Shows the elements of statistical science that are highly relevant
for students who plan to become data scientists less emphasis on
probability theory and methods of probability such as
combinatorics, derivations of probability distributions of
transformations of random variables (except for explanations of t,
chi-squared, and F constructions) Formal statements and proofs of
theorems, and decision theory Introduces some modern topics that do
not normally appear in "math stat" texts but are especially
relevant for data scientists, such as generalized linear models for
non-normal responses (e.g., logistic regression) Bayesian and
regularized fitting of models (e.g., showing an example using the
lasso), classification and clustering, and implementing methods
with modern software (R and Python)
General
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