Probability Theory: Foundation for Data Science

Understand the foundations of probability and its relationship to statistics and data science. We\'ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events. We\'ll study discrete and continuous random variables and see how this fits with data collection. We\'ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science. This course can be taken for academic credit as part of CU Boulder\'s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder\'s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at Logo adapted from photo by Christopher Burns on Unsplash.

Created by: University of Colorado Boulder

Language: English

Find Out More
Share
Facebook
Twitter
Pinterest
Reddit
StumbleUpon
LinkedIn
Email

Cal Poly Online Courses

Back to Top

Log In

Contact Us

Upload An Image

Please select an image to upload
Note: must be in .png, .gif or .jpg format
OR
Provide URL where image can be downloaded
Note: must be in .png, .gif or .jpg format

By clicking this button,
you agree to the terms of use

By clicking "Create Alert" I agree to the Uloop Terms of Use.

Image not available.

Add a Photo

Please select a photo to upload
Note: must be in .png, .gif or .jpg format