Fundamentals of Data Science Part II: Statistical Modeling

Fundamentals of Data Science Part II: Statistical Modeling - Jared M. Maruskin

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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