Fundamentals of Data Science Part II: Statistical Modeling
Fundamentals of Data Science Part II: Statistical Modeling
In Part II of this series, we cover the elements of statistical modeling, focusing on:
- validation methodology
- principles of object-oriented design
- linear and logistic regression
- generalized linear models
- causality
- time series analysis
- Bayesian statistics, including simulations in pymc3
- Modeling customer lifetime values, including a detailed study of the beta-Bernoulli/beta-binomial model, a discretized version of the classic Pareto/NBD
- an introduction to credibility theory
The theory is illustrated with simulations in Python throughout the text.
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In Part II of this series, we cover the elements of statistical modeling, focusing on:
- validation methodology
- principles of object-oriented design
- linear and logistic regression
- generalized linear models
- causality
- time series analysis
- Bayesian statistics, including simulations in pymc3
- Modeling customer lifetime values, including a detailed study of the beta-Bernoulli/beta-binomial model, a discretized version of the classic Pareto/NBD
- an introduction to credibility theory
The theory is illustrated with simulations in Python throughout the text.
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