Probability for Data Science

A solid foundation in probability is essential for aspiring data scientists. This short course equips you with the crucial mathematical background needed to model and interpret real-world data, from everyday activities to complex computer system performance. Probability underpins many key areas within data science, including machine learning, natural language processing, and computer vision, all of which heavily rely on probabilistic models.

This course provides the necessary groundwork for mastering advanced machine learning and data science concepts. You'll begin with the fundamental concept of probability and gain an overview of discrete random variables. We'll cover how to calculate expectation and variance, and delve into important probability distributions like geometric, binomial, and Poisson distributions. Numerous worked examples will demonstrate practical applications of these concepts.

By the end of this course, you will be able to:

• Describe core probability concepts, including mean, variance, conditional probability, Bayes' rule, and statistical independence.
• Compute the mean and variance of random variables.
• Explain discrete and continuous distributions, such as geometric, binomial, and Poisson.
• Understand how probability distributions can be used to model real-world phenomena.