Covariance and Correlation
In this course, you will learn about the basics of probability and how it relates to statistics and data analytics. You can learn how to compute probabilities, the difference between independent and dependent outcomes, and conditional events. Let's look at discrete and continuous random variables, as well as how they relate to data collecting. You finish the course with learning about Gaussian (normal) random variables and the Central Limit Theorem, as well as its usefulness in statistics and data science.
This course is part of CU Boulder's Master of Science in Data Science (MS-DS) program, which is available on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together academics from the Applied Mathematics, Computer Science, Information Science, and other departments at CU Boulder. The MS-DS is excellent for persons with a broad variety of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics, as it is based on performance rather than application.
This course offers:
- Flexible deadlines: Reset deadlines in accordance to your schedule.
- Certificate: Earn a Certificate upon completion
- 100% online
- Beginner Level
- Approx. 1 hours to complete
- Subtitles: English
Course Rating: 4.2/5
Enroll here: https://www.coursera.org/lecture/probability-theory-foundation-for-data-science/covariance-and-correlation-aL9HY
