Fundamentals of Data Science Part II: Statistical Modeling


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Description

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.





Author: Jared M. Maruskin
Publisher: Cayenne Canyon Press
Published: 01/17/2022
Pages: 356
Binding Type: Paperback
Weight: 1.10lbs
Size: 9.21h x 6.14w x 0.74d
ISBN13: 9781941043127
ISBN10: 1941043127
BISAC Categories:
- Mathematics | Probability & Statistics | Bayesian Analysis
- Mathematics | Probability & Statistics | Time Series
- Computers | Data Science | Data Modeling & Design

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