Correlation, Association and Causation

77 min
Block 4 — Understanding the data before modeling
Objective
define covariance and Pearson's coefficient rigorously, state exactly what they measure and what they ignore, choose a coefficient that matches the shape of the relationship and the type of the variables, exploit a correlation matrix for preliminary triage and redundancy detection, and be able to explain why an observed association licenses no causal conclusion whatsoever.
Estimated duration
55 minutes
Prerequisites
chapters 005 (dataset structure), 006 (features and target), 013 (exploratory data analysis), 014 (descriptive statistics) and 015 (data visualization)
Associated quizzes
016.1-quiz-covariance-and-pearson.md to 016.7-quiz-correlation-and-causation.md

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This lesson is part of the “Exploratory Data Analysis (EDA)” module

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