Problem Solving on Data Preprocessing | Data Warehousing | Lec. 11

In this lecture, we solve practice problems on Data Preprocessing, covering all important concepts discussed so far in the Data Warehousing course. This session focuses on strengthening concept clarity, calculations, and exam-oriented problem solving. Problem Solving – Data Preprocessing 📌 Topics Covered 🔹 Data Cleaning • Missing Values • Noise Handling • Outliers 🔹 Data Reduction • PCA • Attribute Subset Selection • Decision Tree-based Reduction • Numerosity Reduction • Sampling • Compression Techniques – RLE – LZW – Huffman Encoding – PCA-based Compression 🔹 Data Transformation • Normalization – Min-Max – Z-Score – Decimal Scaling 🔹 Data Discretization • Binning Techniques • Histogram Analysis • V-Optimal Histogram • MaxDiff Histogram • Step-by-step problem solving approach 🎯 Why this topic is important? Practice helps in connecting all preprocessing concepts and improves problem-solving accuracy for GATE DA. 🎯 Important for: GATE DA Data Preprocessing Data Analysis 📌 This lecture acts as a complete revision + practice session for preprocessing concepts covered so far. Practice preprocessing → Build stronger analytics foundations 🚀 #DataWarehousing #DataPreprocessing #GATEDA