayes Classification Algorithm – Solved Numerical Question 2

Lecture 16: Linear Regression Solved Numerical Example(1)

Lecture 17: Linear Regression Solved Numerical Example(2)

Lecture 18: Decision Tree Classification Algorithm – Solved Numerical Question 1

Lecture 19: Decision Tree Classification Algorithm – Solved Numerical Question 2

Lecture 20: Data Mart - Definition, Types, Advantages, Disadvantages - Data Warehouse Vs Data Mart

Lecture 21: Steps in Implementing Data Mart

Lecture 22: Data Warehouse – Need, Goals, Advantages, Benefits and Problems in Implementation

Lecture 23: Data Warehouse – 2 Tier, 3 Tier and 4 Tier Architecture Models

Lecture 24: Distributed Data Warehouse – Categories, Advantages and Disadvantages

Lecture 25: Data Noise – Techniques to remove Noise(Binning, Regression, Clustering)

Lecture 26: Data Mining Applications, Benefits, Tasks(Predictive and Descriptive)

Lecture 27: Data Mining – Memory Based Reasoning(MBR) Technique for Classification and Prediction

Lecture 28: Data Mining - Decision Tree Technique for Classification and Prediction

Lecture 29: Data Mining – Clustering Technique with Advantages and Euclidian Distance Measure

Lecture 30: ROLAP(Relational OLAP) – Advantages and Disadvantages

Lecture 31: HOLAP(Hybrid OLAP) – Comparison of OLAP Servers

Lecture 32: MOLAP(Multidimensional OLAP) – Advantages and Disadvantages

Lecture 33: Data Warehouse – ETL Process, Comparison between OLTP and Data Warehouse

Lecture 34: Data Warehouse – Dimension Modelling, Fact and Dimension Table with Example

Lecture 35: Data Warehouse Schema – Star, Snowflake and Fact Constellation, Adv. and Disadv.

Lecture 36: Types of Keys in Data Warehouse Schema – Primary Key, Surrogate Key and Foreign Keys

Lecture 37: OLAP Introduction – Features of OLAP , Difference between OLAP and OLTP

Lecture 38: OLAP Operations – Pivoting, Slice and Dice, Rollup and Drilldown

Lecture 39: OLAP Rules by CODD., Difference between OLAP and OLTP

Lecture 40: Part 1 - Important Difference in Data Warehouse and Data Mining

Lecture 41: Part 2 - Important Difference Question in Data Warehouse and Data Minin

Lecture 42: Difference between Data Warehouse and Data Mining

Lecture 43: Data Mining Introduction, Evolution, Need of Data Mining

Lecture 44: Data Mining Process Architecture, Steps in Data Mining/Phases of KDD in Databases

Lecture 45: Data Transformations – Smoothing, Aggregation, Generalization, Normalization(Min-Max, Z-Score)

Lecture 46: Data Preprocessing, Data Cleaning, Ways to handle missing data during cleaning

Lecture 47: Data Integration, Issues in Data Integration - Data Warehouse and Data Mining Lectures

Lecture 48: Data Reduction and Data Cube Aggregation

Lecture 49: Dimensionality Reduction (Lossless, Lossy) and Numerosity Reduction (Parametric, Non Parametric)

Lecture 50: Data Discretization(Splitting, Merging, Supervised, Unsupervised), Concept Hierarchy, Numerical Data

Lecture 51: Data Mining – Neural Networks(ANN) Technique for Classification and Prediction

Lecture 52: Data Mining – Link Analysis and its Applications

Lecture 53: Data Mining – Genetic Algorithms technique with simple example

Lecture 54: Data Mining – Association Rule Mining(ARM)