University of Colorado Boulder - Probability Theory: Foundation for Data Science
- Offered byCoursera
Probability Theory: Foundation for Data Science at Coursera Overview
Duration | 48 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Probability Theory: Foundation for Data Science at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
- Approx. 48 hours to complete
- English Subtitles: English
Probability Theory: Foundation for Data Science at Coursera Course details
- 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 https://www.coursera.org/degrees/master-of-science-data-science-boulder.
- Logo adapted from photo by Christopher Burns on Unsplash.
Probability Theory: Foundation for Data Science at Coursera Curriculum
Descriptive Statistics and the Axioms of Probability
Intro to Probability
Axioms of Probability
Counting: Permutations and Combinations
Course Textbook
Course Resources
Intro to Probability
Module 1 Quiz
Conditional Probability
Conditional Probability and Bayes Theorem
Independent Events
Conditional Probability and Bayes Theorem
Module 2 Quiz
Discrete Random Variables
Discrete Random Variables
Bernoulli and Geometric Random Variables
Expectation and Variance
Binomial and Negative Binomial Random Variables
Discrete Random Variables
Module 3 Quiz
Continuous Random Variables
Continuous Random Variables
The Poisson and Exponential Random Variables
The Gaussian (normal) Random Variable Part 1
The Normal Random Variable Part 2
Continuous random variables
Normal Random Variable
Module 4 Quiz
Joint Distributions and Covariance
More on Expectation and Variance
Jointly Distributed Random Variables
Covariance and Correlation
Covariance and Correlation
Module 5 Quiz
Central Limit Theorem
Introduction to the Central Limit Theorem
Central Limit Theorem Examples
Central Limit Theorem
Module 6 Quiz
Probability Theory: Foundation for Data Science at Coursera Admission Process
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