Fundamentals of Data Science Part I

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.