Learn conditional statements in Python with simple examples. This Lecture 4 guide explains if, elif, else, comparison operators, real-world use cases, MCQs, and practice questions for Data Science beginners. What You Will Learn By the end of this lecture, you…
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Lecture 8 explains Web Mining and Social Network Mining, including content mining, structure mining, usage mining, link analysis, PageRank, HITS, graph theory, community detection, sentiment analysis, examples, diagrams, and Python code. The modern web contains billions of interconnected documents, pages,…
Lecture 7 explains Outlier and Anomaly Detection in Data Mining including statistical methods (Z-score, IQR), density-based LOF, clustering techniques, and machine learning approaches like Isolation Forest. Includes diagrams, examples, Python code, and case studies. Outlier and Anomaly Detection plays a…
Python Data Structures in Lecture 3 of the Python for Data Science course. Simple explanations of lists, tuples, sets, and dictionaries with examples, quiz, and practice questions for beginners. What You Will Learn By the end of this lecture you…
Lecture 6 explains clustering in Data Mining, including K-Means, K-Medoids, hierarchical clustering, and DBSCAN with diagrams, algorithms, examples, Python code, real-world applications, and comparisons for BS CS, BS AI, BS IT & Data Science students. Clustering is one of the…