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Computer Science I
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Computer Science II
Data Mining
Discrete Structures
Financial Applications and Institutions
Hindu Literature and Ethics
Computer Science I
Data Mining
Course
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Data preparation, Python, similarity, decision trees, regression, and model evaluation.
Lectures
Data mining and the path from data to knowledge
Lecture 1 ·
August 28, 2026
Attribute types, data quality, and distance
Lecture 2 ·
September 1, 2026
Similarity, correlation, and preprocessing
Lecture 3 ·
September 4, 2026
Categorical encoding, scaling, and Python foundations
Lecture 4 ·
September 8, 2026
List comprehensions, files, and regular expressions
Lecture 5 ·
September 11, 2026
NumPy arrays and pandas tables
Lecture 6 ·
September 15, 2026
Data cleaning, feature engineering, and classification
Lecture 7 ·
September 18, 2026
Supervised learning and decision-tree rules
Lecture 8 ·
September 22, 2026
Decision-tree impurity, entropy, and split selection
Lecture 9 ·
September 25, 2026
Simple linear regression and prediction error
Lecture 10 ·
September 29, 2026
Regression loss and classification evaluation
Lecture 11 ·
October 2, 2026