Fundamentals of Data Science Part I: Inference and Experiment
Fundamentals of Data Science Part I: Inference and Experiment
In Part I of this series, we cover basic statistical inference and experimentation, focusing on:
- basic statistics;
- derivation and review of key distributions and their relations;
- hypothesis testing, including an in depth power analysis for the chi-squared statistic;
- experimentation, including A/B tests, stratification, one- and two-factor experiments, and an introduction to bandit algorithms;
- maximum likelihood;
- gradient descent;
- introduction to survival analysis and stochastic processes, including empirical estimation of online survival and event processes.
The theory is illustrated with simulations in Python throughout the text.
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In Part I of this series, we cover basic statistical inference and experimentation, focusing on:
- basic statistics;
- derivation and review of key distributions and their relations;
- hypothesis testing, including an in depth power analysis for the chi-squared statistic;
- experimentation, including A/B tests, stratification, one- and two-factor experiments, and an introduction to bandit algorithms;
- maximum likelihood;
- gradient descent;
- introduction to survival analysis and stochastic processes, including empirical estimation of online survival and event processes.
The theory is illustrated with simulations in Python throughout the text.
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