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Probability Foundations for Data Science
Summary
Upskill in data science by learning key probability concepts: expectation, variance, discrete and continuous distributions, limit theorems, approximations, Bayesian probability, and estimation.
A solid understanding of mathematics, especially probability, is crucial for successful data science endeavors. This course covers the essentials of probability with clear explanations, common equations, simple examples, and real-life applications. First, a review of the basics, like random variables, are covered along with the core distribution types: discrete, continuous, cumulative, and joint. Then, expectation and variance are explored, including conditional expectation, standard deviation, covariance, and correlation. Next, several standard discrete distributions and continuous distributions are detailed, followed by popular limit theorems and approximations. After that, Bayesian probability is explored, including how it differs from frequentist probability. Finally, a few common estimation methods are covered. Join Megan Silvey as she takes you through each section, imparting her expertise to you.