The importance of having a solid grasp over essential concepts of statistics and probability cannot be overstated. Many practitioners in the field actually consider classical (non-neural network) machine learning to be nothing but statistical learning. The subject is vast, and focused planning is critical to cover the most essential concepts:

- Data summaries and descriptive statistics, central tendency, variance, covariance, correlation

- Basic probability: basic idea, expectation, probability calculus, Bayes' theorem, conditional probability

- Probability distribution functions: uniform, normal, binomial, chisquare, Student's t-distribution, central limit theorem

- Sampling, measurement, error, random number generation

- Hypothesis testing, A/B testing, confidence intervals, p-values

- ANOVA, t-test

- Linear regression, regularization

**Where You Might Use It**

In interviews. If you can show you've mastered these concepts, you will impress the other side of the table fast. And you will use them nearly every day as a data scientist.