Statistical Rethinking by Richard McElreath

The unfortunate truth about data is that nothing much can be done with it, until we say what caused it.

This course teaches data analysis, but it focuses on scientific models:
• Conceptual, causal models and precise questions about those models
• Bayesian data analysis to connect scientific models to evidence
• Powerful computational tools for coping with high-dimension, imperfect data of the kind that biologists and social scientists face.

Table of Contents
Week 01 — Science Before Statistics / Garden of Forking Data
Week 02 — Geocentric Models / Categories and Curves
Week 03 — Elemental Confounds / Good and Bad Controls
Week 04 — Overfitting / MCMC
Week 05 — Modeling Events / Counts and Confounds
Week 06 — Ordered Categories / Multilevel Models
Week 07 — Multilevel Adventures / Correlated Features
Week 08 — Social Networks / Gaussian Processes
Week 09 — Measurement / Missing Data
Week 10 — Generalized Linear Madness / Horoscopes

Links:
• GitHub
• Book

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General hashtags: #math #statistics #stat

@data_science_weekly