The 21st century has seen growth in statistical methods. These methods impact various fields. Terms like “Big data,” “Data science,” and “Machine learning” are now common. They are applied to large modern datasets. This book explores data analysis evolution since the 1950s. It starts with traditional inferential theories. These include Bayesian, frequentist, and Fisherian theories. The book then explores influential topics. These include survival analysis, logistic regression, and empirical Bayes. It also covers the jackknife and bootstrap, random forests, and neural networks. Other topics include Markov chain Monte Carlo and inference after model selection. The book blends methodology, algorithms, and statistical inference. It concludes with thoughts on the future of statistics and data science.
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