Probability Theory Basics for Data Science
Probability theory provides the mathematical foundation for modeling uncertainty. By understanding random variables and probability distributions, data scientists can quantify risks and make informed decisions.
Random Variables and Distributions
A random variable assigns numerical values to outcomes in a sample space. Key distributions such as the binomial, normal, and Poisson describe how probabilities are spread across possible outcomes. Knowing these distributions helps in selecting appropriate models and estimating parameters.
Expectation and Variance
Two fundamental measures of a random variable are its expected value and variance. The expected value represents the long-run average, while the variance measures how spread out the outcomes are. These metrics are critical for evaluating models and comparing predictions.
Mastering probability theory enables data scientists to better interpret model outputs and reason about uncertainty in real-world applications.
