Data Reduction | Numerosity Reduction & Sampling | Data Warehousing | Lec. 06

In this lecture, we continue Data Reduction techniques in Data Preprocessing, focusing on Numerosity Reduction and Sampling, which help reduce dataset size while preserving important information. These techniques are essential for efficient data analysis in large-scale systems. Data Preprocessing – Data Reduction 📌 Topics Covered 🔹 Numerosity Reduction • What is numerosity reduction? • Parametric methods (e.g., regression) • Non-parametric methods (e.g., histograms, clustering) 🔹 Sampling • What is sampling? • Types of sampling – Simple Random Sampling – Stratified Sampling – Other common techniques (basic idea) • Examples and intuition 🎯 Why this topic is important? These methods reduce data size while maintaining accuracy and efficiency in analysis. 🎯 Important for: GATE DA Data Preprocessing Data Analysis 📌 Data reduction is key for handling large datasets effectively. Reduce smartly → Compute faster → Analyze better 🚀 #DataWarehousing #DataReduction #Sampling #GATEDA