Covering descriptive and inferential statistics, as well as hypothesis testing techniques and exercises, statistics puts the “scientist” in data scientist.

-Learning probability theory is necessary to think like a data scientist.

-Learning expected values, combinatorics, Bayesian notation, and probability distributions.

learning the linear algebra needed for the true data science expertise of developing powerful machine learning models.