Probability Theory: Foundation for Data Science

Learn the basics of probability and how it relates to statistics and data science. We will learn how to calculate a probability and what independent and dependent outcomes are. This course will cover both continuous and discrete random variables, and how they relate to data collection. The course will end with Gaussian (normal), random variables and the Central Limit Theorem. This will help us understand its fundamental importance in statistics and data science.

This course is available for academic credit through CU Boulder's Master of Science degree in Data Science (MSDS), which can be found on Coursera. The MS-DS degree is an interdisciplinary program that brings together faculty members from CU Boulder's departments in Applied Mathematics, Computer Science and Information Science. The MS-DS program is open to individuals who have a wide range of education and/or experience in information science, computer science, statistics, and mathematics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo taken from Christopher Burns' photo on Unsplash.