Probability Theory: Foundation for Data Science
Master probability fundamentals from discrete to continuous distributions with practical applications in data science and statistical inference.
Master probability fundamentals from discrete to continuous distributions with practical applications in data science and statistical inference.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Science Foundations: Statistical Inference Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
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What you'll learn
Learn fundamental concepts of probability theory and its role in data science
Master discrete and continuous random variables and their distributions
Understand and apply conditional probability and Bayes' Theorem
Develop proficiency in calculating expectations and variances
Gain practical experience with joint distributions and covariance
Master the Central Limit Theorem and its applications in data analysis
Skills you'll gain
This course includes:
5.2 Hours PreRecorded video
6 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course establishes the mathematical foundations of probability theory essential for data science applications. The curriculum progresses from basic probability axioms through discrete and continuous random variables, joint distributions, and the Central Limit Theorem. Students gain hands-on experience implementing probability concepts using R programming, preparing them for advanced statistical inference and data analysis.
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Module 1 · 1 Hours to complete
Descriptive Statistics and the Axioms of Probability
Module 2 · 5 Hours to complete
Conditional Probability
Module 3 · 6 Hours to complete
Discrete Random Variables
Module 4 · 7 Hours to complete
Continuous Random Variables
Module 5 · 8 Hours to complete
Joint Distributions and Covariance
Module 6 · 5 Hours to complete
The Central Limit Theorem
Module 7 · 6 Hours to complete
Fee Structure
Instructors
Dedicated Educator and Leader in Applied Mathematics
Dr. Dougherty has held the position of J.R. Woodhull/Logicon Teaching Professor of Applied Mathematics since July 2012. In addition to her teaching responsibilities, she serves as the Associate Chair for Applied Mathematics and is a faculty advisor for applied math majors and minors, as well as statistics minors. Dr. Dougherty also represents the CU campus for the Goldwater Scholarship and advises students participating in the international Mathematics Contest in Modeling.
Associate Professor
Jem Corcoran is an Associate Professor in the Department of Applied Mathematics at the University of Colorado Boulder, where he specializes in probability theory and statistical inference. He holds a Ph.D. in Applied Mathematics from Colorado State University and has been instrumental in developing courses that bridge theoretical concepts with practical applications in data science. His teaching includes "Probability Theory: Foundation for Data Science," "Statistical Inference and Hypothesis Testing in Data Science Applications," and "Statistical Inference for Estimation in Data Science."Dr. Corcoran's research focuses on advanced statistical methods and their applications, particularly in the context of data analysis and modeling. He has contributed significantly to the field through numerous publications and is recognized for his expertise in applied probability and Monte Carlo methods. As a dedicated educator, he aims to enhance student understanding of complex statistical concepts, preparing them for successful careers in data science and related fields. Through his work, Jem Corcoran continues to influence the next generation of mathematicians and data scientists at CU Boulder.
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4.5 course rating
167 ratings
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