Fundamentals of Data Science Part I
Inference and Experiment
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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 illustr...
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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