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Data Cleaning in Python Module

Authors

Dr. Mark Keith, PhD
Brigham Young University

Contributors

Edition 1
(rev. 20)

This module will help students understand the primary data cleaning steps necessary to prepare for predictive modeling. This includes outlier detection and management, handling missing data of all types (random, completely at random, and not at random), and making mathematical transformations. In addition, several basic and common modifications of Pandas DataFrames are covered, including iterating over records, recoding values, and converting dates to integers.

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