Probability Theory: A Foundation for Data Science

Understand the fundamentals of probability and its relationship to statistics and data science. We’ll learn what it means to calculate probability, independent and dependent outcomes, and conditional events. We’ll explore discrete and continuous random variables and see how this fits into data mining. Finally, we’ll look at Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance to all of statistics and data science. This course can be taken for credit toward CU Boulder’s Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that features faculty from CU Boulder’s Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS program, which is outcomes-based and requires no application, is ideal for individuals with broad educational and/or professional experience in computer science, computer science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder Logo adapted from a photo by Christopher Burns on Unsplash.